CN117077981A - Method and device for distributing stand by fusing neighborhood search variation and differential evolution - Google Patents

Method and device for distributing stand by fusing neighborhood search variation and differential evolution Download PDF

Info

Publication number
CN117077981A
CN117077981A CN202311329651.0A CN202311329651A CN117077981A CN 117077981 A CN117077981 A CN 117077981A CN 202311329651 A CN202311329651 A CN 202311329651A CN 117077981 A CN117077981 A CN 117077981A
Authority
CN
China
Prior art keywords
stand
population
machine
neighborhood
individuals
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202311329651.0A
Other languages
Chinese (zh)
Other versions
CN117077981B (en
Inventor
张丽蓉
林毅
张建伟
刘洪�
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Sichuan University
Original Assignee
Sichuan University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Sichuan University filed Critical Sichuan University
Priority to CN202311329651.0A priority Critical patent/CN117077981B/en
Publication of CN117077981A publication Critical patent/CN117077981A/en
Application granted granted Critical
Publication of CN117077981B publication Critical patent/CN117077981B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Strategic Management (AREA)
  • General Physics & Mathematics (AREA)
  • Educational Administration (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Game Theory and Decision Science (AREA)
  • Development Economics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a stand allocation method and a stand allocation device integrating neighborhood search variation and differential evolution, which relate to the technical field of airport scene resource scheduling and comprise the following steps: s1, airport incoming and outgoing flight and stand data; s2, building a stand allocation mathematical model; s3, generating an initialization population and setting parameters; determining iteration times, population size, initialization algebra, solved problem dimension and the like; s4, initializing population; randomly initializing a population by taking the upper limit and the lower limit of a search space as a boundary, and then coding the population into binary individuals; s5, group variation crossing and selection optimization; calculating objective function values and sequencing, forming individuals with the top 60% of ranks into a neighborhood, and generating new excellent individuals to enter the next generation through a neighborhood searching mutation mechanism; s6, optimizing the population by using a row 'difference update' strategy and an individual optimization strategy; the method effectively solves the problem of multi-constraint complex optimization, and improves the convergence accuracy and the machine position distribution efficiency of the algorithm.

