A novel hybrid PSO–GWO algorithm for optimization problems

Şenel, Fatih Ahmet; Gökçe, Fatih; Yüksel, Asım Sinan; Yi̇ği̇t, Tuncay

doi:10.1007/s00366-018-0668-5

Cited by 191 publications

(109 citation statements)

References 43 publications

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“…Due to its competitive performance, the GWO is employed for parameter optimization in this study. Because of a slow convergence rate and easy to fall into local optimum of the original GWO, various improved strategies for the GWO have been proposed, and achieved good results [55][56][57][58]. This paper proposes a hybrid grey wolf optimization algorithm (HGWO), combining the DE algorithm with the GWO, which uses the powerful search ability of the DE to update position of the grey wolf α, β, δ, and thus jump out stagnation and makes the GWO not to fall into the local optimum, which accelerates the convergence speed and improves the performance of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

et al. 2019

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Dissolved gas analysis (DGA) is a widely used method for transformer internal fault diagnosis. However, the traditional DGA technology, including Key Gas method, Dornenburg ratio method, Rogers ratio method, International Electrotechnical Commission (IEC) three-ratio method, and Duval triangle method, etc., suffers from shortcomings such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which affect the reliability of fault analysis. Grey wolf optimizer (GWO) is a novel swarm intelligence optimization algorithm proposed in 2014 and it is easy for the original GWO to fall into the local optimum. This paper presents a new meta-heuristic method by hybridizing GWO with differential evolution (DE) to avoid the local optimum, improve the diversity of the population and meanwhile make an appropriate compromise between exploration and exploitation. A fault diagnosis model of hybrid grey wolf optimized least square support vector machine (HGWO-LSSVM) is proposed and applied to transformer fault diagnosis with the optimal hybrid DGA feature set selected as the input of the model. The kernel principal component analysis (KPCA) is used for feature extraction, which can decrease the training time of the model. The proposed method shows high accuracy of fault diagnosis by comparing with traditional DGA methods, least square support vector machine (LSSVM), GWO-LSSVM, particle swarm optimization (PSO)-LSSVM and genetic algorithm (GA)-LSSVM. It also shows good fitness and fast convergence rate. Accuracies calculated in this paper, however, are significantly affected by the misidentifications of faults that have been made in the DGA data collected from the literature.

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Section: Introductionmentioning

confidence: 99%

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

et al. 2019

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“…The grey wolf optimization (GWO) algorithm [15,16], which simulates the hierarchy and predation behavior of wolves, is one sort of swarm intelligence algorithm. Its evident strengths are a simple mechanism, few parameters, and easy implementation.…”

Section: Standard Grey Wolf Optimization Algorithmmentioning

confidence: 99%

Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine

2020

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In order to improve the accuracy of wind power prediction and ensure the effective utilization of wind energy, a short-term wind power prediction model based on variational mode decomposition (VMD) and an extreme learning machine (ELM) optimized by an improved grey wolf optimization (GWO) algorithm is proposed. The original wind power sequence is decomposed into series of modal components with different center frequencies by the VMD method and some new sequences are obtained by phase space reconstruction (PSR). Then, the ELM model is established for different new time series, and the improved GWO algorithm is used to optimize its parameters. Finally, the output results are weighted and merged as the final predicted value of wind power. The root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the proposed VMD-improved GWO-ELM prediction model in the paper are 5.9113%, 4.6219%, and 13.01% respectively, which are better than these of ELM, back propagation (BP), and the improved GWO-ELM model. The simulation results show that the proposed model has higher prediction accuracy than other models in short-term wind power prediction.

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“…The grey wolf encircles the prey when hunting. The mathematical equations for this behavior are shown in Equations (1) and 2:…”

Section: Encircling Preymentioning

confidence: 99%

“…Finding an optimal solution in high-dimensional complex space is a common issue in many engineering fields [1]. When solving such problems, deterministic algorithms find it difficult to find the optimal value, the calculation cost is too high, and the calculation time is too long [2].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm

Yue

Zhang

Xiao

2020

Sensors

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Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration ability of the fireworks algorithm with the exploitation ability of the grey wolf optimizer (GWO) by setting a balance coefficient. In order to test the competence of the proposed hybrid FWGWO, 16 well-known benchmark functions having a wide range of dimensions and varied complexities are used in this paper. The results of the proposed FWGWO are compared to nine other algorithms, including the standard FWA, the native GWO, enhanced grey wolf optimizer (EGWO), and augmented grey wolf optimizer (AGWO). The experimental results show that the FWGWO effectively improves the global optimal search capability and convergence speed of the GWO and FWA.

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A novel hybrid PSO–GWO algorithm for optimization problems

Cited by 191 publications

References 43 publications

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine

A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm

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