关键词: 均衡优化算法, 混沌映射, 生成概率, 差分变异, 柯西变异

Abstract: In order to solve the problem that the standard equalization optimization algorithm(EO) lacks the balance ability of global search and local search and is easy to fall into local optimal,an equalization optimization algorithm(VDEO) based on variable generation probability and multi-difference Cauchy variation is proposed.First,the diversity of the initial population is increased with Tent chaotic mapping,which provides the basis for optimization.Secondly,the variable generation probability is introduced to replace the original fixed value,so that the algorithm can increase the global search ability in the early stage of iteration,and pay attention to the solving accuracy in the later stage,so as to improve the balance ability of global search and local search.Finally,the fusion of different difference strategies and Cauchy variation helps the optimization process to escape from the local optimal.Aiming at 15 benchmark test functions including single-peak,multi-peak and fixed-dimension multi-peak and CEC2022 test functions,VDEO and ten heuristic algorithms EO,GWO,WOA,SCA,MFO,AOA,AVOA,BWO,AHA and POA are simulated and compared under multiple dimensions.The Wilcoxon rank sum test is performed on the experimental results of the benchmark function.Experimental results show that VDEO achieves better global search and local search balance,and has better ability to jump out of the local optimal and higher convergence accuracy.

Key words: Equilibrium optimization, Chaotic mapping, Generation probability, Differential variation, Cauchy variation