Description

Method and device for distributing stand by fusing neighborhood search variation and differential evolution
Technical Field
The invention relates to the technical field of airport scene resource scheduling, in particular to a stand allocation method and device integrating neighborhood search variation and differential evolution.
Background
Allocation problems refer to allocation of tasks to a set of resources with minimized cost or maximized allocation benefits. Airport scene resource operation management is realized by airport staff searching for an efficient scene resource scheduling method. Among them, airport stand allocation problems (AGAP) are a very important class of complex optimization problems based on scheduled departure and landing time management of flights. The problems have the characteristics of high solving time complexity, complex real environment and the like. The efficient and reasonable allocation of the stand has important significance for improving the operation efficiency of scene resources and the satisfaction of passengers.
At present, the existing research mainly aims at an optimization target and a solving method of the stand allocation problem. The study of the optimization objectives for the stand allocation problem mainly takes into account the time costs (airport operator angle) and passenger satisfaction. There are two main categories of solutions to the stand allocation problem. The mathematical programming method is a popular solving method, but as the problem scale increases, constraint conditions increase, and the mathematical programming method gradually exposes the problems of long calculation time, low solving efficiency and the like. Heuristic algorithms are increasingly receiving extensive attention from a number of scholars. While heuristic algorithms exhibit many advantages in solving the problem of stand allocation, they still suffer from the problems of being prone to search stalls, reduced post diversity, and the like. Therefore, the method for solving the stand allocation problem by searching the intelligent optimization algorithm is necessary, has important theoretical significance and has wide practical engineering value.
The DE algorithm is an excellent evolutionary algorithm proposed by Storn and Price. The method is simple in principle and high in searching efficiency. Is widely applied to various engineering problems such as resource scheduling, industrial design and the like, and obtains excellent results. However, the DE algorithm is used to solve the complex optimization problem, and simultaneously, many disadvantages are exposed, for example, the DE algorithm enters a later searching stage, the searching capability is reduced, the searching speed is reduced, and the local optimum state is easily trapped. Many scholars have proposed a number of practical and effective methods to overcome these problems of the DE algorithm. The following are summarized in general terms: variation strategy, parameter control and algorithm cooperation.
Disclosure of Invention
Aiming at the problems of easy local optimum sinking, low later convergence speed and the like of the DE algorithm, the invention provides a stand allocation method which effectively realizes stand resource optimization and improves the utilization rate of airport key resources.
The invention adopts the following technical scheme:
a stand allocation method integrating neighborhood search variation and differential evolution comprises the following steps:
s1: acquiring airport incoming and outgoing flight and stand data; the incoming and outgoing flight data comprise a flight number, a flight entering position time, a flight leaving position time, the type of each flight and a planned passenger carrier;
the stand data comprise stand type, stand occupation time, stand size and the like;
s2: constructing a stand allocation mathematical model comprising an objective function model, a constraint condition model and a multi-objective unquantized processing model; the objective function model is determined according to the airport real operation environment, and a differential evolution method is adopted for solving;
the objective function comprises five targets of shortest walking time of passengers, balanced idle time of the machine stations, minimum flight-machine station matching difference, most full utilization of large machine stations and highest occupancy rate of machine stations;
based on the machine position parking safety interval constraint, the invention also considers the machine position uniqueness, the machine position matching constraint and the near machine position priority allocated constraint; processing the objective function in a non-quantization mode to finally obtain the objective function;
s3: randomly generating an initialization population, and setting parameters according to a table 1; basic parameters including but not limited to determining iteration times, population size, initialization algebra, and solved problem dimension;
s4: initializing a population; randomly initializing a population by taking the upper limit and the lower limit of a search space as a boundary, and then coding the population into binary individuals;
s5: population variation, machine position crossing and selection optimization; calculating objective function values and sequencing, forming individuals with the top 60% of ranks into a neighborhood, and generating new excellent individuals to enter the next generation through a neighborhood searching mutation mechanism;
s6: executing a differential upgrading strategy and an individual optimizing strategy to optimize the population; the "differential upgrade" strategy is:
re-computing the population adaptation value after mutation, crossover and selection, and re-averaging to define asCurrent individualX l Is defined as +.>If->Will thenX l Defined as the current "difference" and then willX l Upgrading, if the upgraded 'difference generation' cannot be ensured to be more excellent than the current individual, replacing the 'difference generation' position with the current individual;
s7: and (5) circulating the steps S5 and S6 to appoint training rounds and outputting the optimal result.
Preferably, in S2, the objective function model includes the following aspects:
(1) The passenger walking time is shortest
（1）；
In the middle of，M pq To be allocated to the standqOn flightspThe number of people to be transferred by the passengers,D q for passengers to arrive at the standqIs used for the distance of (a),Zis a variable which is 0 to 1,Z=0 represents a flight,Z=1 represents the stand of the machine,Z pq is a variable which is 0 to 1,Z pq =0 represents a flight,Z pq =1 represents the stand of the machine,Pfor the number of flights to be counted,Qthe number of the machine stations is the number of the machine stations;
(2) The idle time of the stand is balanced most, and when delay or conflict occurs, the balanced idle time plays a certain role in buffering so as to avoid large-scale delay;
（2）；
in the method, in the process of the invention,T pq is thatpFlight arrival at standqTime standqIs used for the idle time of the vehicle,t q is the machine idle time;
(3) The flight-station matching difference is minimum, the small aircraft can stop at the small station as much as possible, and the large aircraft can stop at the large station;
（3）；
Rho pq for the current standqThe difference between the largest machine type that can stop and the machine type that the flight should correspond to,Arepresenting the maximum machine position;
(4) Most fully utilizing large-scale machine position
（4）；
H pq The number of the airplanes parked at the medium-sized airplane station for parking the small-sized airplane at the large-sized airplane station;
(5) Based on the highest occupancy rate of the machine, the current machine occupancy time is related to the arrival and departure time of the parked flights, if the difference value between the current machine occupancy time and the arrival and departure time of the parked flights is smaller, the current machine occupancy time is less, and the fact that more flights can park the machine is indicated;
（5）；
in the middle of， For flightspLeave standqTime of (2)>Stop for flight p arrivalTime of bit q, K represents24h，ZIs a variable which is 0 to 1,Z=0 represents a flight,Z=1 represents the stand of the machine,Z pq is a variable which is 0 to 1,Z pq =0 represents a flight,Z pq =1 represents the stand of the machine,Pfor the number of flights to be counted,Qis the number of stand.
Preferably, in S2, the constraint condition model includes the following aspects:
(1) The position uniqueness, i.e. the idle position can only be occupied by one aircraft at a time;
（6）；
for flightspAssigned to the stand->For flightspIs allocated to the tarmac;
(2) The airplane position is matched and constrained, so that the airplane position is matched with the airplane type, namely, a large airplane can only stop at the large airplane position but cannot stop at the middle and small airplane positions;
（7）；
Q_tin the form of a machine location type,Q_tis of the aircraft type;
(3) The safety interval constraint is that two (or more) airplanes are parked at the same airplane station, and a certain time interval is needed between the departure time of the former airplane and the arrival time of the latter airplane;
(8)；
for the last aircraftp+1 arrival at standqTime of (2)>Is thatcurrentAircraft leaving standqIs used for the time period of (a),sepis a safety interval;
(4) Near-range priority is assigned a constraint, near-range priority being assigned means that passengers can reduce walking time;
