Many-Task Differential Evolutionary Algorithm Based on Bi-Space Similarity
提出一种基于双空间相似性的多任务差分进化算法，通过设计相似性度量、任务选择策略和动态知识迁移策略，减少负迁移，提升多任务优化性能。
Many-task differential evolutionary algorithm is an effective way to optimize multiple tasks simultaneously. The optimization performance of the algorithm decreases due to the negative transfer when the number of tasks is large. To address this problem, a many-task differential evolutionary algorithm based on bi-space similarity (MaTDE-BSS) is proposed to improve the positive transfer. First, the bi-space similarity metric is designed to characterize inter-task similarity quantitatively. The decision space similarity and objective space similarity are considered simultaneously in the bi-space similarity metric. Second, a task selection strategy based on evolutionary state is proposed to select the optimal source task from the source task library accurately. The source task library based on bi-space similarity metric is built for storing source tasks. Finally, a dynamic knowledge transfer strategy is proposed to improve the efficiency of knowledge positive transfer in the many-task optimization. Parameters of the knowledge transfer strategy are adjusted according to bi-space similarity metric adaptively. In addition, the experimental results show that MaTDE-BSS is able to evaluate the inter-task similarity more comprehensively. And MaTDE-BSS is more competitive compared to other many-task evolutionary algorithms.