CN111967673A - Multi-target vehicle path optimization method and device based on membrane system - Google Patents

Abstract

The invention discloses a multi-target vehicle path optimization method and a device based on a membrane system, compared with the prior mode of converting a plurality of targets into a single-target optimization problem through mathematical programming, the invention determines the cell transport rules of structural cells, an alphabet and the structural cells according to the multi-target vehicle path optimization task, obtains an output cell identifier and an initial cell state rule finite set, generates a tissue membrane system according to the alphabet, the structural cells, the cell transport rules, the initial cell state rule finite set and the output cell identifier, optimizes the parameters to be optimized of the multi-target vehicle path optimization task according to the tissue membrane system to obtain a target vehicle path, overcomes the defect that the vehicle path problem with a time window cannot be calculated in the prior art, thereby optimizing the multi-target vehicle path and reducing the calculation complexity, the solution set approaches to the front surface in a uniformly distributed mode, and meanwhile, the solution set has strong ductility.

Description

Multi-target vehicle path optimization method and device based on membrane system

Technical Field

The invention relates to the technical field of path optimization, in particular to a multi-target vehicle path optimization method and device based on a membrane system.

Background

As market competition increases, businesses engaged in delivery services increasingly recognize the importance of routing vehicles in a rational manner, which can both increase customer service levels and help reduce delivery costs. The vehicle path problem is a very complex problem, relates to a plurality of expansion problem types, spans multidisciplinary knowledge such as combinatorial optimization, operational research and the like, and has certain difficulty in modeling. Meanwhile, related researches have proved that the problem of Vehicle path with Time window (VRPTW) is NP-hard, and a large number of experiments show that the accurate algorithm can only process small-scale VRPTW, and for larger-scale VRPTW, the calculation Time of the algorithm increases exponentially along with the increase of the problem scale.

At present, in an actual vehicle path problem, multiple targets, such as the number of vehicles, a driving path, a path balance, carbon dioxide emission and other targets, need to be considered and optimized, and in a multi-target optimization problem, a method for converting the multiple targets into a single-target optimization problem through a mathematical programming mode is often inefficient and is not suitable for a nonlinear discrete problem.

The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.

Disclosure of Invention

The invention mainly aims to provide a multi-objective vehicle path optimization method and device based on a membrane system, and aims to solve the technical problem of how to optimize a multi-objective vehicle path.

To achieve the above object, the present invention provides a multi-objective vehicle path optimization method based on a membrane system, comprising the steps of:

when a multi-target vehicle path optimization instruction is received, acquiring a multi-target vehicle path optimization task;

determining a structural cell, an alphabet and a cell transport rule of the structural cell according to the multi-objective vehicle path optimization task;

obtaining an output cell identifier and a finite set of initial cell state rules, and generating a tissue membrane system according to the alphabet, the structural cells, the cell transport rules, the finite set of initial cell state rules, and the output cell identifier;

optimizing parameters to be optimized of the multi-target vehicle path optimization task according to the organizational film system to obtain target parameters;

and optimizing the multi-target vehicle path according to the target parameters to obtain the target vehicle path. .

Preferably, the step of determining the structural cells, the alphabet and the cell transport rules of the structural cells according to the multi-objective vehicle path optimization task specifically includes:

determining parameters to be optimized, task forming parameters and task decision vectors according to the multi-target vehicle path optimization task;

establishing a mathematical optimization model according to the parameters to be optimized, the task composition parameters and the task decision vector, and determining a model constraint condition according to the mathematical optimization model;

and determining the constructed cells according to the mathematical optimization model and the model constraint conditions, and determining the alphabet and the cell transport rule of the constructed cells according to the parameters to be optimized.

Preferably, the step of determining the structural cell according to the mathematical optimization model and the model constraint condition, and determining the alphabet and the cell transport rule of the structural cell according to the parameter to be optimized specifically includes:

acquiring a current character string of the structural cell, and generating a character string ordering rule of the structural cell according to the current character string;

determining an initial pattern generation rule, a character string length self-adaption rule, a stopping rule and a cell optimization rule according to the mathematical optimization model and the model constraint condition;

generating a structural cell according to the character string ordering rule, the initial pattern generating rule, the character string length self-adapting rule, the stopping rule and the cell optimizing rule;

determining the alphabet and the cell transport rule of the constructed cells according to the parameter to be optimized.

Preferably, the step of optimizing the parameter to be optimized according to the tissue membrane system to obtain a target parameter specifically includes:

controlling a preset script to run according to the tissue membrane system to obtain the membrane system evolution pattern time and a current solution set;

judging whether the membrane system evolution pattern moment is equal to a preset moment or not;

and when the membrane system evolution pattern moment is equal to the preset moment, determining target parameters according to the current solution set.

Preferably, after the step of determining whether the membrane system evolution pattern time is equal to a preset time, the method for optimizing a multi-objective vehicle path based on a membrane system further comprises:

when the membrane system evolution pattern moment is not equal to the preset moment, starting a character string length self-adaptive rule to obtain an initial character string;

optimizing the initial character string according to the cell optimization rule to obtain an optimized character string;

and adjusting the membrane system evolution pattern moment, and returning to the step of judging whether the membrane system evolution pattern moment is equal to a preset moment or not.

In addition, to achieve the above object, the present invention also provides a multi-objective vehicle path optimizing apparatus based on a membrane system, including: the system comprises an acquisition module, a determination module, a tissue membrane system generation module and an optimization module;

the acquisition module is used for acquiring a multi-objective vehicle path optimization task when receiving a multi-objective vehicle path optimization instruction;

the determining module is used for determining a structural cell, an alphabet and a cell transport rule of the structural cell according to the multi-objective vehicle path optimization task;

the tissue membrane system generating module is used for acquiring an output cell identifier and a limited set of initial cell state rules and generating a tissue membrane system according to the alphabet, the structural cells, the cell transport rules, the limited set of initial cell state rules and the output cell identifier;

the optimization module is used for optimizing parameters to be optimized of the multi-target vehicle path optimization task according to the organizational film system to obtain target parameters;

the optimization module is further used for optimizing the multi-target vehicle path according to the target parameters to obtain the target vehicle path.

