Improved Particle Swarm Optimization using Evolutionary Algorithm

Abstract

In this paper, the researchers applied the Particle Swarm Optimization (PSO) algorithm combined with the Evolutionary Algorithm (EA) and called this hybrid approach PSOEA. This approach combines the benefits of PSO with EA. Integrating the PSO with the EA's mutation, recombination, and selection processes, allows a more efficient global search and faster convergence rate to obtain the optimal solution. PSO can also escape from local optima using EA process. PSOEA is experiment with 24 benchmark functions comparing with the conventional PSO and other similar approaches. The experiment result showed that PSOEA can find solutions faster and better than compared algorithms.