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Salp Swarm Algorithm (SSA) for Training Feed-Forward Neural Networks

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Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 816))

Abstract

Artificial neural networks (ANNs) have shown efficient results in statistics and computer science applications. Feed-forward neural network (FNN) is the most popular and simplest neural network architecture, capable of solving nonlinearity. In this paper, feed-forward neural networks’ weight and bias figuring using a newly proposed metaheuristic Salp Swarm Algorithm (SSA) are proposed. SSA is a swarm-based metaheuristic inspired by the navigating and foraging behaviour of salp swarm. The performance is evaluated for some of the benchmarked datasets and compared with some well-known metaheuristics.

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Authors and Affiliations

  1. MNIT Jaipur, Jaipur, India

    Divya Bairathi & Dinesh Gopalani

Authors
  1. Divya Bairathi
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  2. Dinesh Gopalani
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Corresponding author

Correspondence to Divya Bairathi .

Editor information

Editors and Affiliations

  1. Department of Mathematics, South Asian University New Delhi , New Delhi, India

    Jagdish Chand Bansal

  2. Department of Mathematics, National Institute Of Technology Silchar Department of Mathematics, Silchar, Assam, India

    Kedar Nath Das

  3. Department of Mathematics and Computer Science, Faculty of Science, , Liverpool Hope University, Liverpool, UK

    Atulya Nagar

  4. Department of Mathematics, Indian Institute of Technology Roor Department of Mathematics, Roorkee, Uttarakhand, India

    Kusum Deep

  5. School of Basic Sciences, Indian Institute of Technology Bhubanesw School of Basic Sciences, Bhubaneswar, Odisha, India

    Akshay Kumar Ojha

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Bairathi, D., Gopalani, D. (2019). Salp Swarm Algorithm (SSA) for Training Feed-Forward Neural Networks. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 816. Springer, Singapore. https://doi.org/10.1007/978-981-13-1592-3_41

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