关键词: 黑猩猩优化算法, 无限折叠迭代混沌映射, 精英反向学习, 单纯形法, 群个体记忆机制

Abstract: Aiming at the problems of chimp optimization algorithm, such as depending on initial population, easily falling into local optimal solution and low convergence accuracy, a multi-strategy chimp optimization algorithm EOSMICOA（chaotic elite opposition-based simple method improved COA） is proposed. In EOSMICOA, Chaotic elite opposition-based learning strategy is used to initialize the individual position of chimpanzee to improve the diversity and quality of the population. Meanwhile, in the process of position updating, simplex method and the individual memory mechanism are used to improve the position of poor individuals, so that can balance the development and exploration ability of the algorithm, and improve the optimization accuracy of the algorithm. In order to verify the optimization ability of the improved algorithm, EOSMICOA is compared with several algorithms on 20 complex functions. The results show that EOSMICOA has obvious advantages in convergence accuracy and optimization speed. Finally, EOSMICOA and several improved algorithms are applied to the optimization of welded beam design. The comparison results show that EOSMICOA can be applied to engineering design optimization problems more effectively.

Key words: chimp optimization algorithm, infinite folding iterative chaotic map, opposition-based learning, simple method, group individual memory mechanism