Predominant Cognitive Learning Particle Swarm Optimization for Global Numerical Optimization

Abstract

Particle swarm optimization (PSO) has witnessed giant success in problem optimization. Nevertheless, its optimization performance seriously degrades when coping with optimization problems with a lot of local optima. To alleviate this issue, this paper designs a predominant cognitive learning particle swarm optimization (PCLPSO) method to effectively tackle complicated optimization problems. Specifically, for each particle, a new promising exemplar is constructed by letting its personal best position cognitively learn from a better personal experience randomly selected from those of others based on a novel predominant cognitive learning strategy. As a result, different particles preserve different guiding exemplars. In this way, the learning effectiveness and the learning diversity of particles are expectedly improved. To eliminate the dilemma that PCLPSO is sensitive to the involved parameters, we propose dynamic adjustment strategies, so that different particles preserve different parameter settings, which is further beneficial to promote the learning diversity of particles. With the above techniques, the proposed PCLPSO could expectedly compromise the search intensification and diversification in a good way to search the complex solution space properly to achieve satisfactory performance. Comprehensive experiments are conducted on the commonly adopted CEC 2017 benchmark function set to testify the effectiveness of the devised PCLPSO. Experimental results show that PCLPSO obtains considerably competitive or even much more promising performance than several representative and state-of-the-art peer methods.

Dual Elite Groups-Guided Differential Evolution for Global Numerical Optimization

“…In the experiments, we use the CEC'2017 benchmark set, which has been widely used to test the optimization performance of evolutionary algorithms [75][76][77][78], to demonstrate the effectiveness and efficiency of DEGGDE. This set consists of 29 numerical optimization problems, which are classified into four different types.…”

An Extended Membrane System Based on Cell-like P Systems and Improved Particle Swarm Optimization for Image Segmentation

“…The studies of improved PSO are mainly related to modified PSO algorithms and hybrid PSO algorithms based on meta-heuristic approaches [37]. Modified PSO algorithms are based on the updated model of PSO and adopted some strategies and methods in the search process of particles, such as flight mechanisms of particles including levy flight [38][39][40], learning strategies for particles including cirssoss learning [41], cognitive learning [42] and comprehensive learning [40]; population topology including stochastic topology [7] and dynamic topology [43]; and optimization strategies including random walk strategy [44], chaos strategy [45] and synergistic strategy [46]; search strategies including local search [47,48] and charged system search [49,50]. Hybrid PSO algorithms are combined with some traditional and evolutionary optimization methods in order to utilized the advantages of both methods and improve the global search ability of PSO, such as simulated annealing (SA) [51], tabu search (TS) [52], BBO [53], artificial bee colony (ABC) [54], genetic algorithm (GA) [55] and differential evolution (DE) [56].…”