Hybrid Whale Optimization Algorithm and Grey Wolf Optimizer Algorithm for Optimal Coordination of Direction Overcurrent Relays

Korashy, Ahmed; Kamel, Salah; Jurado, Francisco; Youssef, Abdel‐Raheem

doi:10.1080/15325008.2019.1602687

Cited by 57 publications

(28 citation statements)

References 29 publications

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“…This section discusses the parameter settings of the various algorithms for comparison to prove the superiority of the proposed versions. The algorithms used for comparison are moth flame optimization (MFO), FA, PSO, GWO, linear–exponential flower pollination algorithm (LEFPA), hybrid whale optimization algorithm (HWGO), 52 ESWOA 53 and the original WOA. For the versions proposed, except for the corresponding improvements, the other parameters are the same as those of the basic WOA.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…Better results can be obtained by using a linearly reduced probability and a natural exponential distribution. HWGO 52 is constructed using a hybrid WOA and GWO; its parameters are the same as those stated in the original paper. In Reference 53, a novel eagle strategy was proposed with the WOA (ESWOA), which authorizes the exploration and exploitation phases through the parameter P e .…”

Section: Experiments and Discussionmentioning

confidence: 99%

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Whale optimization algorithm based on nonlinear convergence factor and chaotic inertial weight

Ding

Zhao

2020

Concurrency and Computation

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The whale optimization algorithm (WOA) is a new biological meta-heuristic algorithm based on the social hunting behaviors of humpback whales. However, it can easily fall into a local optimum when solving complex problems and exhibits slow convergence speed and poor exploration. This study proposed three improved versions of the WOA based on the concepts of chaos initialization, nonlinear convergence factor, and chaotic inertial weight to enhance its exploration abilities. These properties were employed to improve the population diversity and maintain the balance between exploration and exploitation. The performance of the best version was compared with those of moth-flame optimization, firefly algorithm, particle swarm optimization, gray wolf optimizer, flower pollination algorithm, original WOA, and two recently proposed hybrid WOA through 19 benchmark functions. Experimental results indicated that the proposed algorithms exhibit better performance in terms of complexity and convergence speed. INDEX TERMS chaotic inertial weightconvergence factorcube mappingmeta-heuristic algorithmwhale optimization algorithm (WOA) 1 INTRODUCTION Meta-heuristic algorithms have the advantages of simplicity and easy implementation. They can effectively avoid falling into a local optima entrapment. They were developed to quickly solve large-scale and complex problems to obtain satisfactory solutions. 1 These algorithms are widely used in production scheduling, 2,3 signal processing, 4 image processing, 5 and dynamic optimization. 6 The idea of meta-heuristic algorithms originated from various biological mechanisms and physical laws. As an important concept of meta-heuristic algorithms, swarm intelligence algorithms obtain optimal solutions by iteratively updating the population. Representative swarm intelligence algorithms include particle swarm optimization (PSO) 7 which simulates the predatory behaviors of bird flocks; genetic algorithm (GA), 8 which simulate the natural evolution of Darwin's biological evolution theory and the genetic mechanism of biological evolution; ant colony optimization (ACO) algorithm, 9 which simulates ant foraging behaviors; artificial bee colony, 10 which simulates the bee collecting behavior; firefly algorithm (FA), 11 which simulates the glowing behavior of fireflies; and the bat algorithm, 12 which simulates the bat hunting behavior via ultrasound. The whale optimization algorithm (WOA) 13 is a new swarm intelligence algorithm proposed by an Australian scholar Mirjalili, inspired by the social behavior of humpback whales. The WOA determines the global optimum by encircling, searching, and attacking the preys. It has a simple principle, few parameter settings, and strong optimization ability; moreover, it successfully solved 29 mathematical and six structural optimization problems. 13 This algorithm has been successfully applied to tasks such as image segmentation, 14 optimized location, 15 and neural network training. 16 However, like other meta-heuristic optimization algorithms, the traditional WOA...

