计算机工程与应用 ›› 2025, Vol. 61 ›› Issue (17): 185-199.DOI: 10.3778/j.issn.1002-8331.2412-0386

• 理论与研发 • 上一篇    下一篇

融入任务空间转换和等分映射策略的多因子进化算法

罗国星,李治强,刘飞龙,李佩芸,杨夏妮   

  1. 玉林师范学院 计算机科学与工程学院,广西 玉林 537000
  • 出版日期:2025-09-01 发布日期:2025-09-01

Multifactorial Evolutionary Algorithm Integrating Task Space Transformation and Equal-Partitioning Mapping Strategy

LUO Guoxing, LI Zhiqiang, LIU Feilong, LI Peiyun, YANG Xiani   

  1. School of Computer Science and Engineering, Yulin Normal University, Yulin, Guangxi 537000, China
  • Online:2025-09-01 Published:2025-09-01

摘要: 在运用多因子进化算法处理多任务优化问题时,不同任务之间的知识迁移可能会出现负迁移现象,以及算法容易陷入局部最优解等问题。为解决这些问题,提出了一种融入任务空间转换和等分映射策略的多因子进化算法(multifactorial evolutionary algorithm integrating task space transformation mechanism and equal-partitioning mapping strategy,MFEA-TSEM)。该算法通过引入任务空间转换来增强任务之间的相关性,从而促进任务之间的知识迁移。此外,所提出的等分映射策略应用于相同任务或不同任务之间的知识迁移,以避免任务陷入局部最优解并探索有希望的搜索区域。为了验证MFEA-TSEM算法的有效性,在单目标多任务优化问题和多目标多任务优化问题上与其他先进算法进行了比较。实验结果表明,MFEA-TSEM算法在保持解的多样性的同时,有效减少了负迁移现象的发生,从而提高了算法的全局搜索能力。

关键词: 进化算法, 多因子进化算法, 任务空间转换, 等分映射策略

Abstract: When applying multifactorial evolutionary algorithm to handle multi-task optimization problems, there may be negative transfer phenomena in the knowledge transfer between different tasks, and the algorithm is prone to getting trapped in local optimal solutions. To address these issues, a multifactorial evolutionary algorithm integrating task space transformation and equal-partitioning mapping strategy (MFEA-TSEM) is proposed. This algorithm enhances the correlation between tasks by introducing the task space transformation, thereby promoting knowledge transfer between tasks. In addition, the proposed equal-partitioning mapping strategy is applied to knowledge transfer within?the same task or between?different tasks to prevent tasks from getting trapped in local optima solutions and explore promising search regions. To verify the effectiveness of the MFEA-TSEM algorithm, it is compared with other advanced algorithms on single-objective multi-task optimization problems and multi-objective multi-task optimization problems. The experimental results show that the MFEA-TSEM algorithm effectively reduces the occurrence of negative transfer phenomena while maintaining the diversity of solutions, thus improving the global search ability of the algorithm.

Key words: evolutionary algorithm, multifactorial evolutionary algorithm, task space transformation, equal-partitioning mapping strategy