优化场景视角下的进化多任务优化综述  

Review of evolutionary multitasking from the perspective of optimization scenarios

作　　者：赵佳伟 陈雪峰 冯亮 候亚庆 朱泽轩[3] Ong Yew-Soon ZHAO Jiawei;CHEN Xuefeng;FENG Liang;HOU Yaqing;ZHU Zexuan;Yew-Soon Ong(College of Computer Science,Chongqing University,Chongqing 400044,China;School of Computer Science and Technology,Dalian University of Technology,Dalian Liaoning 116024,China;College of Computer Science and Software Engineering,Shenzhen University,Shenzhen Guangdong 518060,China;School of Computer Science and Engineering,Nanyang Technological University,Singapore 639798,Singapore)

机构地区：[1]重庆大学计算机学院,重庆400044 [2]大连理工大学计算机科学与技术学院,辽宁大连116024 [3]深圳大学计算机与软件学院,广东深圳518060 [4]南洋理工大学计算机科学与工程学院,新加坡639798

出　　处：《计算机应用》2024年第5期1325-1337,共13页journal of Computer Applications

基　　金：重庆市自然科学基金面上项目(CSTB2022NSCQ-MSX1285);重庆市留学人员回国创业创新支持计划项目(cx2022084);国家自然科学基金面上项目(62372081)。

摘　　要：随着优化问题变得日益复杂,传统的进化算法由于计算成本高昂和适用性有限而面临挑战。为了克服这些挑战,基于知识迁移的进化多任务优化(EMTO)算法应运而生,它的核心思想是通过跨任务的知识共享,同时解决多个优化问题,旨在提高进化算法在应对复杂优化场景的效率。全面总结了当前进化多任务优化研究的进展,与已有综述文章相比,从不同的研究视角进行深入探讨,并指出了现有文献中对优化场景视角分析的缺失。鉴于此,从优化问题的应用场景出发,对适用于进化多任务优化的场景及其基本解决策略进行了系统性的阐述,以帮助研究人员准确地根据具体应用需求选择合适的研究方法。此外,深入讨论进化多任务优化当前面临的挑战和未来的研究方向,旨在为未来的研究提供指导和启示。Due to the escalating complexity of optimization problems,traditional evolutionary algorithms increasingly struggle with high computational costs and limited adaptability.Evolutionary MultiTasking Optimization(EMTO)algorithms have emerged as a novel solution,leveraging knowledge transfer to tackle multiple optimization issues concurrently,thereby enhancing evolutionary algorithms’efficiency in complex scenarios.The current progression of evolutionary multitasking optimization research was summarized,and different research perspectives were explored by reviewing existing literature and highlighting the notable absence of optimization scenario analysis.By focusing on the application scenarios of optimization problems,the scenarios suitable for evolutionary multitasking optimization and their fundamental solution strategies were systematically outlined.This study thus could aid researchers in selecting the appropriate methods based on specific application needs.Moreover,an in-depth discussion on the current challenges and future directions of EMTO were also presented to provide guidance and insights for advancing research in this field.

关 键 词：进化算法 进化多任务优化 知识迁移 复杂优化问题 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]