2025年 05期

摘要(Abstract)：

为了快速、准确地追踪新环境下的Pareto解集，解决传统单一中心点预测不准确且产生的代表性精英个体数量少等问题，提出基于决策空间分段预测(SPDS)的动态多目标优化策略。首先，将前一时刻获取到的Pareto解集按照欧氏距离均匀分成3段，确保搜索空间的广度与搜索效率；其次，求得每段Pareto解集的中心点移动步长；最后，通过线性预测机制分段预测下一代种群，使算法更具鲁棒性和适应性。为了验证SPDS策略的有效性，采用15个标准动态测试函数实验对比动态非支配排序遗传算法-Ⅱ(DNSGA-Ⅱ-A)、种群预测策略(PPS)和基于特殊点的预测策略(SPPS)等算法在使用和不使用SPDS策略的性能，并将SPDS-DNSGA-Ⅱ-A算法应用于柴油机比例-积分-微分参数优化中。结果表明，SPDS策略在反向世代距离指标上的最优率分别比对比算法高78.33%,收敛性和多样性均有不同程度提高，可以适应动态环境变化，有效解决动态多目标优化问题。

关键词(KeyWords)：动态多目标优化;进化算法;分段预测;决策空间

基金项目(Foundation):国家自然科学基金项目(62063019);; 甘肃省自然科学基金项目(20JR10RA152,22JR5RA241)

作者(Author): 李二超,刘辰淼

DOI:10.13349/j.cnki.jdxbn.20250705.001

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