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Enhanced Salp Swarm Algorithm based on random walk and its application to training feedforward neural networks

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Abstract

Salp Swarm Algorithm (SSA) is a new type of metaheuristic and has shown superiority over other well-known algorithms such as Particle Swarm Optimization and Grey Wolf Optimizer in solving challenging optimization problems. Despite its superior performance, SSA still has problems such as insufficient convergence speed. Moreover, its local optima avoidance ability is not as good as those evolutionary algorithms using crossover operators. In this paper, we propose a modified Salp Swarm Algorithm (m-SSA) which improves the exploitation and exploration of SSA by integrating random walk strategy and especially enhances exploration by adding a new controlling parameter. In addition, a simulated annealing-type acceptance criterion is adopted to accept the fittest follower position as the new best leader position. The performance of the proposed algorithm is benchmarked on a set of classical functions and CEC2014 test suite. The proposed algorithm (m-SSA) outperforms SSA significantly on most test functions. When compared with other state-of-the-art metaheuristics, it also presents very competitive results. Besides, we apply the proposed algorithm on training feedforward neural networks (FNNs) and the results prove the effectiveness and efficiency of m-SSA.

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Authors and Affiliations

  1. School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, Guangdong, China

    Yongqiang Yin, Qiang Tu & Xuechen Chen

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  1. Yongqiang Yin
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  2. Qiang Tu
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  3. Xuechen Chen
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Correspondence to Xuechen Chen.

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Communicated by V. Loia.

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The work was supported by the National Natural Science Foundation of China (Grant No. U1530120).

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Yin, Y., Tu, Q. & Chen, X. Enhanced Salp Swarm Algorithm based on random walk and its application to training feedforward neural networks. Soft Comput 24, 14791–14807 (2020). https://doi.org/10.1007/s00500-020-04832-9

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  • DOI: https://doi.org/10.1007/s00500-020-04832-9

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