A New Hybridization Approach between the Fireworks Algorithm and Grey Wolf Optimizer Algorithm

Barraza, Juan; Rodríguez, Luis; Castillo, Oscar; Melín, Patricia; Valdez, Fevrier

doi:10.1155/2018/6495362

Cited by 24 publications

(11 citation statements)

References 25 publications

(36 reference statements)

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“…Singh et al [ 23 ] proposed an improved grey wolf optimization algorithm, which was used to solve the economic power load scheduling problem and achieved good results. Barraza et al [ 24 ] proposed a fusion algorithm based on FWA and GWO. The test of the benchmark function showed that the fusion algorithm had better performance.…”

Section: Related Workmentioning

confidence: 99%

An improved Wolf pack algorithm for optimization problems: Design and evaluation

et al. 2021

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Wolf Pack Algorithm (WPA) is a swarm intelligence algorithm that simulates the food searching process of wolves. It is widely used in various engineering optimization problems due to its global convergence and computational robustness. However, the algorithm has some weaknesses such as low convergence speed and easily falling into local optimum. To tackle the problems, we introduce an improved approach called OGL-WPA in this work, based on the employments of Opposition-based learning and Genetic algorithm with Levy’s flight. Specifically, in OGL-WPA, the population of wolves is initialized by opposition-based learning to maintain the diversity of the initial population during global search. Meanwhile, the leader wolf is selected by genetic algorithm to avoid falling into local optimum and the round-up behavior is optimized by Levy’s flight to coordinate the global exploration and local development capabilities. We present the detailed design of our algorithm and compare it with some other nature-inspired metaheuristic algorithms using various classical test functions. The experimental results show that the proposed algorithm has better global and local search capability, especially in the presence of multi-peak and high-dimensional functions.

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Section: Related Workmentioning

confidence: 99%

An improved Wolf pack algorithm for optimization problems: Design and evaluation

et al. 2021

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“…2018, [50] The aim of this hybridization was to combine the two most efficient algorithms, which have been inspired by physics and nature.…”

Section: Firework Algorithm (Fwa)mentioning

confidence: 99%

A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design

Mohammed

Rashid

2020

Neural Comput & Applic

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A recent metaheuristic algorithm, such as Whale Optimization Algorithm (WOA), was proposed. The idea of proposing this algorithm belongs to the hunting behavior of the humpback whale. However, WOA suffers from poor performance in the exploitation phase and stagnates in the local best solution. Grey Wolf Optimization (GWO) is a very competitive algorithm comparing to other common metaheuristic algorithms as it has a super performance in the exploitation phase while it is tested on unimodal benchmark functions. Therefore, the aim of this paper is to hybridize GWO with WOA to overcome the problems. GWO can perform well in exploiting optimal solutions. In this paper, a hybridized WOA with GWO which is called WOAGWO is presented. The proposed hybridized model consists of two steps. Firstly, the hunting mechanism of GWO is embedded into the WOA exploitation phase with a new condition which is related to GWO. Secondly, a new technique is added to the exploration phase to improve the solution after each iteration. Experimentations are tested on three different standard test functions which are called benchmark functions: 23 common functions, 25 CEC2005 functions and 10 CEC2019 functions. The proposed WOAGWO is also evaluated against original WOA, GWO and three other commonly used algorithms. Results show that WOAGWO outperforms other algorithms depending on the Wilcoxon rank-sum test. Finally, WOAGWO is likewise applied to solve an engineering problem such as pressure vessel design. Then the results prove that WOAGWO achieves optimum solution which is better than WOA and Fitness Dependent Optimizer (FDO).

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“…Implementing the GWO algorithm has been encouraging enough to merit further investigation as the GWO algorithm is used straight forward and can converge rapidly. Therefore, scientists and engineers, who are working on this field, are still under ways to improve GWO further (Mittal et al, 2016;Gao and Zhao, 2019;Wang et al, 2020;Barraza et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A new K-means grey wolf algorithm for engineering problems

Mohammed¹,

Abdul²,

Rashid

et al. 2021

WJE

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Purpose This paper aims at studying meta-heuristic algorithms. One of the common meta-heuristic optimization algorithms is called grey wolf optimization (GWO). The key aim is to enhance the limitations of the wolves’ searching process of attacking gray wolves. Design/methodology/approach The development of meta-heuristic algorithms has increased by researchers to use them extensively in the field of business, science and engineering. In this paper, the K-means clustering algorithm is used to enhance the performance of the original GWO; the new algorithm is called K-means clustering gray wolf optimization (KMGWO). Findings Results illustrate the efficiency of KMGWO against to the GWO. To evaluate the performance of the KMGWO, KMGWO applied to solve CEC2019 benchmark test functions. Originality/value Results prove that KMGWO is superior to GWO. KMGWO is also compared to cat swarm optimization (CSO), whale optimization algorithm-bat algorithm (WOA-BAT), WOA and GWO so KMGWO achieved the first rank in terms of performance. In addition, the KMGWO is used to solve a classical engineering problem and it is superior.

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A New Hybridization Approach between the Fireworks Algorithm and Grey Wolf Optimizer Algorithm

Cited by 24 publications

References 25 publications

An improved Wolf pack algorithm for optimization problems: Design and evaluation

An improved Wolf pack algorithm for optimization problems: Design and evaluation

A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design

A new K-means grey wolf algorithm for engineering problems

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