Preferably, the determining module is further configured to determine parameters to be optimized, task configuration parameters and task decision vectors according to the multi-objective vehicle path optimization task;

the determining module is further configured to establish a mathematical optimization model according to the parameter to be optimized, the task configuration parameter and the task decision vector, and determine a model constraint condition according to the mathematical optimization model;

the determining module is further used for determining the constructed cells according to the mathematical optimization model and the model constraint conditions, and determining the alphabet and the cell transport rule of the constructed cells according to the parameters to be optimized.

Preferably, the determining module is further configured to obtain a current character string of the structural cell, and generate a character string ordering rule of the structural cell according to the current character string;

the determining module is further used for determining an initial pattern generating rule, a character string length self-adaptive rule, a stopping rule and a cell optimizing rule according to the mathematical optimization model and the model constraint condition;

the determining module is further configured to generate a structural cell according to the initial pattern generation rule, the string length adaptation rule, the stopping rule, and the cell optimization rule;

the determining module is further used for determining an alphabet and a cell transport rule of the constructed cells according to the parameter to be optimized.

Preferably, the optimization module is further configured to control a preset script to run according to the tissue membrane system, so as to obtain a membrane system evolution pattern time and a current solution set;

the optimization module is also used for judging whether the membrane system evolution pattern moment is equal to a preset moment or not;

and the optimization module is further used for determining target parameters according to the current solution set when the membrane system evolution pattern time is equal to the preset time.

Preferably, the optimizing module is further configured to start a character string length adaptive rule to obtain an initial character string when the membrane system evolution pattern time is not equal to the preset time;

the optimization module is further used for optimizing the initial character string according to the cell optimization rule to obtain an optimized character string;

and the optimizing module is also used for adjusting the membrane system evolution pattern time and returning to the step of judging whether the membrane system evolution pattern time is equal to a preset time.

In the invention, when a multi-target vehicle path optimization instruction is received, a multi-target vehicle path optimization task is obtained; determining a structural cell, an alphabet and a cell transport rule of the structural cell according to the multi-objective vehicle path optimization task; obtaining an output cell identifier and a finite set of initial cell state rules, and generating a tissue membrane system according to the alphabet, the structural cells, the cell transport rules, the finite set of initial cell state rules, and the output cell identifier; optimizing parameters to be optimized of the multi-target vehicle path optimization task according to the organizational film system to obtain target parameters; performing multi-target vehicle path optimization according to the target parameters to obtain a target vehicle path; compared with the existing mode of converting a plurality of targets into a single-target optimization problem through mathematical programming, the method and the device construct the organizational film system according to the multi-target Vehicle path optimization task, optimize the parameters to be optimized of the multi-target Vehicle path optimization task according to the organizational film system, obtain the target Vehicle path, and overcome the defect that the problem of Vehicle paths (VRPTW) with Time Windows cannot be calculated in the prior art, so that the multi-target Vehicle path can be optimized, the calculation complexity is reduced, the solution set approaches the front edge surface in a uniformly distributed manner, and the solution set has high ductility.

Drawings

FIG. 1 is a schematic flow diagram of a first embodiment of a method for multi-objective vehicle path optimization based on a membrane system of the present invention;

FIG. 2 is a block diagram of an organized membrane system in an embodiment of the membrane system based multi-objective vehicle path optimization method of the present invention;

FIG. 3 is a schematic flow diagram of a second embodiment of a method for multi-objective vehicle path optimization based on a membrane system of the present invention;

fig. 4 is a block diagram showing the structure of a first embodiment of the multi-objective vehicle path optimizing apparatus based on a membrane system according to the present invention.

The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

Referring to fig. 1, fig. 1 is a schematic flow diagram of a first embodiment of a multi-objective vehicle path optimization method based on a membrane system according to the present invention, and proposes the first embodiment of the multi-objective vehicle path optimization method based on a membrane system according to the present invention.

Step S10: and when receiving the multi-target vehicle path optimization instruction, acquiring a multi-target vehicle path optimization task.

It should be noted that an implementation subject of this embodiment is the multi-objective vehicle path optimization device based on the membrane system, where the multi-objective vehicle path optimization device based on the membrane system may be an electronic device such as a mobile phone, a computer, and a server, or may also be another device that can achieve the same or similar functions.

It should be noted that the multi-objective vehicle path optimization task may be a VRPTW problem to be optimized that is input by a user through a membrane system-based multi-objective vehicle path optimization device.

Step S20: determining a structural cell, an alphabet, and a cell transport rule for the structural cell according to the multi-objective vehicle path optimization task.

It is understood that determining the structural cells, the alphabet, and the cell transport rules for the structural cells according to the multi-objective vehicle path optimization task may be determining parameters to be optimized, task composition parameters, and task decision vectors according to the multi-objective vehicle path optimization task, establishing a mathematical optimization model according to the parameters to be optimized, the task composition parameters, and the task decision vectors, determining model constraints according to the mathematical optimization model, determining the structural cells according to the mathematical optimization model and the model constraints, and determining the alphabet and the cell transport rules for the structural cells according to the parameters to be optimized.

It should be noted that, the embodiment solves the multi-objective vehicle path optimization task, and therefore, the parameters to be optimized may be the number of vehicles, the path length, the path balance, and the like, which is not limited in this embodiment; the task configuration parameters may be as shown in the task configuration parameter table in table 1, but the embodiment is not limited thereto.

TABLE 1 task composition parameter Table

It should be noted that the task decision vector of the multi-objective vehicle path optimization task is shown as the following formula

It should be understood that the mathematical optimization model is shown as follows:

min f₁＝∑_k∈N∑_j∈Nx_0jk

min f₂＝∑_k∈N∑_i∈N∑_j∈Nd_ijx_ijk

the model constraints are shown as follows:

step S30: obtaining an output cell signature and a finite set of initial cell state rules, and generating a tissue membrane system based on the alphabet, the structural cells, the cell transport rules, the finite set of initial cell state rules, and the output cell signature.

