Chaotic co-evolutionary algorithm based on differential evolution and particle swarm optimization

A chaotic co-evolutionary algorithm based on differential evolution and particle swarm optimization (CCDEPSO) is proposed. CCDEPSO is a two-population co-evolutionary algorithm, in which the individuals of one population are evolved according to differential evolution algorithm (DE) and the other individuals are evolved according to particle swarm optimization algorithm (PSO). In order to realize co-evolving of the two sub-populations, an information sharing scheme by sharing the fitness value and the corresponding position value of the two sub-populations is introduced. Furthermore, in order to improve the searching speed and avoid the results trapping in local optima prematurely, chaotic initialization and chaotic perturbation based on Tent map are introduced in CCDEPSO. The comparative testing experiments are performed by means of CCDEPSO, DE, CPSO and PSO algorithms on six benchmark functions. Experimental results demonstrate that the global optimization ability of CCDEPSO is better than other algorithms above mentioned.