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Selection scheme sensitivity for a hybrid Salp Swarm Algorithm: analysis and applications

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Abstract

This paper proposes a hybrid version of the Salp Swarm Algorithm (SSA) and the hill climbing (HC) technique using various selection schemes to solve engineering design problems. The proposed algorithm consists of two stages. In the first stage, the basic SSA is hybridized with HC local search to improve its exploitation capabilities; we refer to the hybridized algorithm as HSSA. In the second stage, a selection scheme is applied to enhance the exploration capabilities of the hybrid SSA. Six popular selection schemes were investigated, and the proportional selection scheme was selected as it yielded the best performance. We refer to the hybridized SSA along with the proportional selection scheme as PHSSA. To validate the performance of the proposed algorithms, a series of experiments were conducted using thirty benchmark functions and four engineering design problems. The investigations using benchmark functions revealed that HSSA overcame the weaknesses of the local search in the basic SSA. Moreover, PHSSA enhanced performance by providing an appropriate balance between exploration and exploitation as well as maintaining the diversity of the solutions and avoiding premature convergence. Finally, PHSSA produced results on engineering design problems that were at least comparable and in many cases superior to SSA and similar algorithms in the literature.

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Authors and Affiliations

  1. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953, Jordan

    Laith Abualigah

  2. Computer Science Department, Aqaba University of Technology, Aqaba, 77110, Jordan

    Mohammad Shehab

  3. Division of Engineering, New York University Abu Dhabi, Saadiyat Island, Abu Dhabi, 129188, United Arab Emirates

    Ali Diabat

  4. Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, 11201, USA

    Ali Diabat

  5. Department of Computer Science, University of Pretoria, Pretoria, 0002, South Africa

    Ajith Abraham

  6. Machine Intelligence Research Labs (MIR Labs), 3rd Street NW, P.O. Box 2259, Auburn, WA, 98071, USA

    Ajith Abraham

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  1. Laith Abualigah
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Abualigah, L., Shehab, M., Diabat, A. et al. Selection scheme sensitivity for a hybrid Salp Swarm Algorithm: analysis and applications. Engineering with Computers 38, 1149–1175 (2022). https://doi.org/10.1007/s00366-020-01067-y

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