（9）；
is near the machine position, is->Is remote, in addition to the above>Is a tarmac.
Preferably, in S2, the objective function adopts no quantization processing, adopts a weighting method to convert the multi-objective problem into a single-objective problem, and introduces a weight factor；
（10）；
（11）；
Is provided with，/>The objective function after the non-quantization treatment is:
（12）；
the final objective function is as follows:
（13）；
in the method, in the process of the invention,r 1 、r 2 、r 3 、r 4 、r 5 are all the weight factors of the weight factors,is->，/>Is->，/>Is that，/>Is->，/>Is->。
Preferably, in S4, the specific method for encoding the population into binary individuals is as follows:
the original floating point type is replaced by 0-1 for the initial population, so that the search space is simplified, the search efficiency is improved, and a random array is generated;
normalizing the random matrix by using the upper limit and the lower limit of the search space to obtain a random array ND with dimension NxD; the random array ND is an irregular floating point array, so that blind searching is easy to occur, and the efficiency is reduced;
thus, each vector is converted into binary codes distributed in 0-1 according to the discrimination conditions to form a binary populationx ij ；
Random array expression:
（14）；
the expression of the normalized random matrix of the upper limit and the lower limit of the search space is as follows:
（15）；
the random array ND is:
（16）；
the expression of the discrimination condition is:
（17）；
in the method, in the process of the invention,iis defined by the size of the populationNIt is decided that the method comprises the steps of,jthe size of (c) is determined by the dimension D,radom_arrayis a random matrix of size nxd generated by a random function,rand() Is a random number between 0 and 1,lambis a random number subject to a gaussian distribution,in the matrix with the number of rows being N and the number of columns being D,radomto return a randomly generated real number, it is in the range of [0,1 ]。shapeIn order to return to the dimensions of the matrix,upandlowis the upper and lower limit of the search space.
Preferably, in S5, the specific method for population variation crossover is:
the adaptation value of the current generation is increased, individuals with the top 60% of the rank form a neighborhood, so that the strength of neighborhood search variation is enlarged, and the mathematical expression is as follows:
(18);
wherein,Theta、parameters related to the number of iterations and obeying the exponential distribution, respectively +.>Is the g-th iteration in the neighborhoodmThe number of excellent individuals is one,m、m 1 andm 2 is taken from [1, N-1 ]]Random integers and are different from each other; />、/>Respectively, the g-th iteration is adjacent to the first iterationm 1 First, them 2 Individual excellent individuals; />And after the current global optimal individual is selected into the neighborhood, guiding the individuals in the neighborhood to gather to the most excellent individual, and further promoting global optimization.
Preferably, in S5, the method for machine level crossing and selection optimization is as follows:
machine position cross operation: for two adjacent stand-flight pairs, performing a stand crossover operation, i.e., changing the gene sequence for two or more of the stand exchange positions;
selecting an optimization operation: and (3) putting the gene sequence obtained after the cross operation into a constraint condition model in the step (S2), judging whether the gene sequence accords with the constraint condition, and then evaluating the objective function value of the stand allocation model.
Preferably, in S6, the calculation formula of the "differential upgrading" strategy is as follows:
(19)；
(20)；
in the method, in the process of the invention,X l is currently "bad",X m to update the "difference",for the adaptation value corresponding to the optimal individual +.>For the adaptation value corresponding to the worst individual, +.>，/>Is two randomly extracted individuals, +.>，，/>And randomly extracting the corresponding adaptation values of the individuals respectively.
Preferably, the individual optimization strategy is based on normal distribution, and the calculation formula is as follows:
(21)；
(22)；
wherein if it</>Will beX u Is defined as an excellent individual and is defined as an excellent individual,Up，Lowfor the upper and lower limit of the search space, +.>For the mean value of the population adaptation values of the population of the current generation,Xobeying normal distribution; equation (21) represents that the excellent individual is selected to perform mathematical transformation within the upper and lower limits of the search space by comparison +.>And->Wherein better is preserved into the next generation.
Preferably, a stand allocation device integrating neighborhood search variation and differential evolution comprises at least one processor and a memory; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the fusion neighborhood search variation and differential evolution stand allocation method of any one of claims 1 to 9.
The beneficial effects of the invention are as follows:
1. the invention effectively solves the problem of multi-constraint complex optimization.
2. The invention designs a population initialization method based on binary codes to improve the searching efficiency in the initial stage. A neighborhood search mutation mechanism with new mutation factors is provided to improve the quality of the mutated individuals and balance global optimization capacity and local optimization capacity.
3. The present invention proposes a "differential upgrade" strategy to pick out individuals with poor performance and boost their competitiveness. Individual optimization strategies based on normal distribution are designed to mine the potential of excellent individuals.
4. The airport stand allocation method based on the NSVMDE algorithm provided by the invention has the advantage that the stand allocation efficiency is remarkably improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following brief description of the drawings of the embodiments will be made, it being apparent that the drawings in the following description relate only to some embodiments of the present invention and are not limiting of the present invention.
FIG. 1 is a schematic diagram of a DE method neighborhood in the prior art.
Fig. 2 is a schematic illustration of a stand-to-flight pair of the method of the present invention.
FIG. 3 is a schematic cross-machine-position diagram of the method of the present invention.
FIG. 4 is a flow chart of the present invention.
FIG. 5 is a comparative graph of the optimizing ability of the method of the present invention and the comparative method;
wherein FIG. 5 (a) is a functional diagram of the method of the present invention and other comparison algorithmsAn iterative convergence graph with dimensions of 30;
wherein FIG. 5 (b) is a functional diagram of the method of the present invention and other comparison algorithmsAn iterative convergence graph with dimensions of 30;
wherein FIG. 5 (c) is a functional diagram of the method of the present invention and other comparison algorithmsAn iterative convergence graph with dimensions of 50;
wherein FIG. 5 (d) is a functional diagram of the method of the present invention and other comparison algorithmsAn iterative convergence graph with dimensions of 50;
wherein FIG. 5 (e) is the presentThe inventive method and other comparison algorithms are in functionAn iterative convergence graph with dimensions of 100;
wherein FIG. 5 (f) is a functional diagram of the method of the present invention and other comparison algorithmsThe iteration convergence graph in the case where the dimension is 100.
Fig. 6 is a diagram of the stand allocation results of the method of the present invention.
FIG. 7 is an optimized graph of the method of the present invention versus a comparison algorithm to solve the problem of stand allocation.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without creative efforts, based on the described embodiments of the present invention fall within the protection scope of the present invention.
The invention will be further described with reference to the drawings and examples.
Example 1
A stand allocation method integrating neighborhood search variation and differential evolution comprises the following steps:
s1: acquiring airport incoming and outgoing flight and stand data; the incoming and outgoing flight data comprise a flight number, a flight entering position time, a flight leaving position time, the type of each flight and a planned passenger carrier;
the stand data comprises a stand type, stand occupation time, stand size and the like.
S2: constructing a stand allocation mathematical model comprising an objective function model, a constraint condition model and a multi-objective unquantized processing model; the objective function model is determined according to the airport real operation environment, and a differential evolution method is adopted for solving;
the objective function comprises five targets of shortest walking time of passengers, balanced idle time of the machine stations, minimum flight-machine station matching difference, most full utilization of large machine stations and highest occupancy rate of machine stations;
