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Differential Evolution Algorithm for Multimodal Optimization: A Short Survey

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Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1393))

Abstract

Most of the real-world problems are multimodal in nature. Several algorithms have been proposed to solve multimodal optimization problems. Classical gradient-based methods fail for optimization problems in which functions are either discontinuous or non-differentiable. Differential Evolution (DE) is simple to implement population-based heuristic method used for solving optimization problems even if the function is discontinuous or non-differentiable. It is proved to have one of the fastest rates of convergence toward the optima. The search behavior of DE algorithm is governed by its parameters. DE has won top ranks in many IEEE CEC competitions as it has outperformed its competitors in solving real parameter space optimization problems. DE and its variants have also been applied to solve various engineering optimization problems. This paper aims to cover the work done in the area of real parameter single objective multimodal optimization using differential evolution algorithm.

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Authors and Affiliations

  1. Indian Institute of Technology, Indore, India

    Shatendra Singh, Aruna Tiwari & Suchitra Agrawal

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  1. Shatendra Singh
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  2. Aruna Tiwari
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  3. Suchitra Agrawal
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Corresponding author

Correspondence to Shatendra Singh .

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Editors and Affiliations

  1. Computer Science and Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

    Aruna Tiwari

  2. Computer Science and Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

    Kapil Ahuja

  3. Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India

    Anupam Yadav

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

    Jagdish Chand Bansal

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

    Kusum Deep

  6. School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

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Singh, S., Tiwari, A., Agrawal, S. (2021). Differential Evolution Algorithm for Multimodal Optimization: A Short Survey. In: Tiwari, A., Ahuja, K., Yadav, A., Bansal, J.C., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1393. Springer, Singapore. https://doi.org/10.1007/978-981-16-2712-5_58

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