Grey Wolf Optimizer: Theory, Literature Review, and Application in Computational Fluid Dynamics Problems
This chapter first discusses inspirations, methematicam models, and an in-depth literature of the recently proposed Grey Wolf Optimizer (GWO). Then, several experiments are conducted to analyze and benchmark the performance of different variants and impro
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Abstract This chapter first discusses inspirations, methematicam models, and an in-depth literature of the recently proposed Grey Wolf Optimizer (GWO). Then, several experiments are conducted to analyze and benchmark the performance of different variants and improvements of this algorithm. The chapter also investigates the application of the GWO variants in finding an optimal design for a ship propeller.
1 Introduction Over the course of last decade, stochastic optimization techniques superseded conventional, deterministic approaches due to several reasons. One of the main reasons is the inefficiency of deterministic optimization or search methods for solving NP hard problems. In fact, for a lot of NP-hard problems, there is no deterministic solution S. Mirjalili (B) Institute of Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD 4111, Australia e-mail: [email protected] I. Aljarah · H. Faris King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan e-mail: [email protected] H. Faris e-mail: [email protected] M. Mafarja Department of Computer Science, Faculty of Engineering and Technology, Birzeit University, PoBox 14, Birzeit, Palestine e-mail: [email protected] A. A. Heidari School of Surveying and Geospatial Engineering, University of Tehran, Tehran, Iran 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_6
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as of toady. Another reason is the dependency of some of deterministic algorithms on calculating the derivation of the problem. For the problems that derivation is ill-defined or difficult to obtain, such methods cannot be used. On the other hand, stochastic optimization techniques are able to find near optimal solutions for NP-hard problems in a reasonable time. Also, the majority of them consider optimization problems as a black box and do not require derivation. This means that the same algorithm can be applied to different problems without the need to know the internal mathematical model or the computer program of an optimization problem. A set of popular stochastic optimization algorithms that have been very popular lately include nature-inspired techniques. Such methods mimic natural intelligence and provide nature-inspired problem solving techniques. One of the seminal algorithms in this area is the well-regarded Genetic Algorithm (GA). This algorithm has been inspired from the biological evolution and mimics the process of evolving creates in a computer. In fact, this algorithm has been equipped with selection, recombination, and mutation operators to do this. GA belongs to the family of evolutionary algorithms. Other popular evolutionary techniques are Evolution Strategy (EA) and Differential Evolution (DE). After the proposal of GA, several other classes of nature-inspired algorithms came to the existence. One of the classes
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