based on the machine position parking safety interval constraint, the invention also considers the machine position uniqueness, the machine position matching constraint and the near machine position priority allocated constraint; and processing the objective function in a non-quantization mode to finally obtain the objective function.
The objective function model includes the following aspects:
(1) The passenger walking time is shortest
（1）；
In the middle of，M pq To be allocated to the standqOn flightspThe number of people to be transferred by the passengers,D q for passengers to arrive at the standqIs used for the distance of (a),Z pq is a variable which is 0 to 1,Z pq =0 represents a flight,Z pq =1 represents the stand of the machine,Pfor the number of flights to be counted,Qthe number of the machine stations is the number of the machine stations;
(2) The idle time of the stand is balanced most, and when delay or conflict occurs, the balanced idle time plays a certain role in buffering so as to avoid large-scale delay;
（2）；
in the method, in the process of the invention,T pq is thatpFlight arrival at standqTime standqIs used for the idle time of the vehicle,t q is the machine idle time;
(3) The flight-station matching difference is minimum, the small aircraft can stop at the small station as much as possible, and the large aircraft can stop at the large station;
（3）；
Rho pq for the current standqThe difference between the largest machine type that can stop and the machine type that the flight should correspond to,Arepresenting the maximum machine position;
(4) Most fully utilizing large-scale machine position
（4）；
H pq The number of the airplanes parked at the medium-sized airplane station for parking the small-sized airplane at the large-sized airplane station;
(5) Based on the highest occupancy rate of the machine, the current machine occupancy time is related to the arrival and departure time of the parked flights, if the difference value between the current machine occupancy time and the arrival and departure time of the parked flights is smaller, the current machine occupancy time is less, and the fact that more flights can park the machine is indicated;
（5）；
in the middle of， For flightspLeave standqTime of (2)>Stop for flight p arrivalTime of bit q, K represents24h，Z pq Is a variable which is 0 to 1,Z pq =0 represents a flight,Z pq =1 represents the stand of the machine,Pfor the number of flights to be counted,Qthe number of the machine stations is the number of the machine stations;
the constraint condition model comprises the following aspects:
(1) The position uniqueness, i.e. the idle position can only be occupied by one aircraft at a time;
（6）；
for flightspAssigned to the stand->For flightspIs allocated to the tarmac;
(2) The airplane position is matched and constrained, so that the airplane position is matched with the airplane type, namely, a large airplane can only stop at the large airplane position but cannot stop at the middle and small airplane positions;
（7）；
Q_tin the form of a machine location type,Q_tis of the aircraft type;
(3) The safety interval constraint is that two (or more) airplanes are parked at the same airplane station, and a certain time interval is needed between the departure time of the former airplane and the arrival time of the latter airplane;
（8）；
for the last aircraftp+1 arrival at standqTime of (2)>Is thatcurrentAircraft leaving standqIs used for the time period of (a),sepis a safety interval;
(4) Near-range priorities are assigned constraints, meaning that passengers can reduce walking time.
（9）；
Is near the machine position, is->Is remote, in addition to the above>Is a tarmac.
The invention considers the constraints of the time of entering and leaving the port, the satisfaction degree of passengers, the stand and the type of the flights, the safety time interval and the like, so as to balance the idle time most, ensure the shortest walking distance of the passengers, fully utilize the large stand, ensure the least matching difference degree of the stand and the highest stand occupancy as the optimization target, establish the objective function and construct the stand allocation mathematical model. For the multi-objective model, a set of single objective functions (formula 10) is given, the objective functions are processed in a non-quantization way, the multi-objective problem is converted into a single objective problem (formula 11) by a weighting method, and weight factors are introduced；
（10）；
（11）；
Is provided with，/>The objective function after the non-quantization treatment is:
（12）；
the final objective function is as follows:
（13）；
in the method, in the process of the invention,r 1 、r 2 、r 3 、r 4 、r 5 are all the weight factors of the weight factors,is->，/>Is->，/>Is that，/>Is->，/>Is->。
S3, randomly generating an initialization population, and setting parameters according to a table 1; basic parameters including but not limited to determining iteration times, population size, initialization algebra, and solved problem dimension;
s4: initializing a population; randomly initializing a population by taking the upper limit and the lower limit of a search space as a boundary, and then coding the population into binary individuals;
the specific method for encoding the population into binary individuals comprises the following steps:
the original floating point type is replaced by 0-1 for the initial population, so that the search space is simplified, the search efficiency is improved, and a random array is generated;
normalizing the random matrix by using the upper limit and the lower limit of the search space to obtain a random array ND with dimension NxD; the random array ND is an irregular floating point array, so that blind searching is easy to occur, and the efficiency is reduced;
thus, each vector is converted into binary codes distributed in 0-1 according to the discrimination conditions to form a binary populationx ij ；
Random array expression:
（14）；
the expression of the normalized random matrix of the upper limit and the lower limit of the search space is as follows:
（15）；
the random array ND is:
（16）；
the expression of the discrimination condition is:
（17）；
in the method, in the process of the invention,iis defined by the size of the populationNIt is decided that the method comprises the steps of,jthe size of (c) is determined by the dimension D,radom_arrayis a random matrix of size nxd generated by a random function,rand() Is a random number between 0 and 1,lambis a random number subject to a gaussian distribution,in the matrix with the number of rows being N and the number of columns being D,radomto return a randomly generated real number, it is in the range of [0,1 ]。shapeIn order to return to the dimensions of the matrix,upandlowis the upper and lower limit of the search space.
S5: population variation, machine position crossing and selection optimization; calculating objective function values and sequencing, forming individuals with the top 60% of ranks into a neighborhood, and generating new excellent individuals to enter the next generation through a neighborhood searching mutation mechanism;
the invention improves the poor local searching capability of the DE algorithm by dividing the neighborhood and generating excellent new individuals in the neighborhood by variation, and plays a role in balancing the global optimizing capability and the local optimizing capability to a certain extent.
The neighborhood strategy in the prior art is to combine the current individual and its nearby individuals into a search neighborhood, as shown in figure 1,and->Manifestation of (1) pair->Will have a great influence if +.>And->Is a "difference" individual, then->The performance of (a) is also affected.
In order to balance the global optimization capability and the local optimization capability of the DE algorithm, the diversity of excellent individuals is increased. Firstly, the adaptation values of the current generation are increased, individuals with the top 60% of the ranking are selected to form a neighborhood (the strength of the neighborhood search variation is increased, the algorithm diversity is not lost), the 'difference effect' can be avoided, and meanwhile, under the influence of excellent individuals, the convergence speed and the convergence precision are improved. As known from the conventional mutation strategies of the DE algorithm (DE/rand/k (k=1, 2), etc.), the mutation factor directly affects the stability of the DE algorithm, the selection of a new mutation factor is very important, and the mathematical expression of population mutation crossover is as follows:
(18);
wherein,Theta、parameters related to the number of iterations and obeying the exponential distribution, respectively +.>Is the g-th iteration in the neighborhoodmThe number of excellent individuals is one,m、m 1 andm 2 is taken from [1, N-1 ]]Random integers and are different from each other; />、/>Respectively, the g-th iteration is adjacent to the first iterationm 1 First, them 2 Individual excellent individuals; />And after the current global optimal individual is selected into the neighborhood, guiding the individuals in the neighborhood to gather to the most excellent individual, and further promoting global optimization. Due to->、/>The first 60% of globally excellent individuals already belong to the neighborhood when selected, and thus individuals who variant under their interference will be competitive. The neighborhood search variation strategy effectively improves the local optimization capability, maintains strong convergence performance even under the condition that the individual difference is smaller in the later stage of the DE algorithm, and plays an important role in balancing the global optimization capability and the local optimization capability.
