CHPSO: A new collaborative hybrid particle swarm optimization algorithm

Particle Swarm optimization (PSO) is a powerful optimization tool which is widely used to solve a wide range of real-life optimization problems. Some of the widely used PSO variants, including CF-PSO and AW-PSO, cannot guarantee to achieve globally optimal solutions during the period of stagnation when particle velocity variations decline considerably, leading to early convergence. In order to address this problem, this paper proposes an improved PSO algorithm, 'Collaborative Hybrid PSO (CHPSO)'. In the proposed algorithm, the initial swarm is divided into two sub-swarms, one following the Constriction Factor approach and other following Adaptive Weight approach. When the velocity of any of these two sub-swarms goes below the threshold, an information exchange mechanism is utilized and mutation is performed to improve the quality of the solutions. The proposed method is implemented on Matlab and evaluated using five well studied benchmark test functions. Results obtained in this analysis show that the proposed method finds better solutions compared with Constriction Factor PSO (CF-PSO) and Adaptive Weight PSO (AW-PSO), when they work individually. Statistical significance test also shows the robustness of the proposed method compared with CF-PSO and AW-PSO