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SOM-guided evolution: a multi-stage framework for multi-objective optimization
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AI
DOI：10.1007/s12293-026-00530-5.png)
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Abstract
En 中文
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.
Keywords:
Multi-objective evolutionary algorithm
Self-organizing map
Multi-stage
Uniformity enhancement
Elite-led global search
Local search
Journal
IF:
2.3
Papers:
453
Citations:
718