Particle Swarm Optimization with Lévy Flight and Adaptive Polynomial Mutation in gbest Particle
In this paper, particle swarm optimization (PSO) with levy flight is proposed. PSO is a population based global optimization algorithm has faster convergence but often gets stuck in local optima due to lack of diversity in the population. In the proposed method, levy flight is applied on a percentage of particles excluding global best particle to create diversity in population. Adaptive polynomial mutation is applied on global best (gbest) particle to get it out from the trap in local optima. The method is applied on well-known benchmark unconstrained functions and results are compares with classical PSO. Form the experimental result, it has been observed that the proposed method performs better than classical PSO.
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