It will be appreciated that obtaining the output cell identity and the finite set of initial cell state rules and generating the tissue membrane system from the alphabet, the constructed cells, the cell transport rules, the finite set of initial cell state rules and the output cell identity may be by constructing a tissue membrane system as follows:

Π＝{O,σ₁,σ₁,L,σ₇,syn,R₀,i_out}

wherein O is { x ═ x₁,x₂,L,x_npIs the alphabet, where the element x_i(i 1, 2., NP) is a set of permutations of 1,2, L, n customer numbers, corresponding to a set of encodings of the three-target VRPTW feasible solution; syn { (1,2), (1,3), (1,4), (1,5), (1,6), (1,7) } indicates that cell 1 communicates with cell 2 to cell 7, respectively, using the transport rule; i.e. i_out1 indicates that the exported cell is cell 1; r₀＝{(N_1,0,N_2,0,L,N_7,0)→(u_1,0,u_2,0,L,u_7,0),(N_1,t,N_2,t,L,N_7,t)→(N_1,t+1,N_2,t+1,L,N_7,t+1) Denotes a finite set of rules that adaptively initialize cell states. Sigma_i＝(Q_i,s_i,0,ω_i,0,R_i) Represents a cell i, 1. ltoreq. i.ltoreq.7, wherein,

s_i,tis the state of cell i in the pattern t, t_maxRepresenting the maximum constellation time of the evolution of the membrane system; i 1,2, L,7, t 1,2, L, t_max；s_i,0∈Q_iIs an initial state; omega_i,0＝{u_i,0}，

Is an initial character string of a cell i, corresponds to a population of a three-target VRPTW which is ordered according to a frame diagram of a tissue membrane system of figure 2;

is a finite set of rules for cell i, s_i,t,s_i,t+1∈Q_i；ω_i,tBy character string u_i,tAnd a plurality of elements of the alphabet O; u. of_i,t,u_i,t+1∈O^*；

Represents a set T generated in a cell i and sent out to the environmentⁱ；

Denotes a set P produced in cell i and transferred to other cellsⁱ。

Step S40: and optimizing parameters to be optimized of the multi-target vehicle path optimization task according to the organizational film system to obtain target parameters.

It should be understood that, the parameters to be optimized of the multi-target vehicle path optimization task are optimized according to the organizational film system, and the target parameters can be obtained by controlling a preset script to operate according to the organizational film system, obtaining a film system evolution situation time and a current solution set, judging whether the film system evolution situation time is equal to a preset time, and determining the target parameters according to the current solution set when the film system evolution situation time is equal to the preset time; and when the membrane system evolution pattern time is not equal to the preset time, starting a character string length self-adaption rule to obtain an initial character string, optimizing the initial character string according to the cell optimization rule to obtain an optimized character string, adjusting the membrane system evolution pattern time, and returning to the step of judging whether the membrane system evolution pattern time is equal to the preset time.

In specific implementation, for example, a multi-target vehicle path optimization script operation based on a membrane system is controlled, and a membrane system evolution pattern time t and a current solution set are obtained

Judging t as t_maxIf it is true, if t is t_maxThen the optimal solution set is output

Will be provided with

As a target parameter; when the membrane system evolution pattern time is not equal to the preset time, the pattern t is evolved from a pattern t +1, and the specific process is as follows: firstly, starting a character string length adaptive rule, initializing a character string to obtain an initial character string, then obtaining an optimized character string according to a cell optimization rule of a cell i (i ═ 1,2, L,7), adjusting the membrane system evolution pattern time, and returning to the step of judging whether the membrane system evolution pattern time is equal to a preset time or not, wherein the step of judging whether the membrane system evolution pattern time is equal to the preset time or not may be to set t ═ t +1, and returning to the step of judging whether t ═ t-_maxThe step (2).

Step S50: and optimizing the multi-target vehicle path according to the target parameters to obtain the target vehicle path.

It can be understood that, the multi-objective vehicle path optimization is performed according to the target parameters, and obtaining the target vehicle path may be directly generating vehicle paths corresponding to the vehicles according to the target parameters to obtain the target vehicle path.

In the first embodiment, when a multi-objective vehicle path optimization instruction is received, a multi-objective vehicle path optimization task is acquired; determining a structural cell, an alphabet and a cell transport rule of the structural cell according to the multi-objective vehicle path optimization task; obtaining an output cell identifier and a finite set of initial cell state rules, and generating a tissue membrane system according to the alphabet, the structural cells, the cell transport rules, the finite set of initial cell state rules, and the output cell identifier; optimizing parameters to be optimized of the multi-target vehicle path optimization task according to the organizational film system to obtain target parameters; performing multi-target vehicle path optimization according to the target parameters to obtain a target vehicle path; compared with the existing mode of converting a plurality of targets into a single-target optimization problem through mathematical programming, the method for optimizing the multi-target Vehicle path based on the multi-target Vehicle path optimization task in the embodiment constructs the organizational film system according to the multi-target Vehicle path optimization task, optimizes the parameters to be optimized of the multi-target Vehicle path optimization task according to the organizational film system, and obtains the target Vehicle path, so that the defect that the problem of Vehicle paths with Time Windows (VRPTW) cannot be calculated in the prior art is overcome, the multi-target Vehicle path can be optimized, the calculation complexity is reduced, the solution set approaches the front edge surface in a uniformly distributed manner, and meanwhile, the solution set has high ductility.

Referring to fig. 3, fig. 3 is a schematic flow chart of a second embodiment of the multi-objective vehicle path optimization method based on a membrane system according to the present invention, and the second embodiment of the multi-objective vehicle path optimization method based on a membrane system according to the present invention is proposed based on the first embodiment shown in fig. 1.

In the second embodiment, the step S20 includes:

step S201: and determining parameters to be optimized, task forming parameters and task decision vectors according to the multi-target vehicle path optimization task.

It should be noted that, the embodiment solves the multi-objective vehicle path optimization task, and therefore, the parameters to be optimized may be the number of vehicles, the path length, the path balance, and the like, which is not limited in this embodiment; the task configuration parameters may be as shown in the task configuration parameter table in table 1, but the embodiment is not limited thereto.

TABLE 1 task composition parameter Table

It should be noted that the task decision vector of the multi-objective vehicle path optimization task is shown as the following formula

Step S202: and establishing a mathematical optimization model according to the parameters to be optimized, the task composition parameters and the task decision vector, and determining a model constraint condition according to the mathematical optimization model.

It should be understood that the mathematical optimization model is shown as follows:

min f₁＝∑_k∈N∑_j∈Nx_0jk

min f₂＝∑_k∈N∑_i∈N∑_j∈Nd_ijx_ijk

the model constraints are shown as follows:

step S203: and determining the constructed cells according to the mathematical optimization model and the model constraint conditions, and determining the alphabet and the cell transport rule of the constructed cells according to the parameters to be optimized.

