Adaptive Memetic Algorithm Based Evolutionary Multi-tasking Single-Objective Optimization
Adaptive Memetic Algorithm Based Evolutionary Multi-tasking Single-Objective Optimization
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DOI：
10.1007/978-3-319-68759-9_38
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Qunjian Chen;Xiaoliang Ma;Yiwen Sun;Zexuan Zhu
中科院分区：
文献类型：
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作者：
Qunjian Chen;Xiaoliang Ma;Yiwen Sun;Zexuan Zhu
进化多任务优化是近年来出现的一种同时解决不同优化问题的有效框架。与经典进化算法不同，多任务优化(MTO)被设计为在多任务环境中利用隐式遗传转移的优势。它通过利用不同任务之间的相似性和差异性同时处理多个任务。然而，MTO仍然存在一些问题。本文提出了一种求解单目标MTO问题的多因素模因算法。特别是，该算法引入了一种基于拟牛顿的局部搜索方法，重新初始化了一批较差的个体，并提出了一种自适应的父代选择策略。通过与CEC‘17竞赛中提出的多因子进化算法的比较，验证了该算法的有效性。
Evolutionary multitasking optimization has recently emerged as an effective framework to solve different optimization problems simultaneously. Different from the classic evolutionary algorithms, multi-task optimization (MTO) is designed to take advantage of implicit genetic transfer in a multitasking environment. It deals with multiple tasks simultaneously by leveraging similarities and differences across different tasks. However, MTO still suffers from a few issues. In this paper, a multifactorial memetic algorithm is introduced to solve the single-objective MTO problems. Particularly, the proposed algorithm introduces a local search method based on quasi-Newton, reinitializes a port of worse individuals, and suggests a self-adapt parent selection strategy. The effectiveness of the proposed algorithm is validated by comparing with the multifactorial evolutionary algorithm proposed in CEC’17 competition.