A Solution Space Partitioning Based Multi-Population Method for Dynamic Optimization

Dynamic optimization focuses on solving problems where the search space changes over time. The multi-population method is the most widely used approach for addressing such problems. Traditional multi-population methods often lack a deep understanding of the problem’s structural characteristics, such as the boundaries of basins of attraction (BoAs), which leads to redundant searches in less promising regions. Without guidance from these structural features, most populations are regenerated randomly, resulting in inefficient exploration. Furthermore, the search range for each population remains fixed and does not adapt to the BoAs, leading to the loss of tracking for certain peaks. To address these challenges, this paper proposes a solution space partitioning based multi-population method. The algorithm partitions the solution space into subspaces and leverages historical population data to assign an uncertainty property to each subspace. It further learns the problem’s BoAs to guide populations in exploiting within the BoAs while exploring outside them. A dual-layer exclusion mechanism dynamically adjusts the search and exclusion ranges based on the BoAs, ensuring precise control, preventing overlaps, and preserving diversity. Experimental results demonstrate that the proposed algorithm significantly outperforms state-of-the-art algorithms on moving peaks benchmark, generalized moving peaks benchmark, and a real-world problem: marine magnetic compensation problem.