演化多任务优化研究综述
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

国家自然科学基金(61906146,62036006);陕西省高校科协青年人才托举计划(20210103);中央高校基本科研业务费专项资金(JB210210)


Survey of Evolutionary Multitasking Optimization
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    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.本文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.首先我们引入了演化多任务优化问题的概念、给出了其相关的五个定义,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.最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.在本文的最后,我们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.

    Abstract:

    Evolutionary multitasking optimization focuses on population-based search and solving multiple tasks simultaneously via genetic transfer between tasks. It is considered as the third problem optimization paradigm after single-objective optimization and multiobjective optimization, and has become a hot research topic in the field of computational intelligence in recent years. The evolutionary 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. In this paper, we give a systematic review of the research progress in evolutionary multitasking in recent years. Firstly, the concept of evolutionary multitasking optimization is introduced and its related five definitions are given. We also explain this problem from the perspective of knowledge transfer optimization. Secondly, the basic framework of the evolutionary multitasking optimization algorithm is introduced 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 the end of this paper, we point out the existing challenges in the field of evolutionary multitasking optimization and make an outlook for the further development of this direction.

    参考文献
    相似文献
    引证文献
引用本文

李豪,汪磊,张元侨,武越,公茂果.演化多任务优化研究综述.软件学报,,():0

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2021-06-28
  • 最后修改日期:2022-04-14
  • 录用日期:
  • 在线发布日期: 2022-07-22
  • 出版日期:
您是第位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京市海淀区中关村南四街4号,邮政编码:100190
电话:010-62562563 传真:010-62562533 Email:jos@iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号