Intelligent Grey Wolf Optimizer – Development and application for strategic bidding in uniform price spot energy market

Saxena, Akash; Soni, Bhanu Pratap; Kumar, Rajesh; Gupta, Vikas

doi:10.1016/j.asoc.2018.04.018

Cited by 67 publications

(20 citation statements)

References 35 publications

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“…Standalone models may not cope with the non-stationary properties of data, either spatially or temporally, especially when dealing with highly complicated spatial interrelationships and the problem of data scarcity (such as urban environments). Regarding the benefits of Bat and GWO algorithms and wavelet transformers, the results of this study are in agreement with previous findings 34 , 37 .…”

Section: Discussionsupporting

confidence: 93%

“… 33 investigated the optimization performance of GWO and found that it has considerable capacity for optimizing models. Saxena and Shekhawat 34 confirmed the satisfactory efficiency of GWO in optimizing SVM for an air quality classification issue. Yang 35 describes the characteristics of the Bat algorithm that lead to its optimizing and improving model performance.…”

Section: Discussionmentioning

confidence: 81%

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Development of novel hybridized models for urban flood susceptibility mapping

Rahmati

Darabi

Panahi

et al. 2020

Sci Rep

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Floods in urban environments often result in loss of life and destruction of property, with many negative socio-economic effects. However, the application of most flood prediction models still remains challenging due to data scarcity. This creates a need to develop novel hybridized models based on historical urban flood events, using, e.g., metaheuristic optimization algorithms and wavelet analysis. The hybridized models examined in this study (Wavelet-SVR-Bat and Wavelet-SVR-GWO), designed as intelligent systems, consist of a support vector regression (SVR), integrated with a combination of wavelet transform and metaheuristic optimization algorithms, including the grey wolf optimizer (GWO), and the bat optimizer (Bat). The efficiency of the novel hybridized and standalone SVR models for spatial modeling of urban flood inundation was evaluated using different cutoff-dependent and cutoff-independent evaluation criteria, including area under the receiver operating characteristic curve (AUC), Accuracy (A), Matthews Correlation Coefficient (MCC), Misclassification Rate (MR), and F-score. The results demonstrated that both hybridized models had very high performance (Wavelet-SVR-GWO: AUC = 0.981, A = 0.92, MCC = 0.86, MR = 0.07; Wavelet-SVR-Bat: AUC = 0.972, A = 0.88, MCC = 0.76, MR = 0.11) compared with the standalone SVR (AUC = 0.917, A = 0.85, MCC = 0.7, MR = 0.15). Therefore, these hybridized models are a promising, cost-effective method for spatial modeling of urban flood susceptibility and for providing in-depth insights to guide flood preparedness and emergency response services.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 81%

Development of novel hybridized models for urban flood susceptibility mapping

Rahmati

Darabi

Panahi

et al. 2020

Sci Rep

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“…2018, [45] The aim of IGWO was at solving different problems in companies, which sale power in the energy market. Companies using different strategies to increase their profit but they have difficulties in predicting the information about the future energy price.…”

Section: Intelligent Gwomentioning

confidence: 99%

A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design

Mohammed

Rashid

2020

Neural Comput & Applic

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A recent metaheuristic algorithm, such as Whale Optimization Algorithm (WOA), was proposed. The idea of proposing this algorithm belongs to the hunting behavior of the humpback whale. However, WOA suffers from poor performance in the exploitation phase and stagnates in the local best solution. Grey Wolf Optimization (GWO) is a very competitive algorithm comparing to other common metaheuristic algorithms as it has a super performance in the exploitation phase while it is tested on unimodal benchmark functions. Therefore, the aim of this paper is to hybridize GWO with WOA to overcome the problems. GWO can perform well in exploiting optimal solutions. In this paper, a hybridized WOA with GWO which is called WOAGWO is presented. The proposed hybridized model consists of two steps. Firstly, the hunting mechanism of GWO is embedded into the WOA exploitation phase with a new condition which is related to GWO. Secondly, a new technique is added to the exploration phase to improve the solution after each iteration. Experimentations are tested on three different standard test functions which are called benchmark functions: 23 common functions, 25 CEC2005 functions and 10 CEC2019 functions. The proposed WOAGWO is also evaluated against original WOA, GWO and three other commonly used algorithms. Results show that WOAGWO outperforms other algorithms depending on the Wilcoxon rank-sum test. Finally, WOAGWO is likewise applied to solve an engineering problem such as pressure vessel design. Then the results prove that WOAGWO achieves optimum solution which is better than WOA and Fitness Dependent Optimizer (FDO).

