Breeding swarms: a ga/pso hybrid

Abstract. In this paper we propose a novel hybrid (GA/PSO) algorithm, Breeding Swarm, combining the strengths of particle swarm optimization with genetic algorithms. The hybrid algorithm combines the standard velocity and update rules of PSOs with the ideas of selection, crossover and mutation from GAs. We propose a new crossover operator (VPAC), incorporating the PSO velocity vector, which actively disperses the population preventing premature convergence. We compare the hybrid algorithm to both the standard GA and PSO models in evolving solutions to four standard function minimization problems. Results show the algorithm to be highly competitive, often outperforming both the GA and PSO.