The machine position crossing and selection optimization method comprises the following steps:
machine position cross operation: the allocation scheme for each individual stand-flight pair in the population-based NSVMDE algorithm is shown in fig. 2, where each individual contains N genes (representing the flight pair allocated to the current stand), the jth gene represents the jth aircraft, and the numbers on the genes represent the number of stands for the corresponding aircraft. In this individual, the number of positions that each gene can select must satisfy constraints including the time of flight departure, passenger satisfaction, the position of the stop and the type of flight and the safety interval. As shown in fig. 3, two or more crossing points are randomly set for an individual, and then a level crossing operation is performed according to the crossing probability to change the individual gene sequence;
selecting an optimization operation: and (3) putting the gene sequence obtained after the cross operation into a constraint condition model in the step (S2), judging whether the gene sequence accords with the constraint condition, and then evaluating the objective function value of the stand allocation model.
S6: executing a differential upgrading strategy and an individual optimizing strategy to optimize the population; the "differential upgrade" strategy is:
re-computing the population fitness after mutation, crossover and selection, re-averaging the population fitness values to defineCurrent individualX l Is defined as +.>If->Will thenX l Defined as the current "difference" and then willX l Upgrading, if the upgraded 'difference generation' cannot be ensured to be more excellent than the current individual, replacing the 'difference generation' position with the current individual;
the 'difference generation upgrading' strategy can continuously improve the competitiveness of low-level individuals and the convergence accuracy of the DE algorithm, and the calculation formula is as follows:
(19)；
(20)；
in the method, in the process of the invention,X l is currently "bad",X m to update the "difference",for the adaptation value corresponding to the optimal individual +.>For the adaptation value corresponding to the worst individual, +.>，/>Is two randomly extracted individuals, +.>，，/>And randomly extracting the corresponding adaptation values of the individuals respectively.
Equation (19) and equation (20) use a random extraction of individuals to pair "difference generation"X l And the optimization is carried out, so that the situation that the algorithm falls into local optimum is effectively avoided. Since the minimum adaptation value is to be found,>/>. Thus, if，/>Will guideX l Exploring the bestThe area of the individual is shown. In this way, the competitiveness of the 'bad' population is improved, the population diversity can be increased, and new power is provided for the subsequent DE algorithm evolution.
The normal distribution-based individual optimization strategy aims at mining the potential of excellent individuals, which is necessary for the multi-optimization objective problem, because the DE algorithm tends to fall into a local optimum in the vicinity of excellent individuals. The method can further optimize the individual after 'upgrading', keep excellent individual growth force, and simultaneously continuously optimize the DE algorithm towards a better solution direction, thereby playing a guiding role to a certain extent.
The individual optimization strategy is based on normal distribution, and the calculation formula is as follows:
(21)；
(22)；
wherein if it</>Will beX u Is defined as an excellent individual and is defined as an excellent individual,Up，Lowfor the upper and lower limit of the search space, +.>For the mean value of the population adaptation values of the population of the current generation,Xobeying normal distribution; equation (21) represents that the excellent individual is selected to perform mathematical transformation within the upper and lower limits of the search space by comparison +.>And->Wherein better is preserved into the next generation. Equation (21) uses search space upper and lower limits and normal distribution controlX u Can avoid the algorithm from falling into a local optimum. By and->In contrast, more excellent individuals are continually retained until the next generation.
S7: and (5) circulating the steps S5 and S6 to appoint training rounds and outputting the optimal result.
Verification example 1
In order to verify the effectiveness of the algorithm (NSVMDE) of the invention, the models of the six methods of Bina-DE, M-SSA, NEBDE, PSDE, NS-MJPSO and ACDE/F are respectively evaluated and compared with the invention, and 14 reference functions comprising single mode and multiple modes are selected to respectively verify the optimizing speed and the convergence capacity of the model. Wherein,is a unimodal function used to verify its convergence speed, < >>The multi-modal function is used for verifying the convergence capability of the invention and judging whether the convergence is global. />Is a shift unimodal function, ">Shifting the multimodal function ++>Is a mixing function. The parameter settings of the different algorithms are shown in table 1, and the results of the different dimension numerical simulation experiments are shown in tables 2, 3 and 4.
Table 1 parameter settings for different algorithms
Table 2 results of numerical simulation comparisons of the present invention and the comparison method on complex functions (d=30)
Table 3 results of numerical simulation comparisons of the present invention and the comparison method on complex functions (d=50)
Table 4 results of numerical simulation comparisons of the present invention and the comparison method on complex functions (d=100)
Where Opt is the optimal value obtained by the algorithm, worst is the Worst value, mean is the Mean, std is the variance, and Median is the Median.
As can be seen from tables 2 to 4, the present invention has the highest accuracy in the overall view. The accuracy of the Bina-DE algorithm is improved to that of the NEBDE algorithm in a leap way, which shows that a neighborhood searching mechanism plays a great role, and the local searching capability and the global searching capability of the algorithm are well balanced.
The invention compares experiments with other 5 advanced algorithms to judge that the algorithm is in different dimensionalitiesAnd->The optimizing performance of the two functions is run 30 times, and 200 substitution results are plotted as shown in fig. 5.
As shown in fig. 5, the optimizing performance of the present invention in other functions is superior to other algorithms in different dimensions. The invention has excellent optimizing capability and similar trend with NEBDE algorithm, because the neighborhood searching mechanism divides the neighborhood searching range, excellent individuals are generated in the neighborhood, the optimizing direction of the algorithm is guided, the stable and tough searching performance is still maintained in the later stage of the algorithm, and the searching precision is improved.
Verification example 2
The optimized performance of the invention under different dimensions is verified by adopting CEC2005 and CEC2017 standard test functions (NSVMDE), the problem of airport stand allocation is effectively solved, and the utilization rate of important resources of an airport is improved. Airport flight and airplane position data are selected from 26 days of 7 months of 2015 of a certain airport, the data comprise 30 airplane positions and 250 flights;
as shown in FIG. 6, the stand distribution result diagram of the present invention has a maximum distribution rate of up to 98.3%. FIG. 7 is an optimized graph of the present invention and a comparison algorithm for solving the problem of stand allocation, as shown in the figure, the method of the present invention, NSVMDE algorithm, converges with the highest accuracy, and finally converges to 0.591.
Example 2
A stand allocation device integrating neighborhood search variation and differential evolution comprises at least one processor, a memory, an input and output device and a power supply; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a binary coded representation and multi-class based track prediction method of the foregoing embodiments; the input and output equipment comprises a display, a keyboard, a mouse and a USB interface, and completes the interactive operation of data; the power source may be an external power source or a rechargeable battery to provide power to the device.
Those skilled in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a removable storage device, a read only memory (ReadOnlyMemory, ROM), a magnetic or optical disk, or other various media capable of storing program code.
The above-described integrated units of the invention, when implemented in the form of software functional units and sold or used as stand-alone products, may also be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied essentially or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a removable storage device, a ROM, a magnetic disk, or an optical disk.
The present invention is not limited to the above-mentioned embodiments, but is intended to be limited to the following embodiments, and any modifications, equivalents and modifications can be made to the above-mentioned embodiments without departing from the scope of the invention.

