CN108438191B - Fish luring boat driving device and device type selection method - Google Patents

Fish luring boat driving device and device type selection method Download PDF

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CN108438191B
CN108438191B CN201810386103.4A CN201810386103A CN108438191B CN 108438191 B CN108438191 B CN 108438191B CN 201810386103 A CN201810386103 A CN 201810386103A CN 108438191 B CN108438191 B CN 108438191B
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高海波
熊留青
卢炳岐
杜康立
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Wuhan University of Technology WUT
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    • BPERFORMING OPERATIONS; TRANSPORTING
    • B63SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
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    • B63H21/00Use of propulsion power plant or units on vessels
    • B63H21/20Use of propulsion power plant or units on vessels the vessels being powered by combinations of different types of propulsion units
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B63SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
    • B63BSHIPS OR OTHER WATERBORNE VESSELS; EQUIPMENT FOR SHIPPING 
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    • BPERFORMING OPERATIONS; TRANSPORTING
    • B63SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
    • B63HMARINE PROPULSION OR STEERING
    • B63H21/00Use of propulsion power plant or units on vessels
    • B63H21/21Control means for engine or transmission, specially adapted for use on marine vessels
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Abstract

The invention discloses a fish attracting boat driving device and a device type selection method, wherein the fish attracting boat driving device mainly comprises an energy management system device, a diesel generating set, a storage battery pack, a bidirectional DC/DC converter, a frequency converter, a motor and a propeller. The device model selection adopts a multi-target particle swarm algorithm based on chaotic motion to optimize the model selection process, various model selection schemes can be provided for decision makers to select, and finally the decision makers sort the various schemes according to an approximate ideal solution sorting method to select the most satisfactory scheme.

Description

Fish luring boat driving device and device type selection method
Technical Field
The invention belongs to the technical field of designing of an electric propulsion system of a fish luring boat, and particularly relates to a driving device of the fish luring boat and a device type selection method.
Background
The power system of the fish attracting boat is one of the core systems, and the advantages and disadvantages of the design scheme directly relate to the safety, stability and economy of the fish attracting boat. The design scheme of the traditional fish attracting boat power system adopts an empirical design method, namely a motor with proper power is selected according to the navigation working condition of the fish attracting boat, a frequency converter with slightly high power is selected according to the power of the motor, and a diesel generator set with slightly high power is selected according to the power of the frequency converter.
The intelligent optimization algorithm such as genetic algorithm, particle swarm algorithm, ant colony algorithm and the like is more and more widely applied to the aspects of airplanes, automobiles and the like, and the optimization algorithm can assist in the model selection design of a system and select a satisfactory scheme from various schemes. Various factors need to be considered in the model selection design of the system, most popular intelligent optimization algorithms are weight setting, multi-objective problems are converted into single-objective problems to be solved, and better feasible solutions are inevitably omitted due to the fact that preference information is set in advance.
Disclosure of Invention
The invention aims to provide a fish luring boat driving device and a device and equipment type selection method, so that the safety, stability and economy of the fish luring boat are improved.
The technical scheme includes that the fishing boat driving device is mainly composed of an energy management system device, a diesel generating set, a storage battery pack, a bidirectional DC/DC converter, a frequency converter, a motor and a propeller, the energy management system device is respectively connected with the diesel generating set, the storage battery pack and the bidirectional DC/DC converter, the energy management system transmits a set target power value of the diesel generating set, a target power value of the storage battery pack and an instruction for switching an energy transmission mode of the bidirectional DC/DC converter to a P L C controller in an RS485 communication mode through a CAN bus communication mode, the diesel generating set is started, stopped, and subjected to accelerator plus and minus functions, the charging and discharging functions of the storage battery pack and the switching function between the boosting and voltage reducing modes of the bidirectional DC/DC converter, meanwhile, an SoC value of the storage battery pack and a required power value of the motor are transmitted to the energy management system device in the RS485 communication mode through a P L C controller, a three-phase output end interface of the diesel generating set is connected with a three-phase input end interface of the frequency converter through a three-phase direct current output end of the DC converter, and a three-phase direct current output end of the propeller is connected with a three-phase direct current output end of the DC/DC converter, and a three-phase direct current output end of the propeller is connected with a three-direct-current output port of the power generator.