Further, in order to improve the calculation accuracy, the step S203 includes:

acquiring a current character string of the structural cell, and generating a character string ordering rule of the structural cell according to the current character string;

determining an initial pattern generation rule, a character string length self-adaption rule, a stopping rule and a cell optimization rule according to the mathematical optimization model and the model constraint condition;

generating a structural cell according to the character string ordering rule, the initial pattern generating rule, the character string length self-adapting rule, the stopping rule and the cell optimizing rule;

determining the alphabet and the cell transport rule of the constructed cells according to the parameter to be optimized.

It will be appreciated that the current string of the structuring cell obtained and the string ordering rules for generating the structuring cell from the current string may be a string in cell 1 as shown in the block diagram of the tissue membrane system of fig. 2

The ordering rule of (1): p is more than or equal to 1, and q is more than or equal to N_1,tIs provided with

Wherein Nd represents the non-dominant front surface of the same layer, and Cd represents the crowding degree of a certain element;

for a set of permutations of targets 1,2,3, a string of cells (i ═ 2.., 7)

Sort rules (a, b, c): p is more than or equal to 1, and q is more than or equal to N_i,tIs provided with

The cell 2 to the cell 7 adopt the ordering rules of (1,2,3), (1,3,2), (2,1,3), (2,3,1), (3,1,2) and (3,2,1), respectively.

It is understood that the determination of the initial pattern generation rule, the string length adaptation rule, the stopping rule, and the cell optimization rule based on the mathematical optimization model and the model constraints may be as follows:

first, an initial pattern generation rule (N)₁,N₂,L,N₇)→(u_1,0,u_2,0,L,u_7,0)

The initial length vector K for each cell (N)_1,0,N_2,0,L,N_7,0) The initial character string of the cell is generated by, for a cell i (i ═ 1,2, L,7), first, randomly generating an array of 1 to n, forming the elements in the alphabet table 2

Represents the jj (1. ltoreq. j. ltoreq.N) th cell in the i-th cell_i，0) An element, then, according to the rule

The character strings are sorted.

Adaptive rule of character string length (N)_1,t,N_2,t,L,N_7,t)→(N_1,t+1,N_2,t+1,L,N_7,t+1)

At the beginning of the pattern evolution, the string length needs to be adaptively adjusted. For cell 1, at time t +1, the string length

For cells 2 to 7, when t.ltoreq.t_max/6]The character string N_i,t+1＝N_i,tWhen time [ t ]_max/6]<t≤[t_max/2]Then N is_i,t+1＝max{[3/2N_i,t-3t/SN_i,t]10, when the time t_max/2]<t≤t_maxThen N is_i,t+10. Wherein the symbol [ ·]Indicating taking an integer.

③ rules of stopping

In a state s_i,tUnder, | u_i,tCaption string u of 0 |)_i,tWith λ as empty string, i.e. set ω_i,tOnly the elements in O transported from other cells evolve into state s_i,t+1When u is turned on_i,t+1＝λ，ω_i,tThe elements in (1) constitute a set TⁱAnd sent out to the environment.

Optimization rules of cell 1

State s_1,tLower, multiple set omega_1,tContaining character strings

And elements of the alphabet O transported from other cells, which elements form the set P ═ { y ═ y_i|y_iE.g. O ^ i ^ 1,2, L, m }, i.e. omega_1,t＝{u_1,tP }. The optimization rule for cell 1 is: firstly, to the character string u_1,tPerforming a merge-truncate operation with the set P to optimize the string u_1,t(ii) a Then carrying out the elite reservation genetic operation of three-target optimization to obtain a new character string u_1,t+1。

Optimization rule of cell i (i ═ 2,3, L,7)

State s_i,t(i 2, 3.., 7), set ω_i,t＝{u_i,tP }, wherein the character string

Set P ═ y_i|y_iE.o ^ i ═ 1,2, L, m }, and the optimization rules of cell 2, cell 3, …, and cell 7 are: firstly, to the character string u_i,tAnd set P goesOptimizing string u by line merge-truncate operations_i,t(ii) a Then carrying out the swarm operation with the priority and the three-target optimization to obtain a new character string u_i,t+1(ii) a In the rule design, the concept of a document string is proposed for storing the excellent elements that cells produce during evolution, here the initial document string d_i＝u_i,0。

It is to be understood that generating a structuring cell according to the string ordering rule, the initial pattern generation rule, the string length adaptation rule, the stopping rule and the cell optimization rule may be generating a structuring cell as shown in the block diagram of the tissue membrane system of fig. 2.

It should be understood that the alphabet determined according to the parameter to be optimized may be O ═ x₁,x₂,L,x_npIs the alphabet, where the element x_i(i 1, 2., NP) is a set of permutations of 1,2, L, n customer numbers, corresponding to a set of encodings of the three-target VRPTW feasible solution.

It is understood that the cell transport rule for the constructed cells determined according to the parameter to be optimized may be syn { (1,2), (1,3), (1,4), (1,5), (1,6), (1,7) } indicating that the cell transport rule is adopted between cell 1 and cell 2 to cell 7, respectively.

In a second embodiment, a parameter to be optimized, a task composition parameter and a task decision vector are determined according to the multi-objective vehicle path optimization task, a mathematical optimization model is established according to the parameter to be optimized, the task composition parameter and the task decision vector, a model constraint condition is determined according to the mathematical optimization model, a structural cell is determined according to the mathematical optimization model and the model constraint condition, an alphabet and a cell transport rule of the structural cell are determined according to the parameter to be optimized, and therefore the alphabet and the cell transport rule of the structural cell can be determined accurately and rapidly.

In the second embodiment, the step S40 includes:

step S401: and controlling the preset script to run according to the tissue membrane system to obtain the membrane system evolution pattern time and the current solution set.

It should be noted that the preset script may be a processing script set by a user according to actual requirements, and in this embodiment, a multi-target vehicle path optimization script based on a membrane system in the MATLAB program is taken as an example for description.

It should be understood that the obtaining of the membrane system evolution pattern time and the current solution set according to the said operational preset script of the said tissue membrane system control may be the obtaining of the membrane system evolution pattern time t, the current solution set

Step S402: and judging whether the membrane system evolution pattern moment is equal to a preset moment or not.

