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Introductory Review of Swarm Intelligence Techniques

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Advances in Swarm Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1054))

Abstract

With the rapid upliftment of technology, there has emerged a dire need to ‘fine-tune’ or ‘optimize’ certain processes, software, models or structures, with utmost accuracy and efficiency. Optimization algorithms are preferred over other methods of optimization through experimentation or simulation, for their generic problem-solving abilities and promising efficacy with the least human intervention. In recent times, the inducement of natural phenomena into algorithm design has immensely triggered the efficiency of optimization process for even complex multi-dimensional, non-continuous, non-differentiable and noisy problem search spaces. This chapter deals with the Swarm intelligence (SI) based algorithms or Swarm Optimization Algorithms, which are a subset of the greater Nature Inspired Optimization Algorithms (NIOAs). Swarm intelligence involves the collective study of individuals and their mutual interactions leading to intelligent behavior of the swarm. The chapter presents various population-based SI algorithms, their fundamental structures along with their mathematical models.

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Acknowledgements

This work is supported by the Science and Engineering Board (SERB), Department of Science and Technology (DST) of the Government of India under Grant No. EEQ/2019/000657.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, 788010, Assam, India

    Thounaojam Chinglemba, Soujanyo Biswas, Debashish Malakar, Vivek Meena, Debojyoti Sarkar & Anupam Biswas

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  1. Thounaojam Chinglemba
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Correspondence to Anupam Biswas .

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Editors and Affiliations

  1. Department of CS and Engineering, National Institute Of Technology Silchar, Cachar, Assam, India

    Anupam Biswas

  2. Department of Industrial Engineering, Pamukkale Üniversitesi Hastaneleri, Pamukkale, Turkey

    Can B. Kalayci

  3. Centre for AI Research and Optimisation, Torrens University Australia, Brisbane, QLD, Australia

    Seyedali Mirjalili

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Chinglemba, T., Biswas, S., Malakar, D., Meena, V., Sarkar, D., Biswas, A. (2023). Introductory Review of Swarm Intelligence Techniques. In: Biswas, A., Kalayci, C.B., Mirjalili, S. (eds) Advances in Swarm Intelligence. Studies in Computational Intelligence, vol 1054. Springer, Cham. https://doi.org/10.1007/978-3-031-09835-2_2

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