According to the technical scheme, input signals of the energy management system device are processed through an internal P L C program, the diesel generator set, the storage battery pack and the bidirectional DC/DC converter are controlled through the P L C controller according to the processed signals, switching of four power supply modes is achieved, the storage battery pack supplies power to the frequency converter and the motor through the bidirectional DC/DC converter in the first mode, the diesel generator set does not work, the P L C controller controls the storage battery pack to discharge, the bidirectional DC/DC is in a boosting mode, the diesel generator set stops, the diesel generator set supplies power to the frequency converter and the motor in the second mode, the storage battery pack charges through the bidirectional DC/DC converter through a direct current bus of the frequency converter, the P L C controller controls the storage battery pack to charge, the bidirectional DC/DC is in a voltage reduction mode, the diesel generator set works, the diesel generator set independently supplies power to the frequency converter and the motor in the third mode, the storage battery pack does not work, the P L C controller controls the storage battery pack to stop working, the diesel generator set works in the fourth mode, the diesel generator set and the storage battery pack supplies power to the frequency converter and the motor, the P L.
The invention also provides a model selection method for the driving device equipment of the fish luring boat, which comprises the following steps: and (5) solving a Pareto solution set by a multi-target particle swarm algorithm.
Step 1, chaotic initialization of particle populations:
taking 5 factors of cost, oil consumption, emission, weight and layout as solving targets of the multi-target particle swarm algorithm, and randomly generating a 5-dimensional vector z1Then 100 components z are obtained according to the L logistic equation of equation (1)1,z2,...,z100Wherein the value of mu is 4, and a chaotic interval [ z ] of 100 rows and 5 columns is formed1;z2;...;z100]Then, the chaotic interval [ z ] is divided according to the formula (2)l;z2;...;z100]Mapping to the value range of the optimized variable (in the invention, the optimized variable of the multi-target particle swarm algorithm is system equipment, namely 6 types of diesel generator sets, 6 types of storage battery sets and 6 types of motors; wherein bjAnd ajUpper and lower limits of the optimization variable, respectively, then bj=6,aj=0;xijRounded) to form an initial population of particles (100 particles) of 100 rows and 5 columns, i.e. a device selection scheme, such as the first scheme (particles) [ x [)1,1,x1,2,x1,3,x1,4,x1,5]=[123564]Second embodiment (particles) [ x ]2,1,x2,2,x2,3,x2,4,x2,5]=[654312]Total 100 protocols (particles).
Zη+1=μzη(1-zη)n=0,1,2,...;0<zη<1;μ∈[0,4](1)
Xij=αj+(bjj)ziji=1,2,...,N;j=1,2,...,D (2)
Step 2, defining population initial individual extreme values and global extreme values:
defining the current position of each particle i (i.e. the value initialized by each particle) as an individual extremum PiVelocity v of randomly generated initial particle populationi(1)(vi(1)5-dimensional vector), obtaining a first generation solution set (except the deleted solution, the other solutions form the first generation solution set), and randomly selecting one solution, which is defined as a global extremum P, by adopting a non-dominated evaluation idea (deleting the worst solution in the solutions, for example, if one solution has higher cost, higher oil consumption, higher emission, heavy weight and difficult layout than the other solution, the solution is deleted), wherein the other solutions form the first generation solution setg
Step 3, population iteration:
updating the speed and the position of the particle swarm according to the speed updating formula (3) and the position updating formula (4) of the particle swarm (after updating, the speed and the position of each particle are 5-dimensional vectors), combining the updated particle swarm and the solution set of the previous generation to form a current-generation swarm, wherein the position of the current-generation particle is a current-generation individual extreme value PiThen, a non-dominant evaluation idea is adopted to obtain a current generation solution set, and a particle is randomly selected from the current generation solution set as a global extreme value PgFor example, the updated particle swarm after the first iteration is merged with the first generation solution set to form a second generation swarm, where the second generation particle position is a second generation individual extremum PiThen, a non-dominant evaluation idea is adopted to obtain a second generation solution set, and a particle is randomly selected from the second generation solution set to serve as a second generation global extreme value Pg
vi(t+1)=w×vi(t)+C1×r1(pi-xi(t))+C2×r2(pg-xi(t)) (3)
xi(t+1)=xi(t)+vi(t+1) (4)
Wherein p isiAnd pgFor individual extrema and global extrema, vi(t) and vi(t +1) is the particle velocity at the t iteration and at the t +1 iteration, xi(t) and xi(t +1) is the particle position at the t-th iteration and t + 1-th iteration, w is the inertia factor, C1And C2R1, r2 ∈ [0,1 ] as cognitive and social learning factors]Are random variables subject to uniform distribution.
