An improved HPSO-GSA with adaptive evolution stagnation cycle

Particle swarm optimization (PSO) is a relatively new optimization algorithm that has been applied to a variety of problems. However, it may easily get trapped into local optima when solving complex multimodal problems. To address this concerning issue, a novel approach, namely hybrid particle swarm optimization and gravitational search algorithm (GSA) method by introducing GSA into PSO (HPSO-GSA), is proposed in this paper for global numerical optimization. The proposed algorithm incorporates both the different concepts from PSO and GSA, updating particle positions offered by both PSO algorithm and GSA tool. The hybrid approach makes full use of the fast convergence capability of PSO and the exploitation ability of GSA. To efficiently decrease the computational cost in the hybrid algorithm, GSA is introduced when adaptive evolution stagnation cycle is met. HPSO-GSA is tested on a commonly used set of benchmark functions and is compared to other algorithms presented in the literature. Experimental results show that HPSO-GSA obtains better performance on the tested functions.