ELPSO-C: A Clustering-Based Strategy for Dimension-Wise Diversity Control in Enhanced Leader Particle Swarm Optimization

In high-dimensional optimization, particle swarm optimization (PSO) algorithms often suffer from premature convergence due to stagnation in certain dimensions. This study proposes an enhanced variant, ELPSO-C, which integrates dimension-wise convergence detection with adaptive exploration mechanisms. By applying agglomerative clustering to inter-particle velocity diversity, ELPSO-C identifies dimensions showing signs of stagnation and selectively reintroduces diversity through targeted mutation strategies. The algorithm preserves global search capability while reducing unnecessary perturbation in well-explored dimensions. Experimental results on a suite of 18 benchmark functions across various dimensions demonstrate that ELPSO-C consistently achieves superior performance compared to existing PSO variants, especially in high-dimensional and complex landscapes. These findings suggest that dimension-aware adaptation is an effective strategy for improving PSO’s robustness and convergence quality.