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考虑全局和局部帕累托前沿的多模态多目标优化算法
李文桦; 明梦君; 张涛; 王锐; 黄生俊; 王凌
Source Publication自动化学报
ISSN0254-4156
2023
Volume49Issue:1Pages:148-160
Abstract多模态多目标优化问题(Multimodal multi-objective optimization problems, MMOPs)是指具有多个全局或局部Pareto解集(Pareto solution sets, PSs)的多目标优化问题(Multi-objective optimization problems, MOPs).在这类问题中, Pareto前沿(Pareto front, PF)上相距很近的目标向量,可能对应于决策空间中相距较远的不同解.在实际应用中全局或局部最优解的缺失可能导致决策者缺乏对问题的整体认识,造成不必要的困难或经济损失.大部分多模态多目标进化算法(Multimodal multi-objective evolutionary algorithms, MMEAs)仅关注获取尽可能多的全局最优解集,而忽略了对局部最优解集的搜索.为了找到局部最优解集并提高多模态优化算法的性能,首先提出了一种局部收敛性指标(并设计了一种基于该指标和改进种群拥挤度的环境选择策略.基于此提出了一种用于获取全局和局部最优解集的多模态多目标优化算法.经实验验证,该算法在对比的代表性算法中性能较好.
Keyword多模态多目标优化 局部收敛性 进化算法 种群多样性
DOI10.16383/j.aas.c220476
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/56216
Collection学术期刊_自动化学报
Recommended Citation
GB/T 7714
李文桦,明梦君,张涛,等. 考虑全局和局部帕累托前沿的多模态多目标优化算法[J]. 自动化学报,2023,49(1):148-160.
APA 李文桦,明梦君,张涛,王锐,黄生俊,&王凌.(2023).考虑全局和局部帕累托前沿的多模态多目标优化算法.自动化学报,49(1),148-160.
MLA 李文桦,et al."考虑全局和局部帕累托前沿的多模态多目标优化算法".自动化学报 49.1(2023):148-160.
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