It is understood that the determination of whether the membrane system evolution pattern time is equal to the predetermined time may be a determination of t-t_maxAnd whether the current time is up or not is judged, wherein the preset time is preset according to the actual requirement of the user.

Further, after the step S402, the method further includes:

when the membrane system evolution pattern moment is not equal to the preset moment, starting a character string length self-adaptive rule to obtain an initial character string;

optimizing the initial character string according to the cell optimization rule to obtain an optimized character string;

and adjusting the membrane system evolution pattern moment, and returning to the step of judging whether the membrane system evolution pattern moment is equal to a preset moment or not.

It should be understood that when the membrane system evolution pattern time is not equal to the preset time, the pattern t is evolved by the pattern t +1, and the specific process is as follows: first, a string length adaptive rule is started, a string is initialized, an initial string is obtained, and then an optimized string is obtained according to a cell optimization rule of a cell i (i ═ 1,2, L, 7).

It is understood that the step of adjusting the membrane system evolution pattern time and returning to the step of determining whether the membrane system evolution pattern time is equal to the preset time may be setting t to t +1 and returning toJudging t as t_maxThe step (2).

Step S403: and when the membrane system evolution pattern moment is equal to the preset moment, determining target parameters according to the current solution set.

It should be understood that if t ═ t_maxThen the optimal solution set is output

Will be provided with

As the target parameter.

In a second embodiment, a membrane system evolution pattern time and a current solution set are obtained according to the operation of the tissue membrane system control preset script, whether the membrane system evolution pattern time is equal to a preset time or not is judged, and when the membrane system evolution pattern time is equal to the preset time, a target parameter is determined according to the current solution set, so that the evolution and the stop of cells can be controlled by controlling the length of a character string, and the system can operate in a self-adaptive mode.

In addition, referring to fig. 4, an embodiment of the present invention further provides a multi-objective vehicle path optimizing apparatus based on a membrane system, including: an acquisition module 10, a determination module 20, a tissue membrane system generation module 30 and an optimization module 40;

the obtaining module 10 is configured to obtain a multi-objective vehicle path optimization task when receiving a multi-objective vehicle path optimization instruction.

It should be noted that the multi-objective vehicle path optimization task may be a VRPTW problem to be optimized that is input by a user through a membrane system-based multi-objective vehicle path optimization device.

The determination module 20 is configured to determine a structural cell, an alphabet, and a cell transport rule of the structural cell according to the multi-objective vehicle path optimization task.

It is understood that determining the structural cells, the alphabet, and the cell transport rules for the structural cells according to the multi-objective vehicle path optimization task may be determining parameters to be optimized, task composition parameters, and task decision vectors according to the multi-objective vehicle path optimization task, establishing a mathematical optimization model according to the parameters to be optimized, the task composition parameters, and the task decision vectors, determining model constraints according to the mathematical optimization model, determining the structural cells according to the mathematical optimization model and the model constraints, and determining the alphabet and the cell transport rules for the structural cells according to the parameters to be optimized.

It should be noted that, the embodiment solves the multi-objective vehicle path optimization task, and therefore, the parameters to be optimized may be the number of vehicles, the path length, the path balance, and the like, which is not limited in this embodiment; the task configuration parameters may be as shown in the task configuration parameter table in table 1, but the embodiment is not limited thereto.

TABLE 1 task composition parameter Table

It should be noted that the task decision vector of the multi-objective vehicle path optimization task is shown as the following formula

It should be understood that the mathematical optimization model is shown as follows:

min f₁＝∑_k∈N∑_j∈Nx_0jk

min f₂＝∑_k∈N∑_i∈N∑_j∈Nd_ijx_ijk

the model constraints are shown as follows:

the tissue membrane system generating module 30 is configured to obtain an output cell identifier and a limited set of initial cell state rules, and generate a tissue membrane system according to the alphabet, the structural cells, the cell transport rules, the limited set of initial cell state rules, and the output cell identifier.

It will be appreciated that obtaining the output cell identity and the finite set of initial cell state rules and generating the tissue membrane system from the alphabet, the constructed cells, the cell transport rules, the finite set of initial cell state rules and the output cell identity may be by constructing a tissue membrane system as follows:

Π＝{O,σ₁,σ₁,L,σ₇,syn,R₀,i_out}

wherein O is { x ═ x₁,x₂,L,x_npIs the alphabet, where the element x_i(i 1, 2., NP) is a set of permutations of 1,2, L, n customer numbers, corresponding to a set of encodings of the three-target VRPTW feasible solution; syn { (1,2), (1,3), (1,4), (1,5), (1,6), (1,7) } indicates that cell 1 communicates with cell 2 to cell 7, respectively, using the transport rule; i.e. i_out1 indicates that the exported cell is cell 1; r₀＝{(N_1,0,N_2,0,L,N_7,0)→(u_1,0,u_2,0,L,u_7,0),(N_1,t,N_2,t,L,N_7,t)→(N_1,t+1,N_2,t+1,L,N_7,t+1) Denotes a finite set of rules that adaptively initialize cell states. Sigma_i＝(Q_i,s_i,0,ω_i,0,R_i) Represents a cell i, 1. ltoreq. i.ltoreq.7, wherein,

s_i,tis the state of cell i in the pattern t, t_maxRepresenting the maximum constellation time of the evolution of the membrane system; i 1,2, L,7, t 1,2, L, t_max；s_i,0∈Q_iIs an initial state; omega_i,0＝{u_i,0}，

Is an initial character string of a cell i, corresponds to a population of a three-target VRPTW which is ordered according to a frame diagram of a tissue membrane system of figure 2;

is a finite set of rules for cell i, s_i,t,s_i,t+1∈Q_i；ω_i,tBy character string u_i,tAnd a plurality of elements of the alphabet O; u. of_i,t,u_i,t+1∈O^*；

Represents a set T generated in a cell i and sent out to the environmentⁱ；

Denotes a set P produced in cell i and transferred to other cellsⁱ。

The optimization module 40 is configured to optimize parameters to be optimized of the multi-objective vehicle path optimization task according to the organizational film system, so as to obtain objective parameters.

