华中科技大学学报（自然科学版）2024，Vol.52Issue(6)：24-31,8.DOI:10.13245/j.hust.240449
改进头脑风暴优化算法求解多模态多目标问题
Improved brain storm optimization algorithm for solving multimodal multi-objective problems
摘要
Abstract
Aiming at the problem that multimodal multi-objective optimization is hard to find sufficient equivalent solutions and maintain decision space diversity,a differential brain storm optimization algorithm based on zoning search and non-dominated special crowding distance sort algorithm was proposed.In the proposed algorithm,zoning search divided the decision space into multiple subspaces to reduce search difficulty and maintain population diversity.The k-means clustering strategy could locate and maintain various Pareto optimal solutions,and non-dominated special crowding distance sorting could consider the diversity of decision and objective space and serve as an environmental selection operator to filter solutions.The difference mutation operator replaced the traditional new individual generation operator to enhance the population's diversity and help locate multiple equivalent optimal solutions.Compared with 5 algorithms,the performance of the zoning search and non-dominated special crowding distance sort algorithm was validated on 13 multimodal multi-objective test functions.Experimental results show that the zoning search and non-dominated special crowding distance sort algorithm performs better than the other 5 algorithms on 11 test functions,and zoning search and non-dominated special crowding distance sort algorithm can find as many equivalent Pareto-optimal sets as possible in the decision space and guarantee a good Pareto front distribution in the objective space.关键词
多模态多目标优化/头脑风暴优化算法/多目标优化/多模态优化/群体智能Key words
multimodal multi-objective optimization/brain storm optimization algorithm/multi-objective optimization/multimodal optimization/swarm intelligence分类
信息技术与安全科学引用本文复制引用
程适,刘悦,王雪萍,靳红林..改进头脑风暴优化算法求解多模态多目标问题[J].华中科技大学学报（自然科学版）,2024,52(6):24-31,8.基金项目
国家自然科学基金资助项目(61806119) （61806119）
中央高校基本科研业务费专项资金资助项目(GK202201014) （GK202201014）
陕西省自然科学基础研究计划资助项目(2018JM6011). （2018JM6011）
