Improved chimpanzee search algorithm based on multi-strategy fusion and its application
In order to solve the problems of initial population boundary clustering distribution,slow convergence speed,low accuracy and easy falling into local optimum in chimpanzee search algorithm,an improved chimpanzee optimization algorithm with multi-strategy fusion(SPWChoA)was proposed.Firstly,the modified Sine chaotic map is used to initialize the population to solve the aggregation and distribution problem of population boundaries.Secondly,the concept of linear weight factor and adaptive acceleration factor for particle swarm optimization is presented.This is coupled with the enhanced nonlinear convergence factor balancing algorithm's global search capability to quicken the algorithm's convergence and raise its convergence accuracy.Finally,sparrow elite mutation and Bernoulli chaotic mapping strategies improved by adaptive water wave factors are introduced to improve the ability of individuals to jump out of local optima.By comparing the optimization results of 23 benchmark functions and Wilcoxon rank sum statistical test,it can be seen that the SPWChoA optimization algorithm has stronger robustness and applicability.Lastly,to further demonstrate the SPWChoA optimization algorithm's superiority in handling actual optimization issues,the technique is applied to an engineering case.
modified Sine chaotic mappingnonlinear attenuation factorsparrow elite mutationBernoulli chaotic mappingWilcoxon rank-sum test
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维普
