A Hybrid Optimization Algorithm based on Clonal Selection Principle and Particle Swarm Intelligence

This paper first discusses the background knowledge of the clonal selection algorithm and particle swarm method. The clonal selection algorithm is imitated by the basic principle of the adaptive immune response to virus stimulus. The particle swarm optimization is motivated by the social behaviors of swarms. Inspired by these two optimization methods, we propose a hybrid optimization algorithm in this paper. The steps of this hybrid optimization algorithm are described in details, and its performance is evaluated hybrid unidimensional function optimization and three multidimensional functions optimization problems. It is also compared with both the clonal selection algorithm and particle swarm method based on numerical simulations

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