Optimization Algorithm Based on Artificial Life Algorithm and Particle Swarm Optimization

Abstract

An optimization algorithm based on artificial life algorithm and particle swarm optimization (PSO) is proposed. The optimization includes two stages. In the first stage, artificial life system is created. As artificial life organisms have a sensing system, they can find the resource they want and metabolize it. The micro-interaction with each other in the artificial life's group results in emergent colonization in the whole system. The emergent colonization of artificial life system is used to provide an excellent initial position for PSO, and PSO is further used to find the optimal solution in the second stage. Compared with ordinary artificial life based optimization approach and ordinary PSO approach by simulation, in the newly designed method, the performances of searching speed and optimization solution accuracy are improved.