Journal of Bionic Engineering ›› 2023, Vol. 20 ›› Issue (3): 1296-1332.doi: 10.1007/s42235-022-00304-y

• • 上一篇    下一篇

A Boosted Communicational Salp Swarm Algorithm: Performance Optimization and Comprehensive Analysis

Chao Lin1; Pengjun Wang2; Ali Asghar Heidari1; Xuehua Zhao3; Huiling Chen1   

  1. 1 College of Computer Science and Artifcial Intelligence, Wenzhou University, Wenzhou 325035, Zhejiang, China  2 College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325035, China  3 School of Digital Media, Shenzhen Institute of Information Technology, Shenzhen 518172, China
  • 出版日期:2023-05-10 发布日期:2023-05-10
  • 通讯作者: Pengjun Wang; Huiling Chen;Chao Lin;Ali Asghar Heidari;Xuehua Zhao E-mail:wangpengjun@wzu.edu.cn;chenhuiling.jlu@gmail.com;clin_zd@163.com;aliasghar68@gmaill.com;zhaoxh@sziit.edu.cn
  • 作者简介:Chao Lin1; Pengjun Wang2; Ali Asghar Heidari1; Xuehua Zhao3; Huiling Chen1

A Boosted Communicational Salp Swarm Algorithm: Performance Optimization and Comprehensive Analysis

Chao Lin1; Pengjun Wang2; Ali Asghar Heidari1; Xuehua Zhao3; Huiling Chen1   

  1. 1 College of Computer Science and Artifcial Intelligence, Wenzhou University, Wenzhou 325035, Zhejiang, China  2 College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325035, China  3 School of Digital Media, Shenzhen Institute of Information Technology, Shenzhen 518172, China
  • Online:2023-05-10 Published:2023-05-10
  • Contact: Pengjun Wang; Huiling Chen;Chao Lin;Ali Asghar Heidari;Xuehua Zhao E-mail:wangpengjun@wzu.edu.cn;chenhuiling.jlu@gmail.com;clin_zd@163.com;aliasghar68@gmaill.com;zhaoxh@sziit.edu.cn
  • About author:Chao Lin1; Pengjun Wang2; Ali Asghar Heidari1; Xuehua Zhao3; Huiling Chen1

摘要: The Salp Swarm Algorithm (SSA) is a recently proposed swarm intelligence algorithm inspired by salps, a marine creature similar to jellyfish. Despite its simple structure and solid exploratory ability, SSA suffers from low convergence accuracy and slow convergence speed when dealing with some complex problems. Therefore, this paper proposes an improved algorithm based on SSA and adds three improvements. First, the Real-time Update Mechanism (RUM) underwrites the role of ensuring that excellent individual information will not be lost and information exchange will not lag in the iterative process. Second, the Communication Strategy (CMS), on the other hand, uses the multiplicative relationship of multiple individuals to regulate the exploration and exploitation process dynamically. Third, the Selective Replacement Strategy (SRS) is designed to adaptively adjust the variance ratio of individuals to enhance the accuracy and depth of convergence. The new proposal presented in this study is named RCSSSA. The global optimization capability of the algorithm was tested against various high-performance and novel algorithms at IEEE CEC 2014, and its constrained optimization capability was tested at IEEE CEC 2011. The experimental results demonstrate that the proposed algorithm can converge faster while obtaining better optimization results than traditional swarm intelligence and other improved algorithms. The statistical data in the table support its optimization capabilities, and multiple graphs deepen the understanding and analysis of the proposed algorithm.

关键词: Salp swarm algorithm , · Swarm intelligence , · Global optimization , · Exploration , · Exploitation

Abstract: The Salp Swarm Algorithm (SSA) is a recently proposed swarm intelligence algorithm inspired by salps, a marine creature similar to jellyfish. Despite its simple structure and solid exploratory ability, SSA suffers from low convergence accuracy and slow convergence speed when dealing with some complex problems. Therefore, this paper proposes an improved algorithm based on SSA and adds three improvements. First, the Real-time Update Mechanism (RUM) underwrites the role of ensuring that excellent individual information will not be lost and information exchange will not lag in the iterative process. Second, the Communication Strategy (CMS), on the other hand, uses the multiplicative relationship of multiple individuals to regulate the exploration and exploitation process dynamically. Third, the Selective Replacement Strategy (SRS) is designed to adaptively adjust the variance ratio of individuals to enhance the accuracy and depth of convergence. The new proposal presented in this study is named RCSSSA. The global optimization capability of the algorithm was tested against various high-performance and novel algorithms at IEEE CEC 2014, and its constrained optimization capability was tested at IEEE CEC 2011. The experimental results demonstrate that the proposed algorithm can converge faster while obtaining better optimization results than traditional swarm intelligence and other improved algorithms. The statistical data in the table support its optimization capabilities, and multiple graphs deepen the understanding and analysis of the proposed algorithm.

Key words: Salp swarm algorithm , · Swarm intelligence , · Global optimization , · Exploration , · Exploitation