A perturbed particle swarm algorithm for numerical optimization
Applied Soft Computing
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Evolutionary Computation
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IEEE Transactions on Evolutionary Computation
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In order to keep balance of premature convergence and diversity maintenance, an AntiCentroid-oriented particle updating strategy and an improved Particle Swarm Algorithm (ACoPSA) are presented in this paper. The swarm centroid reflects the search focus of the PSA algorithm and its distance to the global best particle (gbest) indicates the behavior difference between the population search and the gbest. Therefore the directional vector from the swarm centroid to the gbest implies an effective direction that particles should follow. This direction is utilized to update the particle velocity and to guide swarm search. Experimental comparisons among ACoPSA, standard PSA and a recent perturbed PSA are made to validate the efficacy of the strategy. The experiments confirm us that the swarm centroid-guided particle updating strategy is encouraging and promising for stochastic heuristic algorithms.