A noval hybrid optimizer: EO-DEPSO

PSO - particle swarm optimization and DE - differential evolution algorithms have paved there name and fame towards optimization. Both when clubbed together make a good optimizer. DEPSO, overcome the problem of falling in local minima often encountered in the case of optimization by the classical methods. This paper proposes a novel Hybrid EO-DEPSO. This hybrid includes Extremal optimization which includes process of levy mutation. Novel Hybrid works on global and local searches where global search is based on difference in group which rashly approaches towards optimal solution and local search is based on EO-adaptive mutation which helps DEPSO to get out of local maxima points which indeed helps in fine tuning and adjustment. Thus DEPSO benefits from exploitation ability and EO benefits from exploitations. This algorithm solves problem of prematurity to local optima and gain in effective computation. Results have been revealed on benchmark function showing accelerated convergence and better performance compared with PSO.