Bibliographic Details
| Title: |
融入任务空间转换和等分映射策略的多因子进化算法. (Chinese) |
| Alternate Title: |
Multifactorial Evolutionary Algorithm Integrating Task Space Transformation and Equal-Partitioning Mapping Strategy. (English) |
| Authors: |
罗国星, 李治强, 刘飞龙, 李佩芸, 杨夏妮 |
| Source: |
Journal of Computer Engineering & Applications; Sep2025, Vol. 61 Issue 17, p185-199, 15p |
| Subject Terms: |
SEARCH algorithms, KNOWLEDGE transfer, ALGORITHMS |
| Abstract (English): |
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 multitask 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. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
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. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |
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