Claims (10)

1. The stand allocation method integrating the neighborhood search variation and the differential evolution is characterized by comprising the following steps of:
s1: acquiring airport incoming and outgoing flight and stand data;
s2: constructing a stand allocation mathematical model comprising an objective function model, a constraint condition model and a multi-objective unquantized processing model; the objective function model is determined according to the airport real operation environment, and a differential evolution method is adopted for solving;
s3: randomly generating an initialization population, and setting parameters according to a table 1; including but not limited to determining iteration number, population size, initialization algebra, and solved problem dimension;
s4: initializing a population; randomly initializing a population by taking the upper limit and the lower limit of a search space as a boundary, and then coding the population into binary individuals;
s5: population variation, machine position crossing and selection optimization; calculating objective function values and sequencing, forming individuals with the top 60% of ranks into a neighborhood, and generating new excellent individuals to enter the next generation through a neighborhood searching mutation mechanism;
s6: executing a differential upgrading strategy and an individual optimizing strategy to optimize the population; the "differential upgrade" strategy is:
re-computing the population adaptation value after mutation, crossover and selection, and re-averaging to define asCurrent individualX l Is defined as +.>If->Will thenX l Defined as the current "difference" and then willX l Upgrading, if the upgraded 'difference generation' cannot be ensured to be more excellent than the current individual, replacing the 'difference generation' position with the current individual;
s7: and (5) circulating the steps S5 and S6 to appoint training rounds and outputting the optimal result.
2. The method for assigning stand for fusion neighborhood search variation and differential evolution according to claim 1, wherein in S2, the objective function model comprises the following aspects:
(1) The passenger walking time is the shortest:
（1）；
in the middle of，M pq To be allocated to the standqOn flightspThe number of people to be transferred by the passengers,D q for passengers to arrive at the standqIs used for the distance of (a),Zis a variable which is 0 to 1,Z=0 represents a flight,Z=1 represents the stand of the machine,Z pq is a variable which is 0 to 1,Z pq =0 generationThe flight number of the on-schedule flight,Z pq =1 represents the stand of the machine,Pfor the number of flights to be counted,Qthe number of the machine stations is the number of the machine stations;
(2) Stand idle time is balanced:
（2）；
in the method, in the process of the invention,T pq is thatpFlight arrival at standqTime standqIs used for the idle time of the vehicle,t q is the machine idle time;
(3) The flight-location matching variance is the smallest:
（3）；
Rho pq for the current standqThe difference between the largest machine type that can stop and the machine type that the flight should correspond to,Arepresenting the maximum machine position;
(4) The most fully utilized of the large-scale machine position:
（4）；
H pq the number of the airplanes parked at the medium-sized airplane station for parking the small-sized airplane at the large-sized airplane station;
(5) The highest occupancy rate based on the machine position:
（5）；
in the middle of， For flightspLeave standqTime of (2)>Stop for flight p arrivalTime of bit q, K represents24h，ZIs a variable which is 0 to 1,Z=0 represents a flight,Z=1 represents the stand of the machine,Z pq is a variable which is 0 to 1,Z pq =0 represents a flight,Z pq =1 represents the stand of the machine,Pfor the number of flights to be counted,Qis the number of stand.
3. The method for assigning stand for fusion neighborhood search variation and differential evolution according to claim 1, wherein in S2, the constraint condition model comprises the following aspects:
(1) Machine position uniqueness:
（6）；
for flightspAssigned to the stand->For flightspIs allocated to the tarmac;
(2) Machine position matching constraint:
（7）；
Q_tin the form of a machine location type,Q_tis of the aircraft type;
(3) Safety interval constraint:
（8）；
for the last aircraftp+1 arrival at standqIs used for the time period of (a),/>is thatcurrentAircraft leaving standqIs used for the time period of (a),sepis a safety interval;
(4) Near-machine bit priority is constrained by allocation:
（9）；
is near the machine position, is->Remote location (s)/(s)>Is a tarmac.
4. The method for assigning stand by fusion of neighborhood search variation and differential evolution according to claim 1, wherein in S2, said objective function is processed without quantization, a weighted method is used to transform multi-objective problem into single-objective problem, and weight factors are introduced；
（10）；
（11）；
Is provided with，/>The objective function after the non-quantization treatment is:
（12）；
the final objective function is as follows:
（13）；
in the method, in the process of the invention,r 1 、r 2 、r 3 、r 4 、r 5 are all the weight factors of the weight factors,is->，/>Is->，/>Is that，/>Is->，/>Is->。
5. The method for assigning stand by fusion of neighborhood search variation and differential evolution according to claim 1, wherein in S4, the specific method for encoding the population into binary individuals is as follows:
replacing the original floating point type with 0-1 for the initial population to generate a random array;
normalizing the random matrix by using the upper limit and the lower limit of the search space to obtain a random array ND with dimension NxD;
converting each vector into binary codes distributed in 0-1 according to the discrimination conditions to form a binary populationx ij ；
Random array expression:
（14）；
the expression of the normalized random matrix of the upper limit and the lower limit of the search space is as follows:
（15）；
the random array ND is:
（16）；
the expression of the discrimination condition is:
（17）；
in the method, in the process of the invention,iis defined by the size of the populationNIt is decided that the method comprises the steps of,jthe size of (c) is determined by the dimension D,radom_arrayis a random matrix of size nxd generated by a random function,rand () Is a random number between 0 and 1,lambis a random number subject to a gaussian distribution,in the matrix with the number of rows being N and the number of columns being D,radomto return a randomly generated real number, it is in the range of [0,1 ]，shapeIn order to return to the dimensions of the matrix,upandlowis the upper and lower limit of the search space.
6. The method for assigning stand by fusion of neighborhood search variation and differential evolution according to claim 1, wherein in S5, the specific method for population variation crossover is as follows:
the adaptation value of the current generation is increased, individuals with the top 60% of the rank form a neighborhood, so that the strength of neighborhood search variation is enlarged, and the mathematical expression is as follows:
（18）；
wherein,Theta、parameters related to the number of iterations and obeying the exponential distribution, respectively +.>Is the g-th iteration in the neighborhoodmThe number of excellent individuals is one,m、m 1 andm 2 is taken from [1, N-1 ]]Random integers and are different from each other; />、/>Respectively, the g-th iteration is adjacent to the first iterationm 1 First, them 2 Individual excellent individuals; />Guiding the individuals in the neighborhood after being selected to be the individuals with the current global optimumThe body direction is gathered to the most excellent individual, and global optimization is further promoted.
7. The method for assigning stand for fusion neighborhood search variation and differential evolution according to claim 1, wherein in S5, the method for stand crossover and selection optimization is as follows:
machine position cross operation: for two adjacent stand-flight pairs, performing a stand crossover operation, i.e., changing the gene sequence for two or more of the stand exchange positions;
selecting an optimization operation: and (3) putting the gene sequence obtained after the cross operation into a constraint condition model in the step (S2), judging whether the gene sequence accords with the constraint condition, and then evaluating the objective function value of the stand allocation model.
8. The method for assigning stand by fusion of neighborhood search variation and differential evolution according to claim 1, wherein in S6, the calculation formula of the "differential upgrading" strategy is as follows:
(19)；
(20)；
in the method, in the process of the invention,X l is currently "bad",X m to update the "difference",for the adaptation value corresponding to the optimal individual,for the adaptation value corresponding to the worst individual, +.>，/>Is two randomly extracted individuals, +.>，，/>And randomly extracting the corresponding adaptation values of the individuals respectively.
9. The method for assigning stand by fusion neighborhood search variation and differential evolution according to claim 1, wherein the individual optimization strategy is based on normal distribution and the calculation formula is as follows:
(21)；
(22)；
wherein if it</>Will beX u Is defined as an excellent individual and is defined as an excellent individual,Up，Lowfor the upper and lower limit of the search space, +.>For the mean value of the population adaptation values of the population of the current generation,Xobeying normal distribution; equation (21) represents that the excellent individual is selected to perform mathematical transformation within the upper and lower limits of the search space by comparison +.>And->Wherein better is preserved into the next generation.
10. The stand allocation device integrating the neighborhood search variation and the differential evolution is characterized by comprising at least one processor and a memory; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the fusion neighborhood search variation and differential evolution stand allocation method of any one of claims 1 to 9.
CN202311329651.0A 2023-10-16 2023-10-16 Method and device for distributing stand by fusing neighborhood search variation and differential evolution Active CN117077981B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202311329651.0A CN117077981B (en) 2023-10-16 2023-10-16 Method and device for distributing stand by fusing neighborhood search variation and differential evolution