Step 4, global extreme value PgLocal chaos optimization:
p is introduced according to formula (5)gDefinitions [0,1 ] mapping to L g-istic equations]In the formula bjAnd ajUpper and lower limits of the optimization variable, respectively, bj=6,aj0. Then 50 chaotic variables z are generated according to the formula (1)1,z2,…,z50In step 1, the values are mapped to the value intervals of the optimized variables to obtain 50 particles, a solution set of the global extremum is obtained according to the non-dominant evaluation idea, and one of the particles is randomly selected as the global extremum p'gSimultaneously p'gThe position of any particle in the surrogate population is replaced,
Figure BDA0001642273850000031
and 5, returning to the step 3 until the iteration times are finished (the iteration times are 100), and outputting the remaining schemes (a plurality of different schemes), namely a Pareto solution set.
A second part: and (5) a decision making process.
Step 6, ordering the Pareto solution set by approaching an ideal solution ordering method:
multiple alternative solutions are available according to the multi-objective particle swarm algorithm, which are feasible for the decision maker, but how the decision maker can obtain the solutions from the solutionsAnd selecting the scheme which is most suitable for the user. The invention adopts an approximate ideal solution sorting method to sort the schemes according to the requirements of the decision maker and provides the most suitable scheme for the decision maker. For the standby scheme, the optimal scheme is set to S+The worst scheme is SFor any of the alternative schemes SiThe distance between the solution and the optimal and worst solutions can be determined from equations (6) and (7), and then the relative closeness C of the solution can be determined from equation (8)iI.e. the extent to which the solution is close to the optimal solution between the optimal solution and the worst solution, C i1, represents the best solution, Ci0 indicates the worst case.
The approximate ideal solution ordering method is based on the relative closeness CiIs used to order all Pareto solution set schemes from top to bottom, relative closeness CiThe larger the value, the more standby scheme S is indicatediThe closer to the optimal solution, the more in line with the requirements of the decision maker.
Figure BDA0001642273850000041
Figure BDA0001642273850000042
Figure BDA0001642273850000043
The formulas (6) and (7) are evaluation schemes SiThe distance between the ideal solution and the positive and negative solutions is recorded as di +And di Formula (8) is scheme SiRelative closeness CiThe calculation formula of (2).
And 7, selecting an optimal selection scheme: selecting relative closeness C according to the sorting result in the step 1iThe scheme with the largest value.
The invention has the following beneficial effects: the fish luring boat adopts an electric propulsion system structure, the design process comprises two parts of structure design and equipment type selection, the system structure design mainly adopts a power supply scheme taking a diesel generator set as a main part and a storage battery set as an auxiliary part, and four power supply modes are provided, so that the diesel generator set can be ensured not to be under-loaded or overloaded. The device model selection adopts a multi-target particle swarm algorithm based on chaotic motion to optimize the model selection process, various model selection schemes can be provided for decision makers to select, and finally the decision makers sort the various schemes according to an approximate ideal solution sorting method to select the most satisfactory scheme.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a schematic view of a fishing boat drive;
figure 2 shows a process for designing the power system of the fish attracting boat.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the invention provides a fishing boat driving device, which comprises a control system, a power supply system and a propulsion system, wherein the main equipment of the control system is an energy management system device 7 as shown in figure 1, the main equipment of the power supply system comprises a diesel generator set 1, a storage battery set 6 and a bidirectional DC/DC converter 5, the main equipment of the propulsion system comprises a frequency converter 2, a motor 3 and a propeller 4, the fishing boat driving device is mainly composed of an energy management system device, a diesel generator set, a storage battery set, a bidirectional DC/DC converter, a frequency converter, a motor and a propeller, the energy management system device is respectively connected with the diesel generator set, the storage battery set and the bidirectional DC/DC converter, the energy management system adopts a CAN bus communication mode to connect a set target power value of the diesel generator set, a target power value of the storage battery set and an instruction for switching the energy transmission mode of the bidirectional DC/DC converter, adopts RS communication to transmit to a P L C controller, and finishes the functions of starting, stopping the diesel generator set, connecting an accelerator function of the storage battery set, the power output function of the storage battery set, the DC/DC converter and the propeller output power of the bidirectional DC/DC converter, the propeller output power converter of the bidirectional DC/DC converter, the propeller output port of the bidirectional DC converter, the DC/DC converter, the propeller output port of the DC converter, the propeller output port of the DC converter.
