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Rezaei, F., Safavi, HR., GuASPSO: a new approach to hold a better exploration–exploitation balance in PSO algorithm, Soft Computing


This paper presents a new variant of particle swarm optimization (PSO) algorithm named guided adaptive search-based particle swarm optimizer (GuASPSO). In this algorithm, the personal best particles are all divided into a linearly decreasing number of clusters. Then, the unique global best guide of a given particle located at a cluster is obtained as the weighted average calculated over other clusters’ best particles. Since the clustered particles are being well-distributed over the whole search space in the clustering process, there would be a moderate distance between each particle and its unique global best guide, contributing the particles neither to be trapped in local optima nor engaged in a drift leading to lose diversity in the search space. In this approach, the number of clusters is high at the early iterations and is gradually decreased by lapse of iterations to less stress the diversity factor and further stress the fitness role to cause the particles to better converge to the optimal point. Holding this balance between global and personal bests’ role to attract the particles, on the one hand and between convergence and diversity, on the other hand, can hold a better exploration–exploitation balance in the proposed algorithm. To test the performance of GuASPSO, four popular meta-heuristic algorithms, including genetic algorithm, gravitational search algorithm, gray wolf optimizer, and PSO algorithm as well as 23 standard benchmark functions as the test beds, are employed. The experimental results validated GuASPSO as a robust well-designed algorithm to handle various optimization problems.

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