Velocity clamping-assisted adaptive salp swarm algorithm: balance analysis and case studies

Hongwei Ding; Xingguo Cao; Zongshan Wang; Gaurav Dhiman; Peng Hou; Jie Wang; Aishan Li; Xiang Hu; Hongwei Ding; Xingguo Cao; Zongshan Wang; Gaurav Dhiman; Peng Hou; Jie Wang; Aishan Li; Xiang Hu

doi:10.3934/mbe.2022364

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 8: 7756-7804. doi: 10.3934/mbe.2022364

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Research article Special Issues

Velocity clamping-assisted adaptive salp swarm algorithm: balance analysis and case studies

1.
School of Information Science and Engineering, Yunnan University, Kunming 650500, China
2.
University Key Laboratory of Internet of Things Technology and Application, Yunnan Province, Kunming 650500, China
3.
Department of Computer Science, Government Bikram College of Commerce, Patiala, India
4.
University Centre for Research and Development, Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali, India
5.
Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India
6.
School of Computer Science, Fudan University, Shanghai 200433, China
7.
School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450000, China
8.
Rackham Graduate School, University of Michigan, Ann Arbor, USA
9.
College of Information, Shanghai Ocean University, Shanghai, China

Academic Editor: Runzhang Xu

Received: 12 April 2022 Revised: 28 April 2022 Accepted: 16 May 2022 Published: 25 May 2022

Salp swarm algorithm (SSA) is a recently proposed, powerful swarm-intelligence based optimizer, which is inspired by the unique foraging style of salps in oceans. However, the original SSA suffers from some limitations including immature balance between exploitation and exploration operators, slow convergence and local optimal stagnation. To alleviate these deficiencies, a modified SSA (called VC-SSA) with velocity clamping strategy, reduction factor tactic, and adaptive weight mechanism is developed. Firstly, a novel velocity clamping mechanism is designed to boost the exploitation ability and the solution accuracy. Next, a reduction factor is arranged to bolster the exploration capability and accelerate the convergence speed. Finally, a novel position update equation is designed by injecting an inertia weight to catch a better balance between local and global search. 23 classical benchmark test problems, 30 complex optimization tasks from CEC 2017, and five engineering design problems are employed to authenticate the effectiveness of the developed VC-SSA. The experimental results of VC-SSA are compared with a series of cutting-edge metaheuristics. The comparisons reveal that VC-SSA provides better performance against the canonical SSA, SSA variants, and other well-established metaheuristic paradigms. In addition, VC-SSA is utilized to handle a mobile robot path planning task. The results show that VC-SSA can provide the best results compared to the competitors and it can serve as an auxiliary tool for mobile robot path planning.
- salp swarm algorithm,
- swarm intelligence,
- numerical optimization,
- engineering design optimization,
- robot path planning
Citation: Hongwei Ding, Xingguo Cao, Zongshan Wang, Gaurav Dhiman, Peng Hou, Jie Wang, Aishan Li, Xiang Hu. Velocity clamping-assisted adaptive salp swarm algorithm: balance analysis and case studies[J]. Mathematical Biosciences and Engineering, 2022, 19(8): 7756-7804. doi: 10.3934/mbe.2022364

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Abstract

Salp swarm algorithm (SSA) is a recently proposed, powerful swarm-intelligence based optimizer, which is inspired by the unique foraging style of salps in oceans. However, the original SSA suffers from some limitations including immature balance between exploitation and exploration operators, slow convergence and local optimal stagnation. To alleviate these deficiencies, a modified SSA (called VC-SSA) with velocity clamping strategy, reduction factor tactic, and adaptive weight mechanism is developed. Firstly, a novel velocity clamping mechanism is designed to boost the exploitation ability and the solution accuracy. Next, a reduction factor is arranged to bolster the exploration capability and accelerate the convergence speed. Finally, a novel position update equation is designed by injecting an inertia weight to catch a better balance between local and global search. 23 classical benchmark test problems, 30 complex optimization tasks from CEC 2017, and five engineering design problems are employed to authenticate the effectiveness of the developed VC-SSA. The experimental results of VC-SSA are compared with a series of cutting-edge metaheuristics. The comparisons reveal that VC-SSA provides better performance against the canonical SSA, SSA variants, and other well-established metaheuristic paradigms. In addition, VC-SSA is utilized to handle a mobile robot path planning task. The results show that VC-SSA can provide the best results compared to the competitors and it can serve as an auxiliary tool for mobile robot path planning.

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