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Section: Experiments and Discussionmentioning

confidence: 99%

Section: Experiments and Discussionmentioning

confidence: 99%

Whale optimization algorithm based on nonlinear convergence factor and chaotic inertial weight

Ding

Zhao

2020

Concurrency and Computation

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“…To improve the exploration, convergence speed, and local minimum avoidance, several variants of WOA were introduced [46], [52]- [56]. The Levy-Flight trajectory [52] is one of the popular techniques used to improve the performance of meta-heuristics algorithms such as in Firefly [57], Krill Herd [58], Grey Wolf Optimization [25], [59], [60], Butterfly [61], and Harris Hawks Optimization [62]. In our work, WOA is evolved as EWOA in evolution way with enhanced searchability based on two ideas: 1) Using Levy-Flight trajectory to escape the local optima and accelerate the convergence speed, 2) Using Crossover operator to create more potential random offspring candidates in an evolution way than in [44].…”

Section: A Evolution Whale Optimization Algorithm (Ewoa)mentioning

confidence: 99%

Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem

et al. 2020

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The works presented in this paper addresses the robust population-based global optimization that is influenced by the simplicity and efficiency principles introduced in two new generation optimization algorithms. Galactic Swarm Optimization is inspired by the motion of stars, galaxies, and superclusters of galaxies under the influence of gravity. It acts well as a global controller of the whole optimization process by employing multiple flexible cycles of exploration and exploitation phases to find new, better solutions. However, the optimization process still suffers poverty in the exploitation phase, which is improved in this work by its hybridization with our evolution version of the Whale Optimization Algorithm. Concretely, the exploitation phase of Galactic Swarm Optimization is replaced by our Evolution Whale Optimization Algorithm to avoid early convergence. The Whale Optimization Algorithm mimics the unusual social behaviors and the hunting activities of humpback whales. However, it is not optimized for global optimization when the number of dimensions is increased. Hence its is evolved in our works by Levy-Flight trajectory for faster local search with adaptive step lengths and two-point crossover operator to reduce bias in the offspring creation procedure. The achieved results through extensive and careful experiments showed that our hybridization and evolution enhancements bring outstanding performance in terms of accuracy, convergence speed, and stability.

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“…Este trabalho realiza a comparação do desempenho entre as meta-heurísticas: Whale Optimization Algorithm (WOA) (Mirjalili and Lewis, 2016); Grey Wolf Optimizer (GWO) (Mirjalili et al, 2014); e Hybrid Whale Optimization Algorithm and Grey Wolf Optimizer Algorithm (HWGO) (Korashy et al, 2019). Sendo que HWGO consiste em um modelo híbrido que faz uso do mecanismo de hierarquia de liderança, oriundo do GWO, ao mecanismo de ataque com rede de bolhas do WOA.…”

Section: Introductionunclassified

“…O modelo híıbrido, Hybrid Whale Optimization Algorithm and Grey Wolf Optimizer Algorithm, combina os mecanismos bioinspiradas dos algoritmos em WOA e GWO com o intuito de melhorar o processo evolutivo. A referenciaKorashy et al (2019) utilizou o mecanismo de hierarquia de liderança, oriundo da meta-heurística GWO, associado ao mecanismo de ataque com rede de bolhas, pertencente ao WOA, para solucionar o problema de coordenação de relés de sobrecorrente direcionais, onde se obteve melhores resultados quando comparado com o WOA, GWO e outras técnicas de otimização bioinspiradas.…”

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Planejamento da Transmissão via Meta-Heurística Híbrida

Nepomuceno

Paula

Oliveira

2020

Anais Do Simpósio Brasileiro De Sistemas Elétricos 2020

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This work presents a hybrid method based on the Gray Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA) metaheuristics where a discrete variable treatment is proposed to solve the transmission line expansion planning problem. Active transmission power losses and the deterministic N-1 reliability criterion are included in the problem. The Garver and Southern Brazilian equivalent systems are used to show the efficiency of the proposed methodology. Resumo: Este trabalho apresenta um método hibrido baseado nas meta-heurísticas Grey Wolf Optimizer (GWO) e Whale Optimization Algorithm (WOA) onde um tratamento para variáveis discretaé proposto para resolver o problema de planejamento da expansão de linha de transmissão. As perdas de potência ativa e o critério de confiabilidade determinístico N-1 são incluídos no problema. Os sitemas Garver e equivalente Sul do Brasil são utilizados para mostrar a eficiência da metodologia proposta.

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Hybrid Whale Optimization Algorithm and Grey Wolf Optimizer Algorithm for Optimal Coordination of Direction Overcurrent Relays

Cited by 57 publications

References 29 publications

Whale optimization algorithm based on nonlinear convergence factor and chaotic inertial weight

Whale optimization algorithm based on nonlinear convergence factor and chaotic inertial weight

Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem

Planejamento da Transmissão via Meta-Heurística Híbrida

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