Modified Binary Grey Wolf Optimizer

Rebello, Gustavo; Oliveira, Edimar J. de

doi:10.1007/978-981-15-2133-1_7

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…In (Rebello et al, 2020), Rebello et al proposed the use of a sigmoid transfer function to convert the optimization variables into binary values. They also incorporated a simple modification at the local search component of the algorithm to best suit its application to escape local minima.…”

Section: Related Work On Binarization Of Gwomentioning

confidence: 99%

On the binarization of Grey Wolf optimizer: a novel binary optimizer algorithm

Roayaei

2021

Soft Comput

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Grey Wolf Optimizer (GWO) is a populationbased evolutionary algorithm inspired by the hunting behaviour of grey wolves. GWO, in its basic form, is a real coded algorithm, therefore, it needs modifications to deal with binary optimization problems. In this paper, we review previous works on binarization of GWO, and classify them with respect to their encoding scheme, updating strategy, and transfer function. Then, we propose a novel binary GWO algorithm (named Set-GWO), which is based on set encoding and uses set operations in its updating strategy. Experimental results on different real-world combinatorial optimization problems and different datasets, show that SetGWO outperforms other existing binary GWO algorithms in terms of quality of solutions, running time, and scalability.

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Section: Related Work On Binarization Of Gwomentioning

confidence: 99%

On the binarization of Grey Wolf optimizer: a novel binary optimizer algorithm

Roayaei

2021

Soft Comput

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On the Binarization of Grey Wolf Optimizer‎: ‎A Novel Binary Optimizer Algorithm

Roayaei

2021

Preprint

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‎Grey Wolf Optimizer (GWO) is a population-based evolutionary algorithm inspired by the hunting behaviour of grey wolves‎. ‎GWO‎, ‎in its basic form‎, ‎is a real coded algorithm‎, ‎therefore‎, ‎it needs modifications to deal with binary optimization problems‎. ‎In this paper‎, ‎we review previous works on binarization of GWO‎, ‎and classify them with respect to their encoding scheme‎, ‎updating strategy‎, ‎and transfer function‎. ‎Then‎, ‎we propose a novel binary GWO algorithm (named SetGWO)‎, ‎which is based on set encoding and uses set operations in its updating strategy‎. ‎Experimental results on different real-world combinatorial optimization problems and different datasets‎, ‎show that SetGWO outperforms other existing binary GWO algorithms in terms of quality of solutions‎, ‎running time‎, ‎and scalability‎.

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T-Growth Stochastic Model: Simulation and Inference via Metaheuristic Algorithms

2021

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The main objective of this work is to introduce a stochastic model associated with the one described by the T-growth curve, which is in turn a modification of the logistic curve. By conveniently reformulating the T curve, it may be obtained as a solution to a linear differential equation. This greatly simplifies the mathematical treatment of the model and allows a diffusion process to be defined, which is derived from the non-homogeneous lognormal diffusion process, whose mean function is a T curve. This allows the phenomenon under study to be viewed in a dynamic way. In these pages, the distribution of the process is obtained, as are its main characteristics. The maximum likelihood estimation procedure is carried out by optimization via metaheuristic algorithms. Thanks to an exhaustive study of the curve, a strategy is obtained to bound the parametric space, which is a requirement for the application of various swarm-based metaheuristic algorithms. A simulation study is presented to show the validity of the bounding procedure and an example based on real data is provided.

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Modified Binary Grey Wolf Optimizer

Cited by 3 publications

References 22 publications

On the binarization of Grey Wolf optimizer: a novel binary optimizer algorithm

On the binarization of Grey Wolf optimizer: a novel binary optimizer algorithm

On the Binarization of Grey Wolf Optimizer‎: ‎A Novel Binary Optimizer Algorithm

T-Growth Stochastic Model: Simulation and Inference via Metaheuristic Algorithms

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