Comparative research on genetic algorithm, particle swarm optimization and hybrid GA-PSO

Genetic algorithm (GA) has been proved to be efficient for optimization problems. It contains four operators including coding, selection, crossover and mutation. It is based on `survival of the fittest' theory of Charles Darwin. Due to some drawbacks, it cannot be applied on all optimization problems. Several experiments have been done to improve the quality of GA. In this paper, a hybrid form of GA is presented with particle swarm optimization algorithm which is an iteration based algorithm. This hybrid algorithm has been tested on 5 global optimization test functions (beale, booth, matyas, levy, schaffer,). The simulation results shows that hybrid GA performs better than simple GA. This is by far the first paper in which a comparison table among GA, PSO and hybrid GA-PSO is presented and the testing is performed on 5 global optimization functions.