A greedy non‐hierarchical grey wolf optimizer for real‐world optimization

Akbari, Ebrahim; Rahimnejad, Abolfazl; Gadsden, S. Andrew

doi:10.1049/ell2.12176

Cited by 20 publications

(9 citation statements)

References 16 publications

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“…2.4. Shapley Values.The SHAP technique is based on the Shapley value principle from the game theory[53,54]. The Shapley value (SHAP) principle was developed to anticipate the relevance of an individual player in a cooperative team.…”

mentioning

confidence: 99%

Prediction of Wear Rates of UHMWPE Bearing in Hip Joint Prosthesis with Support Vector Model and Grey Wolf Optimization

Hammam

Attar

Amer

et al. 2022

Wireless Communications and Mobile Computing

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One of the greatest challenges in joint arthroplasty is to enhance the wear resistance of ultrahigh molecular weight polyethylene (UHMWPE), which is one of the most successful polymers as acetabular bearings for total hip joint prosthesis. In order to improve UHMWPE wear rates, it is necessary to develop efficient methods to predict its wear rates in various conditions and therefore help in improving its wear resistance, mechanical properties, and increasing its life span inside the body. This article presents a support vector machine using a grey wolf optimizer (SVM-GWO) hybrid regression model to predict the wear rates of UHMWPE based on published polyethylene data from pin on disc (PoD) wear experiments typically performed in the field of prosthetic hip implants. The dataset was an aggregate of 29 different PoD UHMWPE datasets collected from Google Scholar and PubMed databases, and it consisted of 129 data points. Shapley additive explanations (SHAP) values were used to interpret the presented model to identify the most important and decisive parameters that affect the wear rates of UHMWPE and, therefore, predict its wear behavior inside the body under different conditions. The results revealed that radiation doses had the highest impact on the model’s prediction, where high values of radiation doses had a negative impact on the model output. The pronounced effect of irradiation doses and surface roughness on the wear rates of polyethylene was clear in the results when average disc surface roughness R a values were below 0.05 μm, and irradiation doses were above 95 kGy produced 0 mg/MC wear rate. The proposed model proved to be a reliable and robust model for the prediction of wear rates and prioritizing factors that most significantly affect its wear rates. The proposed model can help material engineers to further design polyethylene acetabular linings via improving the wear resistance and minimizing the necessity for wear experiments.

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mentioning

confidence: 99%

Prediction of Wear Rates of UHMWPE Bearing in Hip Joint Prosthesis with Support Vector Model and Grey Wolf Optimization

Hammam

Attar

Amer

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…On the other hand, this mechanism has a crucial disadvantage in optimizing some real-world problems, i.e ., the algorithm converges rapidly but to a locally optimal solution. To overcome these drawbacks in the GWO, the G-NHGWO algorithm ( Akbari, Rahimnejad & Gadsden, 2021 ), the best personal optimal solution for the -th grey wolf, has been established and stored, like in the PSO algorithm. Then, three members, , , and , with individual best positions, respectively , , and , are selected randomly and utilized to lead the population to update the new positions as Eqs.…”

Section: The Proposed Hde Algorithmmentioning

confidence: 99%

“…In the GWO algorithm, , , and wolves always participate in the hunting phase. Therefore, using the positions of the best wolves in the hunting phase increases the quality of the exploration and accelerates the convergence of the GWO method ( Akbari, Rahimnejad & Gadsden, 2021 ). Since one of the main problems of the DE is the low convergence speed, in this study, the hunting phase of GWO is proposed as an auxiliary mutation vector to the DE to accelerate the convergence rate and achieve more optimal solutions.…”

Section: The Proposed Hde Algorithmmentioning

confidence: 99%

A hybridizing-enhanced differential evolution for optimization

Ghasemi

Zare

Trojovský

et al. 2023

PeerJ Computer Science

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Differential evolution (DE) belongs to the most usable optimization algorithms, presented in many improved and modern versions in recent years. Generally, the low convergence rate is the main drawback of the DE algorithm. In this article, the gray wolf optimizer (GWO) is used to accelerate the convergence rate and the final optimal results of the DE algorithm. The new resulting algorithm is called Hunting Differential Evolution (HDE). The proposed HDE algorithm deploys the convergence speed of the GWO algorithm as well as the appropriate searching capability of the DE algorithm. Furthermore, by adjusting the crossover rate and mutation probability parameters, this algorithm can be adjusted to pay closer attention to the strengths of each of these two algorithms. The HDE/current-to-rand/1 performed the best on CEC-2019 functions compared to the other eight variants of HDE. HDE/current-to-best/1 is also chosen as having superior performance to other proposed HDE compared to seven improved algorithms on CEC-2014 functions, outperforming them in 15 test functions. Furthermore, jHDE performs well by improving in 17 functions, compared with jDE on these functions. The simulations indicate that the proposed HDE algorithm can provide reliable outcomes in finding the optimal solutions with a rapid convergence rate and avoiding the local minimum compared to the original DE algorithm.

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“…To further validate the effectiveness of the REGWO. The performances of different GWO variants are compared, and the benchmark functions ( f 1 ∼ f 31 ) are solved by REGWO, WGWO [ 35 ], DGWO [ 59 ], AGWO [ 60 ], IGWO [ 61 ], RLGWO [ 62 ], and GNHGWO [ 63 ]. To make a fair comparison, the 6 algorithms use the same parameter settings as their original literature.…”

Section: Experiments and Analysismentioning

confidence: 99%

Equalized Grey Wolf Optimizer with Refraction Opposite Learning

Sun

Feng

Chen

et al. 2022

Computational Intelligence and Neuroscience

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Grey wolf optimizer (GWO) is a global search algorithm based on grey wolf hunting activity. However, the traditional GWO is prone to fall into local optimum, affecting the performance of the algorithm. Therefore, to solve this problem, an equalized grey wolf optimizer with refraction opposite learning (REGWO) is proposed in this study. In REGWO, the issue about the low swarm population variety of GWO in the late iteration is well overcome by the opposing learning of refraction. In addition, the equilibrium pool strategy reduces the likelihood of wolves going to the local extremum. To investigate the effectiveness of REGWO, it is evaluated on 21 widely used benchmark functions and IEEE CEC 2019 test functions. Experimental results show/ that REGWO performs better than the other competitors on most benchmarks.

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A greedy non‐hierarchical grey wolf optimizer for real‐world optimization

Cited by 20 publications

References 16 publications

Prediction of Wear Rates of UHMWPE Bearing in Hip Joint Prosthesis with Support Vector Model and Grey Wolf Optimization

Prediction of Wear Rates of UHMWPE Bearing in Hip Joint Prosthesis with Support Vector Model and Grey Wolf Optimization

A hybridizing-enhanced differential evolution for optimization

Equalized Grey Wolf Optimizer with Refraction Opposite Learning

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