Title :
CHPSO-A new collaborative hybrid particle swarm optimization algorithm
Author :
Anwar, Ayesha ; Mahmood, Abdun Naser ; Islam, Rashed
Author_Institution :
Sch. of Eng. & Inf. Technol., Univ. of New South Wales Australia, Canberra, NSW, Australia
Abstract :
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.
Keywords :
algorithm theory; particle swarm optimisation; AW-PSO; CF-PSO; CHPSO; Matlab; PSO variants; adaptive weight PSO; adaptive weight approach; benchmark test functions; collaborative hybrid PSO algorithm; collaborative hybrid particle swarm optimization algorithm; constriction factor PSO; globally optimal solutions; information exchange mechanism; particle velocity variations; powerful optimization tool; real-life optimization problems; stagnation; Convergence; Educational institutions; Information exchange; Optimization; Particle swarm optimization; Standards; Vectors;
Conference_Titel :
Industrial Electronics and Applications (ICIEA), 2014 IEEE 9th Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4799-4316-6
DOI :
10.1109/ICIEA.2014.6931454