摘要
多模态多目标优化是同一个Pareto前沿具有多个Pareto解集的复杂多目标优化问题,已成为多目标优化领域中的重要研究方向。已有的算法能够较好地解决该问题,但在解的多样性、收敛性及处理目标冲突方面表现出一定的局限性,如难以有效覆盖所有解集或在优化过程中出现收敛过早的现象。为解决这些问题,提出了一种新的基于生长神经气体网络(Growing Neural Gas,GNG)的环境选择策略的多模态多目标优化算法。该方法通过引入自适应拓扑结构,动态调整种群分布,同时利用加权的欧氏距离计算拥挤度以进行环境选择,提高种群的多样性和均匀性。此外,引入知识转移机制增强算法搜索能力,进一步提高解的多样性和收敛性。为验证算法的有效性,在HYL和MMF测试函数集上进行了实验。实验结果表明:所提算法在解的分布均匀性、Pareto前沿的收敛性及目标空间的覆盖性等方面的表现均优于5种对比算法。
Multi-modal multi-objective optimization is a complex multi-objective optimization problem with multiple Pareto solutions on the same Pareto front.It has become an important research direction in the field of multi-objective optimization.Existing algorithms can solve this problem well,but they have certain limitations in terms of solution diversity,convergence and handling of target conflicts,such as difficulty in effectively covering all solution sets or premature convergence during the optimization process.To solve these problems,a new multi-modal multi-objective optimization algorithm based on the environment selection strategy of the growing neural gas(GNG)network is proposed.This method introduces an adaptive topological structure to dynamically adjust the population distribution,and uses weighted Euclidean distance to calculate the crowding degree for environment selection,thereby improving the diversity and uniformity of the population.In addition,the knowledge transfer mechanism is introduced to enhance the algorithm’s search ability and further improve the diversity and convergence of solutions.To verify the effectiveness of the algorithm,experiments are carried out on the HYL and MMF test function sets.The experimental results show that the proposed algorithm performs better than the five comparison algorithms in terms of solution distribution uniformity,Pareto front convergence and target space coverage.
作者
宣贺君
寇丽博
刘如意
XUAN Hejun;KOU Libo;LIU Ruyi(School of Computer and Information Technology,Xinyang Normal University,Xinyang,Henan 464000,China;Henan Key Laboratoray of Education Big Data Analysis and Application,Xinyang,Henan 464000,China;School of Computer Science and Technology,Xidian University,Xi’an 710071,China)
出处
《计算机科学》
北大核心
2025年第S2期178-184,共7页
Computer Science
基金
国家自然科学基金青年基金(62202366)
河南省重点研发专项(241111212200)
河南省科技研发计划联合基金项目(20240012)
河南省教育课程改革研究项目(2025-JSJYYB-029)。
关键词
多模态
多目标
神经网络
知识转移
环境选择
Multi-modality
Multi-objective
Neural network
Knowledge transfer
Environmental selection
