多策略大规模多目标优化算法
CSTR:
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

安徽省高校自然科学基金 (2022AH040064); 安徽省高校学科(专业)拔尖人才学术资助项目 (gxbjZD2022021); 安徽省智能计算理论及应用优秀科研创新团队 (2023AH010044); 中央高校基本科研业务费专项资金 (PA2023GDSK0049)


Large-scale Multi-objective Optimization Algorithm with Multiple Strategies
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:

    在解决大规模多目标优化问题(LSMOP)时, 随着决策变量维数的增加会使得MOEA/D算法在决策空间扩展性差且容易收敛于局部最优. 针对这一问题, 提出了一种大规模多策略MOEA/D算法 (MSMOEA/D). MSMOEA/D算法在优化过程中引入了一种基于自动编码器的混合初始化策略, 以改善初始种群的覆盖程度, 从而促进全局搜索. 然后, 提出一种基于聚合函数值的邻域调整策略, 通过调整邻域大小, 能够在搜索过程中更精确地控制搜索范围, 避免因邻域过大或过小而导致的搜索效率低下. 此外, 在优化过程中采用了基于非支配排序的变异选择策略. 不同的子问题根据位于非支配排序第1层的个体数量选择变异策略, 避免种群陷入局部最优, 提高算法的整体性能. 最后, 使用LSMOP和DTLZ测试问题对MSMOEA/D算法和其他已有算法进行了评估. 实验结果证实了MSMOEA/D算法解决大规模多目标优化问题的有效性.

    Abstract:

    When dealing with large-scale multi-objective optimization problem (LSMOP), the MOEA/D algorithm shows poor scalability in the decision space and a tendency to converge to local optima as the dimensionality of decision variables increases. To address this issue, this study proposes a large-scale MOEA/D algorithm with multiple strategies (MSMOEA/D). The MSMOEA/D algorithm introduces a hybrid initialization strategy based on autoencoders in the optimization process to expand the coverage of the initial population, thus promoting global search. Moreover, a neighborhood adjustment strategy based on aggregation functions is proposed, which can more accurately control the search range during the search process by adjusting neighborhood sizes, thereby avoiding low search efficiency caused by excessively large or small neighborhoods. Furthermore, a mutation-selection strategy based on non-dominated sorting is adopted during the optimization process. Different subproblems select their mutation strategies according to the number of individuals in the first level of non-dominated sorting to avoid the population falling into local optima and enhance the overall performance of the algorithm. Finally, the MSMOEA/D algorithm and other existing algorithms are evaluated using LSMOP and DTLZ test problems. Experimental results verify the effectiveness of the proposed algorithm for solving LSMOPs.

    参考文献
    相似文献
    引证文献
引用本文

裴倩如,邹锋,陈得宝.多策略大规模多目标优化算法.计算机系统应用,2024,33(11):142-156

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:2024-04-19
  • 最后修改日期:2024-05-14
  • 录用日期:
  • 在线发布日期: 2024-09-29
  • 出版日期:
文章二维码
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:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) 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