The power supply system mainly supplies power to the frequency converter and the motor through the diesel generator set and the storage battery pack. The diesel generator set can only be used as an energy source to provide electric energy, and the storage battery set can be used as an energy source to provide electric energy and can also be used as an energy storage device to recover the electric energy.
The propulsion system mainly controls the starting, stopping and accelerating and decelerating of the motor through the frequency converter, and then the motor drives the propeller to rotate through the connecting shaft, so that the ship is pushed to advance.
The energy management system device comprises a power supply management system device, a power supply management system device and a power supply management system device, wherein the power supply management system device comprises a power supply management system device, a power supply management system device and a power supply management system device, further, an input signal of the energy management system device is processed through an internal P L C program, a diesel generator set, a storage battery pack and a bidirectional DC/DC converter are controlled through a P L C controller according to the processed signal, switching of four power supply modes is achieved, the storage battery pack supplies power for a frequency converter and a motor through the bidirectional DC/DC converter, the diesel generator set does not work, the diesel generator set supplies power for the frequency converter and the motor through the bidirectional DC/DC converter, the P L C controller controls the storage battery pack to discharge, the bidirectional DC/DC is in a boost mode, the diesel generator set stops working, the diesel generator set supplies power for the frequency converter and the motor through a direct current bus of the frequency converter, the P L C controller controls the storage battery pack to charge, the bidirectional DC/DC is in.
The main equipment type selection design process of the fish luring boat power system designed by the invention comprises two parts, wherein the first part is a Pareto solution process solved by a multi-target particle swarm algorithm, and the second part is a decision process. The first part is responsible for screening out some suitable solutions (which do not have an absolute optimum or worst solution) from a large number of solutions, depending on factors (such as cost, fuel consumption, emissions, weight, layout, etc.) to be considered by a decision maker (such as a designer, shipyard, shipowner, etc.) during construction and operation. The second part is to select the optimal solution according to the specific opinion of a certain decision maker.
The invention provides a model selection method for a driving device of a fish luring boat, which comprises the following steps,
a first part: and (5) solving a Pareto solution set by a multi-target particle swarm algorithm.
Step 1, chaotic initialization of particle populations:
taking 5 factors (which are determined by a decision maker) of cost, oil consumption, emission, weight and layout as a solving target of the multi-target particle swarm algorithm, and randomly generating a 5-dimensional vector z1Then 100 components z are obtained according to the L logistic equation of equation (1)1,z2,...,z100Wherein the value of mu is 4, and a chaotic interval [ z ] of 100 rows and 5 columns is formedl;z2;...;zl00]Then, the chaotic interval [ z ] is divided according to the formula (2)1;z2;...;zl00]Mapping to the value range of the optimized variable (in the invention, the optimized variable of the multi-target particle swarm algorithm is system equipment, namely 6 types of diesel generator sets, 6 types of storage battery sets and 6 types of motors; wherein bjAnd ajUpper and lower limits of the optimization variable, respectively, then bj=6,aj=0;xijThe value of (d) is rounded),an initial population of particles (100 particles) forming 100 rows and 5 columns, i.e. a device selection scheme, such as the first scheme (particles) [ x1,1,x1,2,x1,3,x1,4,xl,5]=[123564]Second embodiment (particles) [ x ]2,1,x2,2,x2,3,x2,4,x2,5]=[654312]Total 100 protocols (particles).
