A large-scale multi-objective optimization algorithm integrating multi-directional fuzzy sampling and multi-source learning competitive swarm optimizer

Abstract

Existing directional sampling-based methods for large-scale multi-objective optimization problems (LSMOPs) show promise but are often constrained by their reliance on singular representative solutions and insufficient diversity in search directions. To overcome these limitations, this paper proposes LSMDCSO, a novel algorithm integrating multi-directional fuzzy sampling (MDFS) and a multi-source learning competitive swarm optimizer (MLCSO). First, LSMDCSO utilizes angle-penalized distance to select representative solutions as guiding anchors. It then constructs a comprehensive set of search directions by synergizing approximate gradient-based sampling for convergence and orthogonal sampling for diversity. A fuzzy variable operator is incorporated to further enhance solution adaptability in high-dimensional spaces. Additionally, an MLCSO is designed to perform fine-grained exploitation, compensating for sampling imprecision. Experimental evaluations on the LSMOP, ZCAT, and real-world TREE benchmarks demonstrate that LSMDCSO outperforms state-of-the-art algorithms, exhibiting superior capabilities in balancing convergence and diversity for solving complex LSMOPs.