It should be understood that, the parameters to be optimized of the multi-target vehicle path optimization task are optimized according to the organizational film system, and the target parameters can be obtained by controlling a preset script to operate according to the organizational film system, obtaining a film system evolution situation time and a current solution set, judging whether the film system evolution situation time is equal to a preset time, and determining the target parameters according to the current solution set when the film system evolution situation time is equal to the preset time; and when the membrane system evolution pattern time is not equal to the preset time, starting a character string length self-adaption rule to obtain an initial character string, optimizing the initial character string according to the cell optimization rule to obtain an optimized character string, adjusting the membrane system evolution pattern time, and returning to the step of judging whether the membrane system evolution pattern time is equal to the preset time.

In specific implementation, for example, a multi-target vehicle path optimization script operation based on a membrane system is controlled, and a membrane system evolution pattern time t and a current solution set are obtained

Judging t as t_maxIf it is true, if t is t_maxThen the optimal solution set is output

Will be provided with

As a target parameter; when the membrane system evolution pattern time is not equal to the preset time, the pattern t is evolved from a pattern t +1, and the specific process is as follows: firstly, starting a character string length adaptive rule, initializing a character string to obtain an initial character string, then obtaining an optimized character string according to a cell optimization rule of a cell i (i ═ 1,2, L,7), adjusting the membrane system evolution pattern time, and returning to the step of judging whether the membrane system evolution pattern time is equal to a preset time or not, wherein the step of judging whether the membrane system evolution pattern time is equal to the preset time or not may be to set t ═ t +1, and returning to the step of judging whether t ═ t-_maxThe step (2).

The optimization module 40 is further configured to perform multi-objective vehicle path optimization according to the target parameters to obtain a target vehicle path.

It can be understood that, the multi-objective vehicle path optimization is performed according to the target parameters, and obtaining the target vehicle path may be directly generating vehicle paths corresponding to the vehicles according to the target parameters to obtain the target vehicle path.

In the embodiment, when a multi-objective vehicle path optimization instruction is received, a multi-objective vehicle path optimization task is obtained; determining a structural cell, an alphabet and a cell transport rule of the structural cell according to the multi-objective vehicle path optimization task; obtaining an output cell identifier and a finite set of initial cell state rules, and generating a tissue membrane system according to the alphabet, the structural cells, the cell transport rules, the finite set of initial cell state rules, and the output cell identifier; optimizing parameters to be optimized of the multi-target vehicle path optimization task according to the organizational film system to obtain target parameters; performing multi-target vehicle path optimization according to the target parameters to obtain a target vehicle path; compared with the existing mode of converting a plurality of targets into a single-target optimization problem through mathematical programming, the method and the device construct the organizational film system according to the multi-target Vehicle path optimization task, optimize the parameters to be optimized of the multi-target Vehicle path optimization task according to the organizational film system, obtain the target Vehicle path, and overcome the defect that the problem of Vehicle paths (VRPTW) with Time Windows cannot be calculated in the prior art, so that the multi-target Vehicle path can be optimized, the calculation complexity is reduced, the solution set approaches the front edge surface in a uniformly distributed manner, and the solution set has high ductility.

Based on the embodiment illustrated in FIG. 4 above, another embodiment of the multi-objective vehicle path optimization apparatus based on a membrane system of the present invention is presented.

In this embodiment, the determining module 20 is further configured to determine parameters to be optimized, task configuration parameters, and task decision vectors according to the multi-objective vehicle path optimization task.

It should be noted that, the embodiment solves the multi-objective vehicle path optimization task, and therefore, the parameters to be optimized may be the number of vehicles, the path length, the path balance, and the like, which is not limited in this embodiment; the task configuration parameters may be as shown in the task configuration parameter table in table 1, but the embodiment is not limited thereto.

TABLE 1 task composition parameter Table

It should be noted that the task decision vector of the multi-objective vehicle path optimization task is shown as the following formula

The determining module 20 is further configured to establish a mathematical optimization model according to the parameter to be optimized, the task configuration parameter, and the task decision vector, and determine a model constraint condition according to the mathematical optimization model.

It should be understood that the mathematical optimization model is shown as follows:

min f₁＝∑_k∈N∑_j∈Nx_0jk

min f₂＝∑_k∈N∑_i∈N∑_j∈Nd_ijx_ijk

the model constraints are shown as follows:

the determining module 20 is further configured to determine a structural cell according to the mathematical optimization model and the model constraint condition, and determine an alphabet and a cell transport rule of the structural cell according to the parameter to be optimized.

Further, in order to improve the calculation accuracy, the determining module 20 is further configured to obtain a current character string of the structural cell, and generate a character string ordering rule of the structural cell according to the current character string;

the determining module 20 is further configured to determine an initial pattern generation rule, a string length adaptive rule, a stopping rule, and a cell optimization rule according to the mathematical optimization model and the model constraint condition;

the determining module 20 is further configured to generate a structural cell according to the string ordering rule, the initial pattern generating rule, the string length adaptive rule, the stopping rule, and the cell optimization rule;

the determining module 20 is further configured to determine an alphabet and a cell transport rule of the structural cell according to the parameter to be optimized.

It will be appreciated that the current string of the structuring cell obtained and the string ordering rules for generating the structuring cell from the current string may be a string in cell 1 as shown in the block diagram of the tissue membrane system of fig. 2

The ordering rule of (1): p is more than or equal to 1, and q is more than or equal to N_1,tIs provided with

Wherein Nd represents the non-dominant front surface of the same layer, and Cd represents the crowding degree of a certain element;

for a set of permutations of targets 1,2,3, a string of cells (i ═ 2.., 7)

Sort rules (a, b, c): p is more than or equal to 1, and q is more than or equal to N_i,tIs provided with

The cell 2 to the cell 7 adopt the ordering rules of (1,2,3), (1,3,2), (2,1,3), (2,3,1), (3,1,2) and (3,2,1), respectively.

It is understood that the determination of the initial pattern generation rule, the string length adaptation rule, the stopping rule, and the cell optimization rule based on the mathematical optimization model and the model constraints may be as follows:

first, an initial pattern generation rule (N)₁,N₂,L,N₇)→(u_1,0,u_2,0,L,u_7,0)

The initial length vector K for each cell (N)_1,0,N_2,0,L,N_7,0) The initial character string of the cell is generated by, for a cell i (i ═ 1,2, L,7), first, randomly generating an array of 1 to n, forming the elements in the alphabet table 2

Represents the jj (1. ltoreq. j. ltoreq.N) th cell in the i-th cell_i，0) An element, then, according to the rule

The character strings are sorted.