Zη+1=μzη(1-zη)a=0,1,2,...;0<Zη<1;μ∈[0,4](1)
Xij=αj+(bjj)ziji=1,2,...,N;j=1,2,...,D (2)
Step 2, defining population initial individual extreme values and global extreme values:
defining the current position of each particle i (i.e. the value initialized by each particle) as an individual extremum PiVelocity v of randomly generated initial particle populationi(1)(vi(1)5-dimensional vector), obtaining a first generation solution set (except the deleted solution, the other solutions form the first generation solution set), and randomly selecting one solution, which is defined as a global extremum P, by adopting a non-dominated evaluation idea (deleting the worst solution in the solutions, for example, if one solution has higher cost, higher oil consumption, higher emission, heavy weight and difficult layout than the other solution, the solution is deleted), wherein the other solutions form the first generation solution setg
Step 3, population iteration:
updating the speed and the position of the particle swarm according to the speed updating formula (3) and the position updating formula (4) of the particle swarm (after updating, the speed and the position of each particle are 5-dimensional vectors), combining the updated particle swarm and the solution set of the previous generation to form a current-generation swarm, wherein the position of the current-generation particle is a current-generation individual extreme value PiThen, a non-dominant evaluation idea is adopted to obtain a current generation solution set, and a particle is randomly selected from the current generation solution set as a global extreme value PgFor example, the updated particle swarm after the first iteration is merged with the first generation solution set to form a second generation swarm, where the second generation particle position is a second generation individual extremum PiThen adopting non-dominant evaluation idea to obtainA second generation solution set, in which a particle is randomly selected as a second generation global extreme value Pg
vi(t+1)=w×vi(t)+C1×r1(pi-xi(t))+C2×r2(pg-xi(t)) (3)
xi(t+1)=xi(t)+vi(t+1) (4)
Wherein p isiAnd pgFor individual extrema and global extrema, vi(t) and vi(t +1) is the particle velocity at the t iteration and at the t +1 iteration, xi(t) and xi(t +1) is the particle position at the t-th iteration and t + 1-th iteration, w is the inertia factor, C1And C2R1, r2 ∈ [0,1 ] as cognitive and social learning factors]Are random variables subject to uniform distribution.
Step 4, global extreme value PgLocal chaos optimization:
p is introduced according to formula (5)gDefinitions [0,1 ] mapping to L g-istic equations]In the formula bjAnd ajUpper and lower limits of the optimization variable, respectively, bj=6,aj0. Then 50 chaotic variables z are generated according to the formula (1)1,z2,…,z50In step 1, the values are mapped to the value intervals of the optimized variables to obtain 50 particles, a solution set of the global extremum is obtained according to the non-dominant evaluation idea, and one of the particles is randomly selected as the global extremum p'gSimultaneously p'gThe position of any particle in the surrogate population is replaced,
Figure BDA0001642273850000071
and 5, returning to the step 3 until the iteration times are finished (the iteration times are 100), and outputting the remaining schemes (a plurality of different schemes), namely a Pareto solution set.
A second part: and (5) a decision making process.
And 6, ordering the Pareto solution set by approaching an ideal solution ordering method.
A plurality of alternative schemes are available according to the multi-target particle swarm optimization, the schemes are feasible for a decision maker, and the decision maker selects the scheme which is most suitable for the decision maker from the schemes. The invention adopts an approximate ideal solution sorting method to sort the schemes according to the requirements of the decision maker and provides the most suitable scheme for the decision maker. For the standby scheme, the optimal scheme is set to S+(the most ideal result of the factors considered by the decision maker for the model selection, such as cost of ten thousand, oil consumption of b liters, emission of c grams, weight of d tons and layout of e cubic meters), the worst scheme is S(the decision maker has the least desirable outcome for the factors considered for model selection, e.g., cost of A ten thousand, fuel consumption of B liters, emissions of C grams, weight of D tons, layout of E cubic meters), S for any one of the alternate scenariosiThe distance between the solution and the optimal and worst solutions can be determined from equations (6) and (7), and then the relative closeness C of the solution can be determined from equation (8)iI.e. the extent to which the solution is close to the optimal solution between the optimal solution and the worst solution, C i1, represents the best solution, Ci0 indicates the worst case.
The approximate ideal solution ordering method is based on the relative closeness CiIs used to order all Pareto solution set schemes from top to bottom, relative closeness CiThe larger the value, the more standby scheme S is indicatediThe closer to the optimal solution, the more in line with the requirements of the decision maker.
Figure BDA0001642273850000081
Figure BDA0001642273850000082
Figure BDA0001642273850000083
The formulas (6) and (7) are evaluation schemes SiThe distance between the ideal solution and the positive and negative solutions is recorded as di +And di Formula (8) is scheme SiRelative closeness CiThe calculation formula of (2).
