Enhanced Comprehensive Learning Particle Swarm Optimization (ECLPSO) represents a cutting-edge metaheuristic approach. This algorithm introduces several enhancements to the Comprehensive Learning Particle Swarm Optimization (CLPSO), including perturbation-based exploitation and adaptive learning probabilities. These improvements allow ECLPSO to retain robust global search capabilities while achieving high solution accuracy. In recent years, there has been a notable increase in research activity exploring the potential of combining swarm intelligence algorithms with LS. To further enhance optimization performance, this study proposes an augmented version of ECLPSO incorporating embedded local search (LS), combining ECLPSO’s powerful global search abilities alongside LS’s rapid convergence properties, the critical issue of determining the initiation point for local search is addressed through a novel entropy index. Experimental results on various benchmark functions demonstrate that ECLPSO with local search consistently locates the global optimum or near-optimal solutions with high precision, which is competitive with CLPSO and ECLPSO algorithms.