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202311329651.0A CN117077981B (en) 2023-10-16 2023-10-16 Method and device for distributing stand by fusing neighborhood search variation and differential evolution

Publications (2)

Publication Number Publication Date
CN117077981A true CN117077981A (en) 2023-11-17
CN117077981B CN117077981B (en) 2024-02-02

Family

ID=88706384

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202311329651.0A Active CN117077981B (en) 2023-10-16 2023-10-16 Method and device for distributing stand by fusing neighborhood search variation and differential evolution

Country Status (1)

Country Link
CN (1) CN117077981B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117852841A (en) * 2024-03-07 2024-04-09 四川大学 Airport joint scheduling method integrating bidirectional particle swarm and multi-strategy ant colony

Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013006549A2 (en) * 2011-07-01 2013-01-10 Trustees Of Boston University Method and system for dynamic parking allocation in urban settings
CN105956714A (en) * 2016-05-21 2016-09-21 华能澜沧江水电股份有限公司 Novel group searching method for optimal scheduling of cascade reservoir groups
CN107886260A (en) * 2017-12-28 2018-04-06 南京航空航天大学 A kind of break indices method based on robustness distribution model
CN109815523A (en) * 2018-12-05 2019-05-28 南京工程学院 Train operation multiple target differential evolution algorithm based on decomposition
CN110490326A (en) * 2019-07-17 2019-11-22 长沙学院 A kind of differential evolution method and system of adaptive multi-Vari strategy
CN110516783A (en) * 2019-07-09 2019-11-29 中国民航大学 Differential evolution algorithm and application based on wavelet basis function and optimum mutation strategy
US20200258017A1 (en) * 2019-02-12 2020-08-13 General Electric Company Aircraft stand recovery optimization
CN113570247A (en) * 2021-07-28 2021-10-29 南京航空航天大学 Multi-objective optimization method for distribution of stand-to-halt positions based on resource limited condition
CN114065896A (en) * 2021-10-25 2022-02-18 西安理工大学 Multi-target decomposition evolution algorithm based on neighborhood adjustment and angle selection strategy
CN115115097A (en) * 2022-06-10 2022-09-27 中国民航大学 Combined optimization method for airport parking space and aircraft sliding path
CN115239026A (en) * 2022-09-22 2022-10-25 珠海翔翼航空技术有限公司 Method, system, device and medium for optimizing parking space allocation
CN115511226A (en) * 2022-11-17 2022-12-23 南京可信区块链与算法经济研究院有限公司 Vehicle path optimization method based on improved differential evolution algorithm
CN115640887A (en) * 2022-10-17 2023-01-24 中国民航大学 Multi-target stand-off allocation method based on multi-strategy rapid non-dominated solution sorting genetic algorithm
CN115841220A (en) * 2022-10-24 2023-03-24 西安悦泰科技有限责任公司 Automatic allocation method for intelligent parking positions of airport
CN116739297A (en) * 2023-06-30 2023-09-12 深圳大学 Method, system, device and storage medium for distributing maintenance aircraft stand

