Ensemble particle swarm optimizer

• Published in: Applied soft computing Vol. 55; pp. 533 - 548
• Main Authors: Lynn, Nandar, Suganthan, Ponnuthurai Nagaratnam
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2017
• Subjects: Ensemble; Particle swarm optimization; Real-parameter optimization; Self-adaptive; Strategy adaptation; Self-adaptive; Real-parameter optimization; Particle swarm optimization; Strategy adaptation; Ensemble
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2017.02.007

[Display omitted] •Ensemble of particle swarm optimization algorithms with self-adaptive mechanism called EPSO is proposed in this paper.•In EPSO, the population is divided into small and large subpopulations to enhance population diversity.•In small subpopulation, comprehensive learning PSO (CLPSO) is used to preserve the population diversity.•In large subpopulation, inertia weight PSO, CLPSO, FDR-PSO, HPSO-TVAC and LIPS are hybridized together as an ensemble approach.•Self-adaptive mechanism is employed to identify the best algorithm by learning from their previous experiences so that best-performing algorithm is assigned to individuals in the large subpopulation. According to the “No Free Lunch (NFL)” theorem, there is no single optimization algorithm to solve every problem effectively and efficiently. Different algorithms possess capabilities for solving different types of optimization problems. It is difficult to predict the best algorithm for every optimization problem. However, the ensemble of different optimization algorithms could be a potential solution and more efficient than using one single algorithm for solving complex problems. Inspired by this, we propose an ensemble of different particle swarm optimization algorithms called the ensemble particle swarm optimizer (EPSO) to solve real-parameter optimization problems. In each generation, a self-adaptive scheme is employed to identify the top algorithms by learning from their previous experiences in generating promising solutions. Consequently, the best-performing algorithm can be determined adaptively for each generation and assigned to individuals in the population. The performance of the proposed ensemble particle swarm optimization algorithm is evaluated using the CEC2005 real-parameter optimization benchmark problems and compared with each individual algorithm and other state-of-the-art optimization algorithms to show the superiority of the proposed ensemble particle swarm optimization (EPSO) algorithm.