Journal
MATHEMATICS
Volume 10, Issue 10, Pages -Publisher
MDPI
DOI: 10.3390/math10101620
Keywords
predominant cognitive learning; multimodal problems; particle swarm optimization; global numerical optimization; black-box optimization
Categories
Funding
- National Natural Science Foundation of China [62006124, U20B2061]
- Natural Science Foundation of Jiangsu Province [BK20200811]
- Natural Science Foundation of the Jiangsu Higher Education Institutions of China [20KJB520006]
- National Research Foundation of Korea [NRF-2021H1D3A2A01082705]
- Startup Foundation for Introducing Talent of NUIST
- National Research Foundation of Korea [2021H1D3A2A01082705] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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This paper proposes a new method called Predominant Cognitive Learning Particle Swarm Optimization (PCLPSO) to effectively tackle complex optimization problems. By guiding each particle with the better personal experience of others, the learning effectiveness and diversity of particles are improved. To address the issue of sensitivity to parameters, dynamic adjustment strategies are introduced to promote learning diversity. Experimental results demonstrate that PCLPSO achieves competitive and promising performance compared to representative state-of-the-art methods.
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.
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