Using Grey Wolf Algorithm to Solve the Capacitated Vehicle Routing Problem

Korayem, L.; Khorsid, M.; Kassem, Sally

doi:10.1088/1757-899x/83/1/012014

Cited by 50 publications

(17 citation statements)

References 6 publications

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“…The hybrid PSO-GWO algorithm based on swarm intelligence had better performance than the particle swarm optimization algorithm in solving single region unit input problems ( Saremi, 2016 ). The K-GWO, combining GWO with a traditional K-means clustering algorithm, solved the capacitated vehicle routing problem ( Korayem et al, 2015 ). A new algorithm using K-means clustering to improve GWO performance was called K-means clustering Gray algorithm Wolf Optimization (KMGWO) ( Hardi et al, 2021 ).…”

Section: The Literature Of the Gray Wolf Algorithmmentioning

confidence: 99%

The Bent-Tube Nozzle Optimization of Force-Spinning With the Gray Wolf Algorithm

Kang

et al. 2021

Front. Bioeng. Biotechnol.

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Force-spinning is a popular way to fabricate various fine fibers such as polymer and metal nanofibers, which are being widely employed in medical and industrial manufacture. The spinneret is the key of the device for spinning fibers, and the physical performance and morphology of the spun nanofibers are largely determined by its structure parameters. In this article, the effect of spinneret parameters on the outlet velocity is explored and the spinneret parameters are also optimized to obtain the maximum outlet velocity. The mathematical model of the solution flow in four areas is established at first, and the relationship between outlet velocity and structure parameters is acquired. This model can directly reflect the flow velocity of the solution in each area. Then, the optimal parameters of outlet diameter, bending angle, and curvature radius are obtained combined with the gray wolf algorithm (GWA). It is found that a curved-tube nozzle with a bending angle of 9.1°, nozzle diameter of 0.6 mm, and curvature radius of 10 mm can obtain the maximum outlet velocity and better velocity distribution. Subsequently, the simulation is utilized to analyze and compare the velocity situation of different parameters. Finally, the fiber of 5 wt% PEO solution is manufactured by a straight-tube nozzle and optimized bent-tube nozzle in the laboratory, and the morphology and diameter distribution were observed using a scanning electron microscope (SEM). The results showed that the outlet velocity was dramatically improved after the bent-tube parameters were optimized by GWA, and nanofibers of better surface quality could be obtained using optimized bent-tube nozzles.

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Section: The Literature Of the Gray Wolf Algorithmmentioning

confidence: 99%

The Bent-Tube Nozzle Optimization of Force-Spinning With the Gray Wolf Algorithm

Kang

et al. 2021

Front. Bioeng. Biotechnol.

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“…For such a motivation, much research attention investigates introducing modified versions of the standard algorithms to improve their overall performance (Ebadifard & Babamir, 2018;Fu et al, 2018;Rani et al, 2019). Besides, other research focuses on proposing novel algorithms by taking into account the interaction of individuals of a swarm and their environment (Korayem et al, 2015;Odili & Mohmad Kahar, 2016). For such a task, one can tap into the so-called swarm intelligence which considered as a specific kind of collective intelligence (Saka et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

An SHO-based approach to timetable scheduling: a case study

Nguyen

2021

Journal of Information and Telecommunication

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University timetable scheduling, which is a typical problem that all universities around the world have to face every semester, is an NPhard problem. It is the task of allocating the right timeslots and classrooms for various courses by taking into account predefined constraints. In the current literature, many approaches have been proposed to find feasible timetables. Among others, swarm-based algorithms are promising candidates because of their effectiveness and flexibility. This paper investigates proposing an approach to university timetable scheduling using a recent novel swarm-based algorithm named Spotted Hyena Optimizer (SHO) which is inspired by the hunting behaviour of spotted hyenas. Then, a combination of SA and SHO algorithms also investigated to improve the overall performance of the proposed method. We also illustrate the proposed method on a real-world university timetabling problem in Vietnam. Experimental results have indicated the efficiency of the proposed method in comparison to other competitive metaheuristic algorithm such as PSO algorithm in finding feasible timetables.

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“…The GWO mimics the ideal hunting behaviors of wolf packs. Since the release of its code in MATLAB and Python [18,54], the GWO has demonstrated very promising results when applied in various real-world applications [44,52,69]. Some of its advantages include its simplicity, few parameters to tune, unique population hierarchy, and the smooth transition from the exploration phase to the exploitation.…”

Section: Introductionmentioning

confidence: 99%

Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach

et al. 2019

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Grey wolf optimizer (GWO) is known as one of the recent popular metaheuristic algorithms inspired from the social collaboration and team hunting activities of grey wolves in nature. This algorithm benefits from stochastic operators, but it is still prone to stagnation in local optima and premature convergence when solving problems with a large number of variables (e.g., clustering problems). To alleviate this shortcoming, the GWO algorithm is hybridized with the well-known tabu search (TS). To investigate the performance of the proposed hybrid GWO and TS (GWOTS), it is compared with well-regarded metaheuristics on various clustering datasets. The comprehensive experiments and analysis verify that the proposed GWOTS shows an improved performance compared to GWO and can be utilized for clustering applications.

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Using Grey Wolf Algorithm to Solve the Capacitated Vehicle Routing Problem

Cited by 50 publications

References 6 publications

The Bent-Tube Nozzle Optimization of Force-Spinning With the Gray Wolf Algorithm

The Bent-Tube Nozzle Optimization of Force-Spinning With the Gray Wolf Algorithm

An SHO-based approach to timetable scheduling: a case study

Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach

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