CAPSO: Chaos Adaptive Particle Swarm Optimization Algorithm

As an influential technology of swarm evolutionary computing (SEC), the particle swarm optimization (PSO) algorithm has attracted extensive attention from all walks of life. However, how to rationally and effectively utilize the population resources to equilibrate the exploration and utilization is...

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Bibliographic Details
Published in:IEEE access Vol. 10; pp. 29393 - 29405
Main Authors: Duan, Youxiang, Chen, Ning, Chang, Lunjie, Ni, Yongjing, Kumar, S. V. N. Santhosh, Zhang, Peiying
Format: Journal Article
Language:English
Published: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Acceleration
Adaptive algorithms
Algorithms
Chaos
Chaos theory
Convergence
Evolutionary algorithms
function optimization
Optimization
Particle swarm optimization
Search problems
Sociology
Statistics
Swarm evolutionary computing
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:As an influential technology of swarm evolutionary computing (SEC), the particle swarm optimization (PSO) algorithm has attracted extensive attention from all walks of life. However, how to rationally and effectively utilize the population resources to equilibrate the exploration and utilization is still a key dispute to be resolved. In this paper, we propose a novel PSO algorithm called Chaos Adaptive Particle Swarm Optimization (CAPSO), which adaptively adjust the inertia weight parameter <inline-formula> <tex-math notation="LaTeX">w </tex-math></inline-formula> and acceleration coefficients <inline-formula> <tex-math notation="LaTeX">c_{1},c_{2} </tex-math></inline-formula>, and introduces a controlling factor <inline-formula> <tex-math notation="LaTeX">\gamma </tex-math></inline-formula> based on chaos theory to adaptively adjust the range of chaotic search. This makes the algorithm have favorable adaptability, and then the particles cannot only effectively prevent missing the global optimal solution, but also have a high probability of jumping out of the local optimal solution. To verify the stability, convergence speed, and accuracy of CAPSO, we conduct ample experiments on 6 test functions. In addition, to further verify the effectiveness and scalability of CAPSO, comparative experiments are carried out on the CEC2013 test suite. Finally, the results prove that CAPSO outperforms other peer algorithms to achieve satisfactory performance.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3158666