Whale Optimization Algorithm: Theory, Literature Review, and Application in Designing Photonic Crystal Filters
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 the
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Abstract This chapter presents and analyzes the Whale Optimization Algorithm. The inspiration of this algorithm is first discussed in details, which is the bubblenet 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.
1 Introduction Nature has been a major source of inspiration for researchers in the field of optimization. This has led to several nature- or bio-inspired algorithms. Such algorithms have mostly a similar framework. They start with a set of random solutions. This set is then improved using mechanisms inspired from nature. In Genetic Algorithms, for instance, each solution is considered as chromosomes in nature that is selected based S. Mirjalili School of Electrical Engineering and Computing, University of Newcastle, Callaghan, NSW 2308, Australia e-mail: [email protected] S. M. Mirjalili Department of Electrical and Computer Engineering, Concordia University, Montreal, QC H3G 1M8, Canada e-mail: [email protected] S. Saremi · S. Mirjalili (B) Institute for Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD 4111, Australia e-mail: [email protected] S. Saremi e-mail: [email protected] © Springer Nature Switzerland AG 2020 S. Mirjalili et al. (eds.), Nature-Inspired Optimizers, Studies in Computational Intelligence 811, https://doi.org/10.1007/978-3-030-12127-3_13
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on its fitness value and undergoes crossover with other chromosomes and mutation. This simulates the process of natural selection, recombination, and mutation inspired from the theory of evolution. In Particle Swarm Optimization [1], each solution is considered as a vector that will be added to a velocity vector to simulate birds’ flying method. In the Ant Colony Optimization [2], every solution is considered a path that an ant takes to reach certain goal. The method of finding the shortest path in a natural ant colony is then used to find an optimal solution for a given optimization problem. Regardless of the differences between each of nature-inspired algorithms in the literature, one of the common features is the use of stochastic components. This means that a nature-inspired algorithm fluctuates solutions in a randomized manner to find the global optimum. Of course, the use of random components should be systematic to perform better than a complete random search. One of the ways to increase the chance of finding the optimal or near optimal solutions using a nature-inspired algorithm that leads to a systematic stochastic search is to change the magnitude or rate of change
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