Orthogonal pinhole-imaging-based learning salp swarm algorithm with self-adaptive structure for global optimization

Zongshan Wang; Hongwei Ding; Jingjing Yang; Peng Hou; Gaurav Dhiman; Jie Wang; Zhijun Yang; Aishan Li

doi:10.3389/fbioe.2022.1018895

Orthogonal pinhole-imaging-based learning salp swarm algorithm with self-adaptive structure for global optimization

Front Bioeng Biotechnol. 2022 Dec 1:10:1018895. doi: 10.3389/fbioe.2022.1018895. eCollection 2022.

Authors

Zongshan Wang^{1

2}, Hongwei Ding^{1

2}, Jingjing Yang^{1

2}, Peng Hou³, Gaurav Dhiman^{4

5

6}, Jie Wang⁷, Zhijun Yang^{1

2}, Aishan Li⁸

Affiliations

¹ School of Information Science and Engineering, Yunnan University, Kunming, China.
² University Key Laboratory of Internet of Things Technology and Application, Kunming, China.
³ School of Computer Science, Fudan University, Shanghai, China.
⁴ Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon.
⁵ Department of Computer Science and Engineering, University Centre for Research and Development, Chandigarh University, Gharuan, India.
⁶ Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India.
⁷ School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou, China.
⁸ Rackham Graduate School, University of Michigan, Ann Arbor, MI, United States.

Abstract

Salp swarm algorithm (SSA) is a simple and effective bio-inspired algorithm that is gaining popularity in global optimization problems. In this paper, first, based on the pinhole imaging phenomenon and opposition-based learning mechanism, a new strategy called pinhole-imaging-based learning (PIBL) is proposed. Then, the PIBL strategy is combined with orthogonal experimental design (OED) to propose an OPIBL mechanism that helps the algorithm to jump out of the local optimum. Second, a novel effective adaptive conversion parameter method is designed to enhance the balance between exploration and exploitation ability. To validate the performance of OPLSSA, comparative experiments are conducted based on 23 widely used benchmark functions and 30 IEEE CEC2017 benchmark problems. Compared with some well-established algorithms, OPLSSA performs better in most of the benchmark problems.

Keywords: benchmark; equilibrium optimizer; exploration and exploitation; global optimization; metaheuristic algorithms; particle swarm optimization.