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A novel lifetime scheme for enhancing the convergence performance of salp swarm algorithm

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Abstract

The performance of any meta-heuristic algorithm depends highly on the setting of dependent parameters of the algorithm. Different parameter settings for an algorithm may lead to different outcomes. An optimal parameter setting should support the algorithm to achieve a convincing level of performance or optimality in solving a range of optimization problems. This paper presents a novel enhancement method for the salp swarm algorithm (SSA), referred to as enhanced SSA (ESSA). In this ESSA, the following enhancements are proposed: First, a new position updating process was proposed. Second, a new dominant parameter different from that used in SSA was presented in ESSA. Third, a novel lifetime convergence method for tuning the dominant parameter of ESSA using ESSA itself was presented to enhance the convergence performance of ESSA. These enhancements to SSA were proposed in ESSA to augment its exploration and exploitation capabilities to achieve optimal global solutions, in which the dominant parameter of ESSA is updated iteratively through the evolutionary process of ESSA so that the positions of the search agents of ESSA are updated accordingly. These improvements on SSA through ESSA support it to avoid premature convergence and efficiently find the global optimum solution for many real-world optimization problems. The efficiency of ESSA was verified by testing it on several basic benchmark test functions. A comparative performance analysis between ESSA and other meta-heuristic algorithms was performed. Statistical test methods have evidenced the significance of the results obtained by ESSA. The efficacy of ESSA in solving real-world problems and applications is also demonstrated with five well-known engineering design problems and two real industrial problems. The comparative results show that ESSA imparts better performance and convergence than SSA and other meta-heuristic algorithms.

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Authors and Affiliations

  1. Department of Computer Science, Al-Balqa Applied University, Salt, Jordan

    Malik Braik

  2. Computer Science Department, Southern Connecticut State University, 501 Crescent St., New Haven, CT, 06515, USA

    Alaa Sheta

  3. Department of Information Technology, Taif University, Taif, Saudi Arabia

    Hamza Turabieh

  4. Department of Computer Information System, Al-Balqa Applied University, Salt, Jordan

    Heba Alhiary

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  1. Malik Braik
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  2. Alaa Sheta
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Correspondence to Alaa Sheta.

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Appendix A: Objective test functions

Appendix A: Objective test functions

A detailed description of the unimodal benchmark test functions (\(F_1\)–\(F_7\)), multimodal benchmark test functions (\(F_8\)–\(F_{13}\)) and fixed-dimension multimodal benchmark test functions (\(F_{14}\)–\(F_{23}\)) is given in Table 14.

Table 14 Characteristics of benchmark functions
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Braik, M., Sheta, A., Turabieh, H. et al. A novel lifetime scheme for enhancing the convergence performance of salp swarm algorithm. Soft Comput 25, 181–206 (2021). https://doi.org/10.1007/s00500-020-05130-0

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