Patent Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013006549A2 (en) * 2011-07-01 2013-01-10 Trustees Of Boston University Method and system for dynamic parking allocation in urban settings
CN105956714A (en) * 2016-05-21 2016-09-21 华能澜沧江水电股份有限公司 Novel group searching method for optimal scheduling of cascade reservoir groups
CN107886260A (en) * 2017-12-28 2018-04-06 南京航空航天大学 A kind of break indices method based on robustness distribution model
CN109815523A (en) * 2018-12-05 2019-05-28 南京工程学院 Train operation multiple target differential evolution algorithm based on decomposition
US20200258017A1 (en) * 2019-02-12 2020-08-13 General Electric Company Aircraft stand recovery optimization
CN110516783A (en) * 2019-07-09 2019-11-29 中国民航大学 Differential evolution algorithm and application based on wavelet basis function and optimum mutation strategy
CN110490326A (en) * 2019-07-17 2019-11-22 长沙学院 A kind of differential evolution method and system of adaptive multi-Vari strategy
CN113570247A (en) * 2021-07-28 2021-10-29 南京航空航天大学 Multi-objective optimization method for distribution of stand-to-halt positions based on resource limited condition
CN114065896A (en) * 2021-10-25 2022-02-18 西安理工大学 Multi-target decomposition evolution algorithm based on neighborhood adjustment and angle selection strategy
CN115115097A (en) * 2022-06-10 2022-09-27 中国民航大学 Combined optimization method for airport parking space and aircraft sliding path
CN115239026A (en) * 2022-09-22 2022-10-25 珠海翔翼航空技术有限公司 Method, system, device and medium for optimizing parking space allocation
CN115640887A (en) * 2022-10-17 2023-01-24 中国民航大学 Multi-target stand-off allocation method based on multi-strategy rapid non-dominated solution sorting genetic algorithm
CN115841220A (en) * 2022-10-24 2023-03-24 西安悦泰科技有限责任公司 Automatic allocation method for intelligent parking positions of airport
CN115511226A (en) * 2022-11-17 2022-12-23 南京可信区块链与算法经济研究院有限公司 Vehicle path optimization method based on improved differential evolution algorithm
CN116739297A (en) * 2023-06-30 2023-09-12 深圳大学 Method, system, device and storage medium for distributing maintenance aircraft stand

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
刘志君;高亚奎;章卫国;王晓光;袁燎原;: "变邻域分解多目标自适应差分进化算法", 控制理论与应用, no. 11, pages 54 - 63 *
孙灿;周新宇;王明文;: "一种融合邻域搜索的多策略差分进化算法", 系统仿真学报, no. 06, pages 101 - 114 *
陈华群;: "基于图论和蚁群算法的机场停机位分配优化研究", 科技通报, no. 10, pages 243 - 246 *
高阳: "基于多目标优化的停机位调度方法研究", 中国优秀硕士学位论文全文数据库工程科技Ⅱ辑, no. 2, pages 031 - 1361 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117852841A (en) * 2024-03-07 2024-04-09 四川大学 Airport joint scheduling method integrating bidirectional particle swarm and multi-strategy ant colony
CN117852841B (en) * 2024-03-07 2024-05-07 四川大学 Airport joint scheduling method integrating bidirectional particle swarm and multi-strategy ant colony

Also Published As

Publication number Publication date
CN117077981B (en) 2024-02-02

Similar Documents

Publication Publication Date Title
CN108880663B (en) Space-ground integrated network resource allocation method based on improved genetic algorithm
Yu et al. Unit commitment using Lagrangian relaxation and particle swarm optimization
CN109840154B (en) Task dependency-based computing migration method in mobile cloud environment
CN103279793B (en) A kind of unmanned vehicle formation method for allocating tasks determined under environment
CN109544998B (en) Flight time slot allocation multi-objective optimization method based on distribution estimation algorithm
CN117077981B (en) Method and device for distributing stand by fusing neighborhood search variation and differential evolution
CN104035816A (en) Cloud computing task scheduling method based on improved NSGA-II
CN112465120A (en) Fast attention neural network architecture searching method based on evolution method
CN109815541B (en) Method and device for dividing rail transit vehicle product parts and modules and electronic equipment
CN104572297A (en) Hadoop job scheduling method based on genetic algorithm
CN104077634B (en) active-reactive type dynamic project scheduling method based on multi-objective optimization
CN113537365B (en) Information entropy dynamic weighting-based multi-task learning self-adaptive balancing method
CN109242290B (en) Automatic generation method for unmanned aerial vehicle group action scheme
CN114417735A (en) Multi-unmanned aerial vehicle cooperative task planning method in cross-regional combined combat
CN112732436A (en) Deep reinforcement learning acceleration method of multi-core processor-single graphics processor
CN110222816B (en) Deep learning model establishing method, image processing method and device
He Optimization of edge delay sensitive task scheduling based on genetic algorithm
Zhou et al. A novel mission planning method for UAVs’ course of action
CN113127167B (en) Heterogeneous resource intelligent parallel scheduling method based on improved genetic algorithm
CN114781508A (en) Clustering-based satellite measurement and control scheduling method and system
CN113962013B (en) Aircraft countermeasure decision making method and device
CN115113647A (en) Cluster unmanned aerial vehicle resource scheduling method based on intelligent evolution algorithm
CN111831421B (en) Task allocation method and terminal equipment
CN113988662A (en) Emergency power supply optimal configuration method and device, electronic equipment and storage medium
CN114707707A (en) Method and system for scheduling AGV task based on improved genetic algorithm

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant