科研之友
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摘要
Inspired by the phenomenon of parasitic immune behavior in natural ecosystem, a novel Particle Swarm Optimization algorithm based on Parasitic Immune (PSOPI) is proposed for conventional Particle Swarm Optimization algorithms (PSO) often trapped in the local optima. In this paper, in order to improve the searching ability of the algorithm, we divided the particle swarm into two populations called as parasitic and host. A certain number of particles of two populations are exchanged according to the fitness values of each population after which is sorted in a given iterations. The particles are obtained by the parasite population with good fitness from the host population which is used to simulate the behavior of the parasite population got nourishments. As a result, the host population was harmed. Elitist learning strategy is applied to the particle of parasite population to avoid the elite particles trapped in the local optimum. Exploration strategy and the clonal selection of immune system were introduced into host population to expand the search space of solutions and inhibit the premature stagnation. Finally, the experimental results of a set of benchmark functions demonstrate the efficacy of the present algorithm.
