A new K-means grey wolf algorithm for engineering problems

Mohammed, Hardi M.; Abdul, Zrar Kh.; Rashid, Tarik A.; Alsadoon, Abeer; Bačanin, Nebojša

doi:10.1108/wje-10-2020-0527

Cited by 15 publications

(10 citation statements)

References 82 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of the modified and hybrid algorithms have also been in the recent years to solve many real-world engineering problems. A new K-means grey wolf algorithm was developed by Mohammed et al (2021) to enhance the limitations of the wolves' searching process of attacking gray wolves. A novel hybrid WOA-GWO presented by Mohammed & Rashid (2020) by embedding the hunting mechanism of GWO into the WOA exploitation phase with the enhanced exploration for global numerical optimization and to solve the pressure vessel design problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of Complex System Reliability using Hybrid Grey Wolf Optimizer

Negi

Kumar

Pant

et al. 2021

Decis. Mak. Appl. Manag. Eng.

View full text Add to dashboard Cite

Reliability allocation to increase the total reliability has become a successful way to increase the efficiency of the complex industrial system designs. A lot of research in the past have tackled this problem to a great extent. This is evident from the different techniques developed so far to achieve the target. Stochastic metaheuristics like simulated annealing, Tabu search (TS), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CS), Genetic Algorithm (GA), Grey wolf optimization technique (GWO) etc. have been used in recent years. This paper proposes a framework for implementing a hybrid PSO-GWO algorithm for solving some reliability allocation and optimization problems. A comparison of the results obtained is done with the results of other well-known methods like PSO, GWO, etc. The supremacy/competitiveness of the proposed framework is demonstrated from the numerical experiments. These results with regard to the time taken for the computation and quality of solution outperform the previously obtained results by the other well-known optimization methods.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Optimization of Complex System Reliability using Hybrid Grey Wolf Optimizer

Negi

Kumar

Pant

et al. 2021

Decis. Mak. Appl. Manag. Eng.

View full text Add to dashboard Cite

show abstract

“…The GWO rule was used in minimizing the SSE of the population and searching for a new cluster center. Mohammed et al [107] introduced KMGWO, in which the K-means clustering algorithm was used to enhance GWO's performance.…”

Section: Grey Wolf Optimizermentioning

confidence: 99%

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

2021

View full text Add to dashboard Cite

K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specification of the number of clusters generated from the dataset, which affects the clustering results. Moreover, random initialization of cluster centers results in its local minimal convergence. Automatic clustering is a recent approach to clustering where the specification of cluster number is not required. In automatic clustering, natural clusters existing in datasets are identified without any background information of the data objects. Nature-inspired metaheuristic optimization algorithms have been deployed in recent times to overcome the challenges of the traditional clustering algorithm in handling automatic data clustering. Some nature-inspired metaheuristics algorithms have been hybridized with the traditional K-means algorithm to boost its performance and capability to handle automatic data clustering problems. This study aims to identify, retrieve, summarize, and analyze recently proposed studies related to the improvements of the K-means clustering algorithm with nature-inspired optimization techniques. A quest approach for article selection was adopted, which led to the identification and selection of 147 related studies from different reputable academic avenues and databases. More so, the analysis revealed that although the K-means algorithm has been well researched in the literature, its superiority over several well-established state-of-the-art clustering algorithms in terms of speed, accessibility, simplicity of use, and applicability to solve clustering problems with unlabeled and nonlinearly separable datasets has been clearly observed in the study. The current study also evaluated and discussed some of the well-known weaknesses of the K-means clustering algorithm, for which the existing improvement methods were conceptualized. It is noteworthy to mention that the current systematic review and analysis of existing literature on K-means enhancement approaches presents possible perspectives in the clustering analysis research domain and serves as a comprehensive source of information regarding the K-means algorithm and its variants for the research community.

show abstract

“…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 ). A mechanism based on a mutation operator and an eliminating-reconstructing mechanism for wolves with poor search not only expanded the random search but also increased the convergence rate ( Zhang and Ming, 2018 ).…”

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.

View full text Add to dashboard Cite

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.

show abstract

A new K-means grey wolf algorithm for engineering problems

Cited by 15 publications

References 82 publications

Optimization of Complex System Reliability using Hybrid Grey Wolf Optimizer

Optimization of Complex System Reliability using Hybrid Grey Wolf Optimizer

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

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

Contact Info

Product

Resources

About