Particle swarm optimization with increasing topology connectivity

• Published in: Engineering applications of artificial intelligence Vol. 27; pp. 80 - 102
• Main Authors: Lim, Wei Hong, Mat Isa, Nor Ashidi
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2014
• Subjects: Artificial intelligence; Increasing topology connectivity (ITC); Metaheuristic search (MS); Neighborhood search (NS) operator; Particle swarm optimization (PSO); Increasing topology connectivity (ITC); Metaheuristic search (MS); Neighborhood search (NS) operator; Particle swarm optimization (PSO)
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2013.09.011

In this paper, we propose a new variant of particle swarm optimization (PSO), namely PSO with increasing topology connectivity (PSO-ITC), to solve unconstrained single-objective optimization problems with continuous search space. Specifically, an ITC module is developed to achieve better control of exploration/exploitation searches by linearly increasing the particle's topology connectivity with time as well as performing the shuffling mechanism. Furthermore, we introduce a new learning framework that consists of a new velocity update mechanism and a new neighborhood search operator that aims to enhance the algorithm's searching performance. The proposed PSO-ITC is extensively evaluated across 20 benchmark functions with various features as well as two engineering design problems. Simulation results reveal that the performance of the PSO-ITC is superior to nine other PSO variants and six cutting-edge metaheuristic search algorithms. The graphical illustration of the proposed particle swarm optimization with increasing topology connectivity (PSO-ITC), consisting of the ITC module and the proposed learning framework. [Display omitted] •A PSO variant, abbreviated as PSO-ITC, is developed.•An ITC module is developed to achieve better balance of global/local searches.•A new learning framework is proposed to improve algorithm's searching performance.•PSO-ITC has prominent searching accuracy and convergence speed in optimization.•Results show that PSO-ITC outperforms other PSO variants and MS algorithms.