Serial configuration of genetic algorithm and particle swarm optimization to increase the convergence speed and accuracy

Abstract

Genetic algorithm and particle swarm optimization are two methods which can be used to find the global extremum of cost functions. The solely performance of each method and their specific characteristics in finding the global extremum have been giving the idea of hybridization of these two methods to many researchers. In this paper a new hybrid algorithm named Serial Genetic Algorithm and Particle Swarm Optimization (SGAPSO) is introduced and the configuration of the algorithm is discussed in details. A set of benchmark cost functions consisted of high dimensional, multimodal and low dimensional cost functions is used to compare the results of proposed method with some other known algorithms such as original genetic algorithm, stud genetic algorithm, jumping gene method, original particle swarm optimization, and classical and fast evolutionary programming. The simulation results show that by using the SGAPSO, the number of generations and cost function evaluations, as two criteria for comparison different algorithms, to reach the global minimum reduce significantly and the convergence speed and accuracy of the algorithm increase.