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Particle Swarm Optimization (PSO) is a newly-emerging heuristic global search algorithm based on swarm intelligence and it searches the global optimal point in the complex search space through the competition and collaboration of the particles; however, PSO is easy to get trapped in local extremum, to have premature convergence or stagnation. In order to help PSO strike a balance between individual diversity and swarm convergence, this paper proposes an artificial immune PSO based on clonal selection. It integrates the advantages of artificial immunity and PSO and introduces the idea of immunity in PSO, namely to add immune operator in PSO so as to make PSO a new algorithm with the function of immunity.
Date : 2025-12-20 Size : 245kb User : Dallaki
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