SOM-guided Evolution: a Multi-Stage Framework for Multi-Objective Optimization

Balancing convergence and diversity remains a key challenge in multi-objective optimization. This paper proposes SOM-MSMOEA, a novel multi-stage evolutionary algorithm that uses a self-organizing map (SOM) to guide the search process. The evolution is divided into three stages: early, middle, and late. In the early stage, a SOM-based uniformity enhancement strategy improves population spread to avoid premature concentration. In the middle stage, an elite-led global search combined with SOM-guided local search in the convergence subspace accelerates front approximation. In the late stage, SOM-driven local search in the diversity subspace refines solution distribution. SOM-MSMOEA exploits the topological structure of SOM to guide stage-specific search strategies, effectively coordinating convergence and diversity throughout the optimization process. Experimental results demonstrate its competitive performance across a wide range of problems.