Performing adaptive channel equalization by novel hybrid EO-DEPSO with adaptive levy mutation

An algorithm DEPSO based on Extremal Optimization (EO) with adaptive levy mutation is proposed in this paper. The idea is achieved by combination mechanism of global and local search. Hybrid Differential Evolution Particle Swarm Optimization (DEPSO) gives accurate optimum solutions by increasing the solution space and EO is powerful local search algorithm which helps the DEPSO out of local maximum points. Novel algorithm HEO-DEPSO is developed by clubbing EO with DEPSO, making a good optimizer. Here DEPSO overcome the problem of falling in local minima often encountered in the case of optimization by the classical methods. 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. Simulated results gave clear evidences of fine and strong global search; also algorithm got rid of pre-mature convergence. This algorithm solves problem of prematurity to local optima and gain in effective computation. Novel algorithm HEO-DEPSO is applied to adaptive channel equalizer as an application. This paper works on complex channel which is randomly chosen during simulation process. Final conclusion came out and proved that the method is feasible and good at application. Results show accelerated convergence and better performance when compared with PSO.