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Whale Optimization Algorithm: Theory, Literature Review, and Application in Designing Photonic Crystal Filters

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Nature-Inspired Optimizers

Part of the book series: Studies in Computational Intelligence ((SCI,volume 811))

Abstract

This chapter presents and analyzes the Whale Optimization Algorithm. The inspiration of this algorithm is first discussed in details, which is the bubble-net foraging behaviour of humpback whales in nature. The mathematical models of this algorithm is then discussed. Due to the large number of applications, a brief literature review of WOA is provided including recent works on the algorithms itself and its applications. The chapter also tests the performance of WOA on several test functions and a real case study in the field of photonic crystal filter. The qualitative and quantitative results show that merits of this algorithm for solving a wide range of challenging problems.

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Authors and Affiliations

  1. School of Electrical Engineering and Computing, University of Newcastle, Callaghan, NSW, 2308, Australia

    Seyedehzahra Mirjalili

  2. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada

    Seyed Mohammad Mirjalili

  3. Institute for Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD, 4111, Australia

    Shahrzad Saremi & Seyedali Mirjalili

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  1. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Seyedali Mirjalili

  2. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Jin Song Dong

  3. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Andrew Lewis

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Mirjalili, S., Mirjalili, S.M., Saremi, S., Mirjalili, S. (2020). Whale Optimization Algorithm: Theory, Literature Review, and Application in Designing Photonic Crystal Filters. In: Mirjalili, S., Song Dong, J., Lewis, A. (eds) Nature-Inspired Optimizers. Studies in Computational Intelligence, vol 811. Springer, Cham. https://doi.org/10.1007/978-3-030-12127-3_13

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