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“…In this context, the first three best solutions are recorded, and other search agents including the omegas are forced to update their current positions according to the location of the best candidate search agent (ie, alpha). 25,26 The following formulas present the position updating mechanism.…”

Section: Overview Of Gwomentioning

confidence: 99%

“…In this context, these methods require particular mention, and they are as follows: (a) bacterial foraging optimization (BFO) 12,13 ; (b) PSO [14][15][16][17][18] ; (c) Artificial Bee Colony technique 19 ; (d) GSA [20][21][22][23][24] ; (e) Genetic Algorithm (GA) 21 ; and (f) the authors' approach based on GWO-PI. 25,26 Particularly, the optimized gain coefficients of PI-compensator can be achieved in two steps as outlined below:…”

mentioning

confidence: 99%

Electronically coupled photovoltaic system with grey wolf optimizer enabled DC ‐link voltage control loop

Patel

Kumar

Gupta

2020

Int Trans Electr Energ Syst

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Stabilization of DC-link voltage is indispensable for the satisfactory operation of three-phase electronically coupled photovoltaic (PV) system. Practically, a challenge is to promptly and effectively settle the DC-link voltage with minimum peak magnitude and settling time to its pre-disturbance level when exposed to disturbances in the load and solar insolation level. The classical methods of DClink voltage stabilization employ a proportional-plus-integral (PI) compensator, and its gain coefficients are modulated through a cumbersome trial-and-error approach. Nevertheless, this approach does not yield superior results, especially under the wide interruptions in operating conditions due to nonlinear and timevarying characteristic of the PV module. These concerns have resulted in the emergence of multiple meta-heuristic techniques to optimally tune PI compensator gain coefficients. Nevertheless, most of these methods call for the ingrained paradox between computational efficiency and dynamic performance. Driven by this drawback, a proposal based on grey wolf optimizer (GWO-PI) is described in this paper to serve the following control targets: (a) be able to improve harmonic filtering with computational effectiveness, and (b) to ensure superior performance under exposure to interferences in the load and solar insolation level. Good agreement in MATLAB/Simulink simulation studies and experimental evaluations establish that the proposed GWO-PI outperforms the existing standardized optimization techniques such as particle swarm optimization (PSO-PI) and gravitational search algorithm (GSA-PI) in aspects of peak magnitude and settling time. Besides, it practically enables an improved harmonic filtering LIST OF SYMBOLS AND ABBREVIATIONS: K p , proportional gain coefficient of PI-controller; K i , integral gain coefficient of PI-controller; i LR , i LY , i LB , load currents; i L0 , zero-sequence component of the current; i Lα , i Lβ , i L0 , stationary reference frame currents; i Ld , active current component; i Lq , reactive current component; φ, grid voltage phase-angle; i * dc , d-axis reference current; i dc , DC-link VCL output; i Ldh , load harmonic current; i α , i β , i 0 , reference compensating currents; i Cα , i Cβ , i C0 , actual compensating current; V DC , measured DC-link voltage; V DCref , reference DC-link voltage; E SS , steady-state error; O f , objective function; M p , peak magnitude; T s , settling time; α, alpha; β, beta; δ, delta; ω, omega; D !

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Intelligent Grey Wolf Optimizer – Development and application for strategic bidding in uniform price spot energy market

Cited by 67 publications

References 35 publications

Development of novel hybridized models for urban flood susceptibility mapping

Development of novel hybridized models for urban flood susceptibility mapping

A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design

Electronically coupled photovoltaic system with grey wolf optimizer enabled DC ‐link voltage control loop

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