And 7, selecting an optimal selection scheme: selecting relative closeness C according to the sorting result in the step 1iThe scheme with the largest value.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.

Claims (1)

1. A method for selecting the type of a driving device of a fish luring boat is characterized by comprising the following steps,
step 1, chaotic initialization of particle populations:
taking 5 factors of cost, oil consumption, emission, weight and layout as solving targets of the multi-target particle swarm algorithm, and randomly generating a 5-dimensional vector z1Then 100 components z are obtained according to the L g-ogic equation of formula (1)1,z2,…,z100Wherein the value of mu is 4, and a chaotic interval [ z ] of 100 rows and 5 columns is formed1;z2;…;z100]Then, the chaotic interval [ z ] is divided according to the formula (2)1;z2;…;z100]The value ranges mapped to the optimized variables, i.e. the device model selection scheme,
zn+1=μzn(1-zn) n=0,1,2,...;0<zn<1;μ∈[0,4](1)
xij=aj+(bj-aj)ziji=1,2,...,N;j=1,2,...,D (2)
step 2, defining population initial individual extreme values and global extreme values:
defining the current position of each particle i as an individual extremum PiVelocity v of randomly generated initial particle populationi(1)Obtaining a first generation solution set by adopting a non-dominant evaluation idea, randomly selecting one solution, and defining the solution as a global extremum Pg
Step 3, population iteration:
updating formula (3) and position according to the speed of the particle swarmThe new formula (4) updates the speed and the position of the particle swarm, combines the updated particle swarm with the solution set of the previous generation to form the contemporary swarm, and the position of the contemporary particle is the extreme value P of the contemporary individualiThen, a non-dominant evaluation idea is adopted to obtain a current generation solution set, and a particle is randomly selected from the current generation solution set as a global extreme value PgThen, a non-dominant evaluation idea is adopted to obtain a second generation solution set, and a particle is randomly selected from the second generation solution set to serve as a second generation global extreme value Pg
vi(t+1)=w×vi(t)+C1×r1(pi-xi(t))+C2×r2(pg-xi(t)) (3)
xi(t+1)=xi(t)+vi(t+1) (4)
Wherein p isiAnd pg are the individual extrema and the global extrema, vi(t) and vi(t +1) is the particle velocity at the t iteration and at the t +1 iteration, xi(t) and xi(t +1) is the particle position at the t-th iteration and t + 1-th iteration, w is the inertia factor, C1And C2R1, r2 ∈ [0,1 ] as cognitive and social learning factors]Random variables subject to uniform distribution;
step 4, global extreme value PgLocal chaos optimization:
p is introduced according to formula (5)gDefinitions [0,1 ] mapping to L g-istic equations]In the formula bjAnd ajUpper and lower limits of the optimization variable, respectively, bj=6,ajGenerating 50 chaotic variables z according to the formula (1) again1,z2,…,z50In step 1, the values are mapped to the value intervals of the optimized variables to obtain 50 particles, a solution set of the global extremum is obtained according to the non-dominant evaluation idea, and one of the particles is randomly selected as the global extremum p'gSimultaneously p'gThe position of any particle in the surrogate population is replaced,
Figure FDA0002485639480000021
step 5, returning to the step 3 until the iteration times are finished, and outputting the remaining schemes, namely a Pareto solution set;
step 6, ordering the Pareto solution set by approaching an ideal solution ordering method:
for the standby scheme, the optimal scheme is set to S+The worst scheme is SFor any of the alternative schemes SiThe distance between the solution and the optimal and worst solutions can be determined from equations (6) and (7), and then the relative closeness C of the solution can be determined from equation (8)iI.e. the extent to which the solution is close to the optimal solution between the optimal solution and the worst solution, Ci1, represents the best solution, Ci0, represents the worst case;
Figure FDA0002485639480000022
Figure FDA0002485639480000023
Figure FDA0002485639480000024
the formulas (6) and (7) are evaluation schemes SiThe distance between the ideal solution and the positive and negative solutions is recorded as di +And di Formula (8) is scheme SiRelative closeness CiThe calculation formula of (2);
and 7, selecting an optimal selection scheme: selecting relative closeness C according to the sorting result in the step 1iThe scheme with the largest value.
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