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A multi-strategy enhanced salp swarm algorithm for global optimization

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Abstract

As a typical nature-inspired swarm intelligence algorithm, because of the simple framework and good optimization performance, salp swarm algorithm (SSA) has been extensively applied to a lot of practical problems. Nevertheless, when facing a number of complicated optimization problems, particularly the high dimensionality and multi-dimensional problems, SSA will come to stagnation and decrease the optimal performance. To tackle this problem, this paper presents an enhanced SSA (ESSA) in which several strategies, including orthogonal learning, quadratic interpolation, and generalized oppositional learning are embedded to boost the global exploration and local exploitation performance of SSA. Orthogonal learning can help the worse salp break away from local optima, while quadratic interpolation is utilized to improve the accuracy of the global optimal through local search near the globally optimal solution. Also, generalized oppositional learning is used to improve the population quality through the initialization step and generation jumping. These strategies work together to assist SSA in promoting convergence performance. At the last CEC2017 benchmark suite and CEC2011, a real-world optimization benchmark is employed to estimate the property of ESSA in dealing with the high dimensionality and multi-dimensional problems. Three constrained engineering optimization problems are also used to assess the capability of ESSA in tackling practical engineering application problems. The experimental results and responding analysis make clear that the presented algorithm significantly outperforms the original SSA and other state-of-the-art methods.

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Acknowledgements

This research is supported by National Natural Science Foundation of China (U1809209), Medical and Health Technology Projects of Zhejiang province (2019RC207), The Ministry of Education of Humanities and Social Science Project of Wenzhou Business College (20YJA790090).

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Author notes

  1. Fangjun Kuang, Huiling Chen, and Yuping Li contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China

    Hongliang Zhang, Zhennao Cai & Huiling Chen

  2. Shanghai Lixin University of Accounting and Finance, Shanghai, 201209, China

    Xiaojia Ye

  3. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Mingjing Wang

  4. School of Information Engineering, Wenzhou Business College, Wenzhou, 325035, China

    Fangjun Kuang

  5. Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China

    Chengye Li & Yuping Li

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  1. Hongliang Zhang
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Zhang, H., Cai, Z., Ye, X. et al. A multi-strategy enhanced salp swarm algorithm for global optimization. Engineering with Computers 38, 1177–1203 (2022). https://doi.org/10.1007/s00366-020-01099-4

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