Hybridizing grey wolf optimization with neural network algorithm for global numerical optimization problems

Zhang, Yiying; Jin, Zhigang; Chen, Ye

doi:10.1007/s00521-019-04580-4

Cited by 29 publications

(13 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To improve the convergence speed of our proposed ITLHHO, an ITLBO is presented in this paper. In the teacher phase, the updating phase given in Equation ( 17) is modified by the following equation (20).…”

Section: The Itlbomentioning

confidence: 99%

“…18 Later, in 2019, the gravitational search algorithm (GSA) combined with GA and a hybrid GSA-GA is developed by Garg to solve engineering design problems. 19 Again, a hybrid method GNNA combining GWO and NNA is proposed by Zhang et al 20 Note that all the above-cited hybrid algorithms have been shown to be more competent compared with the corresponding original methods. Considering the efficiency of the hybrid methods, this paper introduces a new hybrid method based on HHO and TLBO to solve different kinds of numerical and engineering optimization problems.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A hybrid ITLHHO algorithm for numerical and engineering optimization problems

Kundu

Garg

2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

Harris hawks optimization (HHO) is one of the newest metaheuristic algorithms (MHAs) which mimic the interdependent behaviour and hunting style of Harris hawks in nature. It is an efficient swarm optimization technique that has been used to solve various kinds of optimization problems. However, for some optimiza-KUNDU AND GARG | 3901In this section, HHO and TLBO algorithms are briefly described. KUNDU AND GARG | 3903 (20)Similarly, the updating phase in the learner phase given in Equations ( 18) and ( 19) is improved by the following equations ( 21) and ( 22).

show abstract

Section: The Itlbomentioning

confidence: 99%

mentioning

confidence: 99%

A hybrid ITLHHO algorithm for numerical and engineering optimization problems

Kundu

Garg

2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

show abstract

“…Improved Neural Network Algorithm. However, the neural network algorithm provides good results to solve different applications of optimization problems [20][21][22], and it is sometimes stuck in the premature convergence that gives a high impact on the solution accuracy. In this investigation, to modify this drawback, 2 mechanisms are considered.…”

Section: End For End If End Formentioning

confidence: 99%

Skin Cancer Detection Using Kernel Fuzzy C‐Means and Improved Neural Network Optimization Algorithm

Jia

Zhao

Molk

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Early diagnosis of malignant skin cancer from images is a significant part of the cancer treatment process. One of the principal purposes of this research is to propose a pipeline methodology for an optimum computer-aided diagnosis of skin cancers. The method contains four main stages. The first stage is to perform a preprocessing based on noise reduction and contrast enhancement. The second stage is to segment the region of interest (ROI). This study uses kernel fuzzy C-means for ROI segmentation. Then, some features from the ROI are extracted, and then, a feature selection is used for selecting the best ones. The selected features are then injected into a support vector machine (SVM) for final identification. One important part of the contribution in this study is to propose a developed version of a new metaheuristic, named neural network optimization algorithm, to optimize both parts of feature selection and SVM classifier. Comparison results of the method with 5 state-of-the-art methods showed the approach’s higher superiority toward the others.

show abstract

“…Later, Ibrahim et al, [ 38 ] present a hybrid optimization method that combines the salp swarm algorithm (SSA) with the particle swarm optimization for solving the feature selection problem. Again, in 2020, a hybrid method GNNA combining grey wolf optimization (GWO) and neural network algorithm (NNA) is proposed by Zhang [ 94 ]. Note that all above cited hybrid algorithms have been shown to be more competitive compared to the corresponding original methods.…”

Section: Introductionmentioning

confidence: 99%

A hybrid salp swarm algorithm based on TLBO for reliability redundancy allocation problems

2022

View full text Add to dashboard Cite

A novel optimization algorithm called hybrid salp swarm algorithm with teaching-learning based optimization (HSSATLBO) is proposed in this paper to solve reliability redundancy allocation problems (RRAP) with nonlinear resource constraints. Salp swarm algorithm (SSA) is one of the newest meta-heuristic algorithms which mimic the swarming behaviour of salps. It is an efficient swarm optimization technique that has been used to solve various kinds of complex optimization problems. However, SSA suffers a slow convergence rate due to its poor exploitation ability. In view of this inadequacy and resulting in a better balance between exploration and exploitation, the proposed hybrid method HSSATLBO has been developed where the searching procedures of SSA are renovated based on the TLBO algorithm. The good global search ability of SSA and fast convergence of TLBO help to maximize the system reliability through the choices of redundancy and component reliability. The performance of the proposed HSSATLBO algorithm has been demonstrated by seven well-known benchmark problems related to reliability optimization that includes series system, complex (bridge) system, series-parallel system, overspeed protection system, convex system, mixed series-parallel system, and large-scale system with dimensions 36, 38, 40, 42 and 50. After illustration, the outcomes of the proposed HSSATLBO are compared with several recently developed competitive meta-heuristic algorithms and also with three improved variants of SSA. Additionally, the HSSATLBO results are statistically investigated with the wilcoxon sign-rank test and multiple comparison test to show the significance of the results. The experimental results suggest that HSSATLBO significantly outperforms other algorithms and has become a remarkable and promising tool for solving RRAP.

show abstract

Hybridizing grey wolf optimization with neural network algorithm for global numerical optimization problems

Cited by 29 publications

References 41 publications

A hybrid ITLHHO algorithm for numerical and engineering optimization problems

A hybrid ITLHHO algorithm for numerical and engineering optimization problems

Skin Cancer Detection Using Kernel Fuzzy C‐Means and Improved Neural Network Optimization Algorithm

A hybrid salp swarm algorithm based on TLBO for reliability redundancy allocation problems

Contact Info

Product

Resources

About