A Particle Swarm Optimization Based on Immune Mechanism

Particle swarm optimization has poor diversity, slow convergence speed and is easy to trap into local optimum during the course of searching. Clonal selection mechanism and idiotypic immune network theory exhibited in biological immune system are introduced into particle swarm optimization algorithm, and a particle swarm optimization algorithm based on immune mechanism is proposed. The new algorithm has both the properties of the original particle swarm optimization algorithm and the immune mechanism, and can improve the abilities of seeking the global optimum and evolution speed. The simulation results show that the proposed approach has preferable global convergent ability and can avoid premature convergence problem effectively.

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