Particle swarm optimization with an enhanced learning strategy and crossover operator

Particle Swarm Optimization (PSO) is a well-known swarm intelligence (SI) algorithm employed for solving various optimization problems. This algorithm suffers from premature convergence to local optima. Accordingly, a number of PSO variants have been proposed in the literature. These algorithms exploited different schemes to improve performance. In this paper, we propose a new variant of PSO with an enhanced Learning strategy and Crossover operator (PSOLC). This algorithm applies three strategies, comprising altering the exemplar particles, updating the PSO parameters, and integrating PSO with Genetic Algorithm (GA). In the proposed learning strategy, each particle is guided by the best positions (pbests) of all particles, which improves its search capability. Furthermore, the proposed parameter updating scheme computes the self-cognition coefficient for each particle based on the quality of the pbests. Finally, the proposed crossover operator injects randomness to particles to improve the global search ability. The proposed improvements in PSOLC increase its exploration capability at the early stages of the search process and its exploitation ability at the end. The derived outcome from applying PSOLC and other variants of PSO to the benchmark functions verify the superiority of the proposed algorithm in terms of accuracy and convergence speed. (C) 2021 Elsevier B.V. All rights reserved.