Novel multi-target particle swarm optimization method

A multi-objective particle swarm and optimization method technology, applied in the field of new multi-objective particle swarm optimization, can solve problems that have not been effectively solved, and achieve the effects of improving operating efficiency, avoiding premature convergence, and improving diversity and distribution

Inactive Publication Date: 2017-02-15
方洋旺
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The above algorithms have enhanced the global search ability of the algorithm to varying degrees, but they have not been effectively solved for the multi-peak problem in the multi-objective optimization problem.

Method used

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  • Novel multi-target particle swarm optimization method
  • Novel multi-target particle swarm optimization method
  • Novel multi-target particle swarm optimization method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0062] A new multi-objective particle swarm optimization method, the method is based on shared learning and Cauchy mutation, the method uses the shared learning factor to change the velocity and position update formula of the particle, and uses the Cauchy mutation operator to update the particle individual optimal Position and external files, the method improves the global search ability and local optimization accuracy of particles, and makes the algorithm quickly approach the Pareto front while avoiding premature convergence of the algorithm.

[0063] Taking the particle average optimal position C as the shared learning factor, it is defined as:

[0064]

[0065] Among them, t is the current iteration number, M is the particle swarm size, i represents the i-th particle, P i is the average optimal position of the i-th particle.

[0066] The particle velocity update formula is as follows:

[0067] V ij (t+1)=wV ij (t)+c 1 r 1 (P ij (t)-X ij (t))+c 2 r 2 (G j (t)-X...

Embodiment 2

[0088] In this embodiment, the method described in Embodiment 1 is used to test the multi-objective test function; five typical multi-objective test functions ZDT1, ZDT2, ZDT3, ZDT4 and ZDT6 of the ZDT test function set are selected for testing. The specific form of the test function is shown in Table 1. Among them, the Pareto front of ZDT1 is convex, the Pareto front of ZDT2 is non-convex, the Pareto front of ZDT3 is composed of 5 non-continuous convex regions, and the Pareto front of ZDT4 has 21 9 A local optimum is mainly used to test the ability of the method described in this example to solve multimodal problems. ZDT6 has a non-convex and non-uniform Pareto front, which is used to test the ability of the algorithm to maintain population diversity. The above experimental test function can comprehensively test the pros and cons of the multi-objective optimization algorithm from the aspects of non-convexity, non-uniformity, discontinuity and multi-peak.

[0089] Test functi...

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Abstract

The invention mainly belongs to the technical field of multi-target optimization, and particularly relates to a novel multi-target particle swarm optimization method. The method is based on shared learning and Cauthy mutation. Shared learning factors are adopted in the method to change the speed and position updating formula of particles, and the optimal position of the particle individuals and external files are updated by adopting Cauthy mutation operators. According to the method, the global search capability and the local optimization search precision of the particles can be enhanced, and the algorithm is enabled to fast approach Pareto leading edge and premature convergence of the algorithm can be avoided. According to the method, solution convergence, diversity and distributivity of the multi-target particle swarm algorithm for processing the problem of multi-target optimization can be enhanced.

Description

technical field [0001] The invention mainly belongs to the technical field of multi-objective optimization, and in particular relates to a novel multi-objective particle swarm optimization method. Background technique [0002] The optimization problem in practical engineering is usually a multi-objective optimization problem (multi-objective optimization problem, MOP) with multiple objective functions. These objective functions often conflict with each other, and it is impossible to achieve the optimum at the same time. It is necessary to coordinate and A compromise is obtained to obtain the Pareto optimal solution set that cannot be compared. [0003] Inspired by different backgrounds, a large number of multi-objective intelligent optimization algorithms for solving multi-objective optimization problems have emerged. Among them, the particle swarm optimization algorithm is widely used in multi-objective optimization problems due to its advantages of simple parameters, fast...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04
CPCG06Q10/04
Inventor 方洋旺彭广伍有利彭维仕柴栋
Owner 方洋旺
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