Hybrid particle swarm optimization algorithm with simple and efficient coupling strategy
LI Wen-feng
CAO Yu-lian
ZHANG Han
Abstract：A quantitative analysis method is established to judge the pros and cons of the coupling strategy. The shortcomings of the existing hybrid particle swarm optimization algorithm with intermediate starting local search (LS) are discovered. And then a simple and efficient coupling strategy is proposed. Based on this strategy, the traditional LS method with fast convergence performance is introduced into the comprehensive learning particle swarm optimizer (CLPSO)algorithm. Then the CLPSO hybrid algorithm with LS(CLPSO-LS)is proposed. Numerous experiments are carried out to test the performance of the four different LS methods based hybrid algorithms on 10-dimensional, 30-dimensional and 50-dimensional problems of eleven benchmark functions. The results show that the performance of the four CLPSO-LS algorithms is superior to that of CLPSO algorithm,which verifies the validity of the hybrid algorithms. Among them,the performance of the BFGS quasi-Newton method based hybrid algorithm is the best.Finally,comparison results with eight advanced particle swarm optimization algorithms demonstrate that the performance of the CLPSO-LS algorithm as an improved CLPSO algorithm is superior to the compared algorithms including existing improved CLPSO algorithms,which further validates the superiority of the CLPSO-LS algorithm.
Keywords：quantitative analysiscoupling strategylocal searchparticle swarm optimization(PSO)
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 13-23 )