Adaptive division of labor particle swarm optimization

Although evident progress and considerable achievements have been attained in developing a new particle swarm optimization (PSO) algorithm, successfully balancing the exploration and exploitation capabilities of PSO to determine high-quality solutions for complex optimization problems remains a fundamental challenge. In this study, we propose a new PSO variant, namely, adaptive division of labor (ADOL) PSO (ADOLPSO), to overcome the demerits of our previous work. Specifically, an ADOL module is developed in ADOLPSO to adaptively regulate the exploration and exploitation searches of swarm. To achieve this purpose, both criteria of swarm diversity and fitness are considered during the task allocation process of the ADOLPSO current swarm. Two new operators, namely, convex operator and reflectance operator, are adopted to generate new particles from the memory swarm of ADOLPSO to further enhance the searching accuracy and convergence speed of the proposed algorithm. These two operators are activated to evolve the memory swarm only if a fitness improvement is observed in the current swarm of ADOLPSO to prevent excessive computational complexity. The proposed ADOLPSO is applied to solve 18 benchmark functions with various characteristics. Simulation results of ADOLPSO are compared with those of other nine well-established PSO variants. Experimental findings reveal that ADOLPSO significantly outperforms the other PSO variants in terms of searching accuracy, reliability, and convergence speed. 2015 Elsevier Ltd. All rights reserved.