Adaptive rule of character string length (N)_1,t,N_2,t,L,N_7,t)→(N_1,t+1,N_2,t+1,L,N_7,t+1)

At the beginning of the pattern evolution, the string length needs to be adaptively adjusted. For cell 1, at time t +1, the string length

For cells 2 to 7, when t.ltoreq.t_max/6]The character string N_i,t+1＝N_i,tWhen time [ t ]_max/6]<t≤[t_max/2]Then N is_i,t+1＝max{[3/2N_i,t-3t/SN_i,t]10, when the time t_max/2]<t≤t_maxThen N is_i,t+10. Wherein the symbol [ ·]Indicating taking an integer.

③ rules of stopping

In a state s_i,tUnder, | u_i,tCaption string u of 0 |)_i,tWith λ as empty string, i.e. set ω_i,tOnly the elements in O transported from other cells evolve into state s_i,t+1When u is turned on_i,t+1＝λ，ω_i,tThe elements in (1) constitute a set TⁱAnd sent out to the environment.

Optimization rules of cell 1

State s_1,tLower, multiple set omega_1,tContaining character strings

And elements of the alphabet O transported from other cells, which elements form the set P ═ { y ═ y_i|y_iE.g. O ^ i ^ 1,2, L, m }, i.e. omega_1,t＝{u_1,tP }. The optimization rule for cell 1 is: firstly, to the character string u_1,tPerforming a merge-truncate operation with the set P to optimize the string u_1,t(ii) a Then carrying out the elite reservation genetic operation of three-target optimization to obtain a new character string u_1,t+1。

Optimization rule of cell i (i ═ 2,3, L,7)

State s_i,t(i 2, 3.., 7), set ω_i,t＝{u_i,tP }, wherein the character string

Set P ═ y_iy_iE.o ^ i ═ 1,2, L, m }, and the optimization rules of cell 2, cell 3, …, and cell 7 are: firstly, to the character string u_i,tPerforming a merge-truncate operation with the set P to optimize the string u_i,t(ii) a Then carrying out the swarm operation with the priority and the three-target optimization to obtain a new character string u_i,t+1(ii) a In the rule design, the document character string is proposedConcept for storing the superior elements of a cell generated during evolution, here the initial document string d_i＝u_i,0。

It is to be understood that generating a structuring cell according to the string ordering rule, the initial pattern generation rule, the string length adaptation rule, the stopping rule and the cell optimization rule may be generating a structuring cell as shown in the block diagram of the tissue membrane system of fig. 2.

It should be understood that the alphabet determined according to the parameter to be optimized may be O ═ x₁,x₂,L,x_npIs the alphabet, where the element x_i(i 1, 2., NP) is a set of permutations of 1,2, L, n customer numbers, corresponding to a set of encodings of the three-target VRPTW feasible solution.

It is understood that the cell transport rule for the constructed cells determined according to the parameter to be optimized may be syn { (1,2), (1,3), (1,4), (1,5), (1,6), (1,7) } indicating that the cell transport rule is adopted between cell 1 and cell 2 to cell 7, respectively.

In this embodiment, a parameter to be optimized, a task configuration parameter and a task decision vector are determined according to the multi-objective vehicle path optimization task, a mathematical optimization model is established according to the parameter to be optimized, the task configuration parameter and the task decision vector, a model constraint condition is determined according to the mathematical optimization model, a structural cell is determined according to the mathematical optimization model and the model constraint condition, an alphabet and a cell transport rule of the structural cell are determined according to the parameter to be optimized, and therefore the alphabet and the cell transport rule of the structural cell can be determined accurately and rapidly.

In this embodiment, the optimizing module 40 is further configured to control a preset script to run according to the tissue membrane system, so as to obtain a membrane system evolution pattern time and a current solution set.

It should be noted that the preset script may be a processing script set by a user according to actual requirements, and in this embodiment, a multi-target vehicle path optimization script based on a membrane system in the MATLAB program is taken as an example for description.

It should be understood that the obtaining of the membrane system evolution pattern time and the current solution set according to the said operational preset script of the said tissue membrane system control may be the obtaining of the membrane system evolution pattern time t, the current solution set

The optimizing module 40 is further configured to determine whether the membrane system evolution pattern time is equal to a preset time.

It is understood that the determination of whether the membrane system evolution pattern time is equal to the predetermined time may be a determination of t-t_maxAnd whether the current time is up or not is judged, wherein the preset time is preset according to the actual requirement of the user.

Further, the optimizing module 40 is further configured to start a character string length adaptive rule to obtain an initial character string when the membrane system evolution pattern time is not equal to the preset time;

the optimizing module 40 is further configured to optimize the initial character string according to the cell optimization rule to obtain an optimized character string;

and the optimizing module 40 is further configured to adjust the membrane system evolution pattern time, and return to the step of determining whether the membrane system evolution pattern time is equal to a preset time.

It should be understood that when the membrane system evolution pattern time is not equal to the preset time, the pattern t is evolved by the pattern t +1, and the specific process is as follows: first, a string length adaptive rule is started, a string is initialized, an initial string is obtained, and then an optimized string is obtained according to a cell optimization rule of a cell i (i ═ 1,2, L, 7).

It is understood that the step of adjusting the membrane system evolution pattern time and returning to the step of determining whether the membrane system evolution pattern time is equal to the preset time may be to set t +1 and return to the step of determining t_maxThe step (2).

The optimizing module 40 is further configured to determine a target parameter according to the current solution set when the membrane system evolution pattern time is equal to the preset time.

It should be understood that if t ═ t_maxThen the optimal solution set is output

Will be provided with

As the target parameter.

In this embodiment, a membrane system evolution pattern time and a current solution set are obtained according to the operation of the preset script for controlling the tissue membrane system, whether the membrane system evolution pattern time is equal to a preset time or not is judged, and when the membrane system evolution pattern time is equal to the preset time, a target parameter is determined according to the current solution set, so that the evolution and the stop of cells can be controlled by controlling the length of a character string, and the system can operate in a self-adaptive manner.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.

