计算机科学 ›› 2026, Vol. 53 ›› Issue (2): 349-357.doi: 10.11896/jsjkx.250600197
李二超, 黄鹏飞
LI Erchao, HUANG Pengfei
摘要： 进化多任务优化是近年来计算智能领域的研究热点之一,其原理是通过任务间的知识迁移提高算法同时求解多个任务的效率。不合理的迁移知识选择会降低任务间的正向知识迁移,因此如何合理选择迁移知识成为了当前的重点研究方向。此外,在算法进化过程中,单层种群削减难以长期维持算法的高效优化性能。基于此,提出了一种基于迁移知识选择和种群削减的进化多任务优化算法(MTDE-MCT)。首先,初始化任务种群并进行适应度评估,采用基于曼哈顿距离和适应度值的联合指标进行迁移知识的选取。其次,通过子群体对齐策略消除任务间迁移个体的特征差异。最后,提出了一种多层种群削减策略,根据算法的进化阶段对任务种群进行线性规模的削减。为验证所提算法的性能,在CEC2017问题测试集和WCCI2020问题测试集上将其与近几年的经典算法进行了比较。实验结果证明,该算法在求解多任务优化问题时具有较强的竞争力。
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