【摘要】 演化多任务优化研究利用种群进行优化搜索、借助任务间遗传信息的迁移达到多任务同时处理的目的.演化多任务优化被认为是继单目标优化、多目标优化后的第三种问题优化研究范例,是近年来计算智能领域兴起的一大研究热点.演化多任务优化算法模拟自然界选型交配和垂直文化传播的生物文化现象,通过任务间和任务内的知识迁移来促进多个优化任务各自的收敛.对近年来演化多任务优化领域的研究进展做出了系统总结:首先,引入了演化多任务优化问题的概念,给出了其相关的5个定义,并从知识迁移优化的角度对这一问题做出阐述;然后,详细介绍了演化多任务优化算法的基本框架,总结了这一算法近年来的改进情况和基于这一算法框架下其他经典算法的实现情况;之后,对演化多任务优化算法的学术、工程应用情况做出了较为完整的归纳介绍;最后,指出了演化多任务优化领域目前存在的主要问题和挑战,并对这一方向的进一步发展做出了展望.更多还原

【Abstract】 Evolutionary multitasking optimization focuses on population-based search and solves multiple tasks simultaneously via genetic transfer between tasks. It is considered as the third problem optimization paradigm after single-objective optimization and multi-objective optimization, and has become a hot research topic in the field of computational intelligence in recent years. The evolutionar y multitasking optimization algorithm simulates the biocultural phenomena of assortative mating and vertical cultural transmission in nature, which leads to the improved convergence characteristics of multiple optimization tasks with inter-task and intra-task transfer knowledge. This study gives a systematic review of the research progress in evolutionary multitasking in recent years. Firstly, the concept of evolutiona ry multitasking optimization is introduced and its related five definitions are given. This problem is also explained from the perspective of knowledge transfer optimization. Secondly, the basic framework of the evolutionary multitasking optimization algorithm is int roduced in detail. The improvement of it and the implementation of other algorithms based on it are presented. Finally, the application in academic and engineering of this algorithm is summarized. At last, the existing challenges in the field of evolutionary multitasking optimization are pointed out and an outlook is presented for the further development of this direction.更多还原