Predominant Cognitive Learning Particle Swarm Optimization for Global Numerical Optimization

Qiang Yang, Yufei Jing, Xudong Gao, Dongdong Xu, Zhenyu Lu, Sang-Woon Jeon and Jun Zhang
Additional contact information
Qiang Yang: School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
Yufei Jing: School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
Xudong Gao: School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
Dongdong Xu: School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
Zhenyu Lu: School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
Sang-Woon Jeon: Department of Electrical and Electronic Engineering, Hanyang University, Ansan 15588, Korea
Jun Zhang: Department of Electrical and Electronic Engineering, Hanyang University, Ansan 15588, Korea

Mathematics, 2022, vol. 10, issue 10, 1-35

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.

Keywords: predominant cognitive learning; multimodal problems; particle swarm optimization; global numerical optimization; black-box optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/10/1620/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/10/1620/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:10:p:1620-:d:812196

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().