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Proceedings of Sixth International Conference on Soft Computing for Problem Solving pp 76–86Cite as

Review on Inertia Weight Strategies for Particle Swarm Optimization

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 547))

Abstract

In the category of swarm intelligence based algorithms, Particle Swarm Optimization (PSO) is an effective population-based metaheuristic used to solve complex optimization problems. In PSO, global optima is searched with the help of individuals. For the efficient search process, individuals have to explore whole search space as well as have to exploit the identified search area. Researchers are continuously working to balance these two contradictory properties i.e. exploration and exploitation and have been modified the PSO in many different ways to improve its solution search capability in the search space. In this regard, incorporation of inertia weight strategy in PSO is a significant modification and after that many researchers have been developed different inertia weight strategies to improve the solution search capability of PSO. This paper presents an analysis of the developed inertia weight strategies in respect to problem-solving capability and their effect in the solution search process of PSO. The effect of 30 recent inertia weight strategies on PSO is measured while comparing over ten well known test functions of having different degree of complexity and modularity.

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Authors and Affiliations

  1. Rajasthan Technical University, Kota, India

    Ankush Rathore & Harish Sharma

Authors
  1. Ankush Rathore
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  2. Harish Sharma
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Correspondence to Ankush Rathore .

Editor information

Editors and Affiliations

  1. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India

    Kusum Deep

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

    Jagdish Chand Bansal

  3. Department of Mathematics, National Institute of Technology, Silchar, Silchar, Assam, India

    Kedar Nath Das

  4. School of Mathematics, Thapar Institute of Engineering and Technology University, Patiala, Punjab, India

    Arvind Kumar Lal

  5. School of Mathematics, Thapar Institute of Engineering and Technology University, Patiala, Punjab, India

    Harish Garg

  6. Department of Mathematics and Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya K. Nagar

  7. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

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Rathore, A., Sharma, H. (2017). Review on Inertia Weight Strategies for Particle Swarm Optimization. In: Deep, K., et al. Proceedings of Sixth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-10-3325-4_9

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  • DOI: https://doi.org/10.1007/978-981-10-3325-4_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3324-7

  • Online ISBN: 978-981-10-3325-4

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