The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order, but rather the words first, second, third, etc. are to be interpreted as names.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g., a Read Only Memory (ROM)/Random Access Memory (RAM), a magnetic disk, an optical disk), and includes several instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.

The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (10)

1. A membrane system based multi-objective vehicle path optimization method, characterized in that it comprises the steps of:

when a multi-target vehicle path optimization instruction is received, acquiring a multi-target vehicle path optimization task;

determining a structural cell, an alphabet and a cell transport rule of the structural cell according to the multi-objective vehicle path optimization task;

obtaining an output cell identifier and a finite set of initial cell state rules, and generating a tissue membrane system according to the alphabet, the structural cells, the cell transport rules, the finite set of initial cell state rules, and the output cell identifier;

optimizing parameters to be optimized of the multi-target vehicle path optimization task according to the organizational film system to obtain target parameters;

and optimizing the multi-target vehicle path according to the target parameters to obtain the target vehicle path.

2. The membrane system-based multi-objective vehicle path optimization method of claim 1, wherein the step of determining the rules for cell transport of the architectural cells, the alphabet, and the architectural cells according to the multi-objective vehicle path optimization task specifically comprises:

determining parameters to be optimized, task forming parameters and task decision vectors according to the multi-target vehicle path optimization task;

establishing a mathematical optimization model according to the parameters to be optimized, the task composition parameters and the task decision vector, and determining a model constraint condition according to the mathematical optimization model;

and determining the constructed cells according to the mathematical optimization model and the model constraint conditions, and determining the alphabet and the cell transport rule of the constructed cells according to the parameters to be optimized.

3. The membrane system-based multi-objective vehicle path optimization method of claim 2, wherein the step of determining the formation cells based on the mathematical optimization model and the model constraints and determining the alphabet and the cell trafficking rules of the formation cells based on the parameters to be optimized specifically comprises:

acquiring a current character string of the structural cell, and generating a character string ordering rule of the structural cell according to the current character string;

determining an initial pattern generation rule, a character string length self-adaption rule, a stopping rule and a cell optimization rule according to the mathematical optimization model and the model constraint condition;

generating a structural cell according to the character string ordering rule, the initial pattern generating rule, the character string length self-adapting rule, the stopping rule and the cell optimizing rule;

determining the alphabet and the cell transport rule of the constructed cells according to the parameter to be optimized.

4. The method for multi-objective vehicle path optimization based on a membrane system of claim 3, wherein the step of optimizing the parameters to be optimized according to the tissue membrane system to obtain target parameters comprises:

controlling a preset script to run according to the tissue membrane system to obtain the membrane system evolution pattern time and a current solution set;

judging whether the membrane system evolution pattern moment is equal to a preset moment or not;

and when the membrane system evolution pattern moment is equal to the preset moment, determining target parameters according to the current solution set.

5. The membrane system-based multi-objective vehicle path optimization method of claim 4, wherein after the step of determining whether the membrane system evolution pattern time is equal to a preset time, the membrane system-based multi-objective vehicle path optimization method further comprises:

when the membrane system evolution pattern moment is not equal to the preset moment, starting a character string length self-adaptive rule to obtain an initial character string;

optimizing the initial character string according to the cell optimization rule to obtain an optimized character string;

and adjusting the membrane system evolution pattern moment, and returning to the step of judging whether the membrane system evolution pattern moment is equal to a preset moment or not.

6. A membrane system based multi-objective vehicle path optimization apparatus, comprising: the system comprises an acquisition module, a determination module, a tissue membrane system generation module and an optimization module;

the acquisition module is used for acquiring a multi-objective vehicle path optimization task when receiving a multi-objective vehicle path optimization instruction;

the determining module is used for determining a structural cell, an alphabet and a cell transport rule of the structural cell according to the multi-objective vehicle path optimization task;

the tissue membrane system generating module is used for acquiring an output cell identifier and a limited set of initial cell state rules and generating a tissue membrane system according to the alphabet, the structural cells, the cell transport rules, the limited set of initial cell state rules and the output cell identifier;

the optimization module is used for optimizing parameters to be optimized of the multi-target vehicle path optimization task according to the organizational film system to obtain target parameters;

the optimization module is further used for optimizing the multi-target vehicle path according to the target parameters to obtain the target vehicle path.

7. The membrane system-based multi-objective vehicle path optimization apparatus of claim 6, wherein the determination module is further configured to determine parameters to be optimized, task formation parameters, and task decision vectors from the multi-objective vehicle path optimization tasks;

the determining module is further configured to establish a mathematical optimization model according to the parameter to be optimized, the task configuration parameter and the task decision vector, and determine a model constraint condition according to the mathematical optimization model;

the determining module is further used for determining the constructed cells according to the mathematical optimization model and the model constraint conditions, and determining the alphabet and the cell transport rule of the constructed cells according to the parameters to be optimized.

8. The membrane system-based multi-objective vehicle path optimization device of claim 7, wherein the determination module is further configured to obtain a current character string of the build cell and generate a character string ordering rule for the build cell based on the current character string;

the determining module is further used for determining an initial pattern generating rule, a character string length self-adaptive rule, a stopping rule and a cell optimizing rule according to the mathematical optimization model and the model constraint condition;

the determining module is further configured to generate a structural cell according to the initial pattern generation rule, the string length adaptation rule, the stopping rule, and the cell optimization rule;

the determining module is further used for determining an alphabet and a cell transport rule of the constructed cells according to the parameter to be optimized.

9. The membrane system based multi-objective vehicle path optimization apparatus of claim 8, wherein the optimization module is further configured to obtain membrane system evolution pattern time and current solution set according to the organizational membrane system control preset script operation;

the optimization module is also used for judging whether the membrane system evolution pattern moment is equal to a preset moment or not;

and the optimization module is further used for determining target parameters according to the current solution set when the membrane system evolution pattern time is equal to the preset time.

10. The membrane system-based multi-objective vehicle path optimization apparatus of claim 9, wherein the optimization module is further configured to initiate a string length adaptation rule to obtain an initial string when the membrane system evolution pattern time is not equal to the preset time;

the optimization module is further used for optimizing the initial character string according to the cell optimization rule to obtain an optimized character string;

and the optimizing module is also used for adjusting the membrane system evolution pattern time and returning to the step of judging whether the membrane system evolution pattern time is equal to a preset time.

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