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An improved salp swarm algorithm for complex multi-modal problems

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Abstract

In this paper, improved salp swarm algorithm is proposed. The algorithm integrates (1) random opposition-based learning (2) multiple leadership and (3) simulated annealing in swarm intelligence-based metaheuristic salp swarm algorithm. This integration increases the exploration and exploitation of the original salp swarm algorithm. Hence, the effectiveness of the proposed algorithm is better for complex multi-modal problems. The algorithm is tested on several standard numerical benchmark functions and CEC-2015 benchmarks. Results are compared with some well-known metaheuristics. The results represent the merit of the proposed algorithm with respect to other algorithms. The improved salp swarm algorithm is applied for feed-forward neural network training. Performance is compared with other metaheuristic-based feedforward neural network trainers for different data sets. The results show the efficiency and effectiveness of proposed algorithm in solving complex multi-modal problems.

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  1. Malaviya National Institute of Technology Jaipur, Jaipur, India

    Divya Bairathi & Dinesh Gopalani

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  1. Divya Bairathi
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Correspondence to Divya Bairathi.

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I Divya Bairathi declare that I have no conflict of interest. I Dinesh Gopalani declare that I have no conflict of interest.

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Bairathi, D., Gopalani, D. An improved salp swarm algorithm for complex multi-modal problems. Soft Comput 25, 10441–10465 (2021). https://doi.org/10.1007/s00500-021-05757-7

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