《计算机应用》唯一官方网站 ›› 2024, Vol. 44 ›› Issue (5): 1325-1337.DOI: 10.11772/j.issn.1001-9081.2024020208

所属专题: 进化计算专题(2024年第5期“进化计算专题”导读,全文即将上线)

• 进化计算专题 •    

优化场景视角下的进化多任务优化综述

赵佳伟1, 陈雪峰1, 冯亮1(), 候亚庆2, 朱泽轩3, Yew‑Soon Ong4   

  1. 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, 新加坡
  • 收稿日期:2024-03-04 修回日期:2024-03-26 接受日期:2024-03-28 发布日期:2024-04-26 出版日期:2024-05-10
  • 通讯作者: 冯亮
  • 作者简介:赵佳伟(1998—),男,山西运城人,博士研究生,主要研究方向:迁移学习、进化算法、多任务优化
    陈雪峰(1989—),男,四川宜宾人,助理研究员,博士,主要研究方向:子模优化、大数据分析与优化、计算智能
    候亚庆(1990—),男,山东人,副教授,博士,主要研究方向:智能计算、群体智能、人机协同、强化学习、大数据优化
    朱泽轩(1981—),男,广东潮州人,教授,博士,主要研究方向:进化计算、机器学习、生物信息学
    Ong Yew‑Soon (1972—),男,新加坡人,教授,博士,主要研究方向:进化优化、可持续人工智能、统计机器学习、代理模型。
    第一联系人:冯亮(1987—),男,重庆人,教授,博士,CCF会员,主要研究方向:计算智能、迁移优化、多任务优化、多智能体系统、模因计算
  • 基金资助:
    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)

Review of evolutionary multitasking from the perspective of optimization scenarios

Jiawei ZHAO1, Xuefeng CHEN1, Liang FENG1(), Yaqing HOU2, Zexuan ZHU3, Yew‑Soon Ong4   

  1. 1.College of Computer Science,Chongqing University,Chongqing 400044,China
    2.School of Computer Science and Technology,Dalian University of Technology,Dalian Liaoning 116024,China
    3.College of Computer Science and Software Engineering,Shenzhen University,Shenzhen Guangdong 518060,China
    4.School of Computer Science and Engineering,Nanyang Technological University,Singapore 639798,Singapore
  • Received:2024-03-04 Revised:2024-03-26 Accepted:2024-03-28 Online:2024-04-26 Published:2024-05-10
  • Contact: Liang FENG
  • About author:ZHAO Jiawei, born in 1998, Ph. D. candidate. His research interests include transfer learning, evolutionary algorithms, multitasking optimization.
    CHEN Xuefeng, born in 1989, Ph. D., research associate. His research interests include submodule optimization, big data analysis and optimization, computational intelligence.
    HOU Yaqing, born in 1990, Ph. D., associate professor. His research interests include intelligent computing, group intelligence, human-machine collaboration, reinforcement learning, big data optimization.
    ZHU Zexuan, born in 1981, Ph. D., professor. His research interests include Evolutionary computing, machine learning, bioinformatics.
    Ong Yew‑Soon, born in 1972, Ph. D., professor. His research interests include evolutionary optimization, sustainable AI, statistical machine learning, surrogate modelling.
  • Supported by:
    Natural Science Foundation of Chongqing(CSTB2022NSCQ-MSX1285);Chongqing Overseas Educated Personnel Returning to China for Entrepreneurship and Innovation Support Program(cx2022084);National Natural Science Foundation of China(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。

关键词: 进化算法, 进化多任务优化, 知识迁移, 复杂优化问题

Abstract:

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.

Key words: evolutionary algorithm, Evolutionary MultiTasking Optimization (EMTO), knowledge transfer, complex optimization problem

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