摘要
针对多模态多目标优化中存在的所得等价解数量不足和决策空间多样性维护难的问题,提出了一种基于分区搜索和非支配特殊拥挤距离排序的差分头脑风暴优化算法.在算法中分区搜索将决策空间划分为多个子空间以降低搜索难度并维持种群多样性,k-均值聚类可以定位并维持多个帕累托最优解,非支配特殊拥挤距离排序可以同时考虑决策空间和目标空间的多样性,作为环境选择算子筛选解;将差分变异算子替代传统头脑风暴优化算法新个体生成算子以增强种群的多样性,帮助定位多个等价最优解.将该算法与其他5种智能算法在13个多模态多目标测试函数上进行实验,实验结果表明:基于分区搜索和非支配特殊拥挤距离排序的差分头脑风暴优化算法在11个测试函数上的性能优于其他5个算法,该优化算法能够尽可能多地在决策空间中找到多个等价帕累托最优解集,并且能保证在目标空间中具有良好的帕累托前沿分布.
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.
作者
程适
刘悦
王雪萍
靳红林
CHENG Shi;LIU Yue;WANG Xueping;JIN Honglin(School of Computer Science,Shaanxi Normal University,Xi’an 710119,China)
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2024年第6期24-31,共8页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(61806119)
中央高校基本科研业务费专项资金资助项目(GK202201014)
陕西省自然科学基础研究计划资助项目(2018JM6011)。
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
multimodal multi-objective optimization
brain storm optimization algorithm
multi-objective optimization
multimodal optimization
swarm intelligence
