Virus-Evolutionary Particle Swarm Optimization Algorithm

This paper presents an improved discrete particle swarm optimization algorithm based on virus theory of evolution. Virus-evolutionary discrete particle swarm optimization algorithm is proposed to simulate co-evolution of a particle swarm of candidate solutions and a virus swarm of substring representing schemata. In the co-evolutionary process, the virus propagates partial genetic information in the particle swarm by virus infection operators which enhances the horizontal search ability of particle swarm optimization algorithm. An example of partner selection in virtual enterprise is used to verify the proposed algorithm. Test results show that this algorithm outperforms the discrete PSO algorithm put forward by Kennedy and Eberhart.

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