Grey wolf optimizer: a review of recent variants and applications

Faris, Hossam; Aljarah, Ibrahim; Al-Betar, Mohammed Azmi; Mirjalili, Seyedali

doi:10.1007/s00521-017-3272-5

Cited by 765 publications

(355 citation statements)

References 117 publications

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“…Compared with other swarm-based optimizers such as PSO, GWO has fewer adjustable parameters and allows wolves/search agents to approach, encircle and attack prey/optima [12]. GWO has been quickly adapted and used in many fields including water engineering, but has been used little in groundwater modeling [17,22].…”

Section: Introductionmentioning

confidence: 99%

Comparing Single-Objective Optimization Protocols for Calibrating the Birds Nest Aquifer Model—A Problem Having Multiple Local Optima

Lyons

Peralta

Majumder

2020

IJERPH

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To best represent reality, simulation models of environmental and health-related systems might be very nonlinear. Model calibration ideally identifies globally optimal sets of parameters to use for subsequent prediction. For a nonlinear system having multiple local optima, calibration can be tedious. For such a system, we contrast calibration results from PEST, a commonly used automated parameter estimation program versus several meta-heuristic global optimizers available as external packages for the Python computer language—the Gray Wolf Optimization (GWO) algorithm; the DYCORS optimizer framework with a Radial Basis Function surrogate simulator (DRB); and particle swarm optimization (PSO). We ran each optimizer 15 times, with nearly 10,000 MODFLOW simulations per run for the global optimizers, to calibrate a steady-state, groundwater flow simulation model of the complex Birds Nest aquifer, a three-layer system having 8 horizontal hydraulic conductivity zones and 25 head observation locations. In calibrating the eight hydraulic conductivity values, GWO averaged the best root mean squared error (RMSE) between observed and simulated heads—20 percent better (lower) than the next lowest optimizer, DRB. The best PEST run matched the best GWO RMSE, but both the average PEST RMSE and the range of PEST RMSE results were an order of magnitude larger than any of the global optimizers.

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Section: Introductionmentioning

confidence: 99%

Comparing Single-Objective Optimization Protocols for Calibrating the Birds Nest Aquifer Model—A Problem Having Multiple Local Optima

Lyons

Peralta

Majumder

2020

IJERPH

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“…Towards effectively balancing the exploitation and exploration phases for better convergence in next-hop searching, wolf optimization is used to enable the proposed framework W-GUN [36]. It is also a swarm intelligence technique.…”

Section: Adapted Grey Wolf Optimization For Underwater Networkmentioning

confidence: 99%

W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks

Rathore

Sangwan

Mazumdar

et al. 2020

Sensors

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Underwater sensor networks (UWSNs) have witnessed significant R&D attention in both academia and industry due to their growing application domains, such as border security, freight via sea or river, natural petroleum production and the fishing industry. Considering the deep underwater-oriented access constraints, energy-centric communication for the lifetime maximization of tiny sensor nodes in UWSNs is one of the key research themes in this domain. Existing literature on green UWSNs are majorly adapted from the existing techniques in traditional wireless sensor network relying on geolocation and the quality of service-centric underwater relay node selection, without paying much attention to the dynamic underwater network environments. To this end, this paper presents an adapted whale and wolf optimization-based energy and delay-centric green underwater networking framework (W-GUN). It focuses on exploiting dynamic underwater network characteristics by effectively utilizing underwater whale-centric optimization in relay node selection. Firstly, an underwater relay node optimization model is mathematically derived, focusing on underwater whale dynamics for incorporating realistic underwater characteristics in networking. Secondly, the optimization model is used to develop an adapted whale and grey wolf optimization algorithm for selecting optimal and stable relay nodes for centric underwater communication paths. Thirdly, a complete workflow of the W-GUN framework is presented with an optimization flowchart. The comparative performance evaluation attests to the benefits of the proposed framework and is compared to state-of-the-art techniques considering various metrics related to underwater network environments.Sensors 2020, 20, 1377 2 of 23 water-based transport applications [6,7], oil, and natural gas production applications [8,9], and developing fishing-centric industries [10,11]. In underwater networking, tiny sensor nodes are deployed underwater, as well as on the upper surface layer for monitoring the specific underwater area [12]. These underwater nodes communicate with the surface nodes, acting as access points or cluster heads for reaching the sink node of the network, which accumulates the information and communicates with the cloud-enabled computing resources [13]. Underwater networking is significantly challenging compared to traditional wireless networking due to the dynamic self-mobility of the medium of communication and constraints in signal propagation in the underwater environment [14][15][16]. In this constrained networking environment, the underwater network deployment-oriented challenges further complicate scientific investigations towards the development of an energy-centric green underwater network for various application domains [17][18][19].Towards enabling green underwater networking, several service and geolocation-centric techniques of varying quality have been suggested [20,21]. A heuristic approach has been suggested in underwater networking for solving the surface gateway deployment o...

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“…Finally, the convergence analysis of GWO confirmed the convergence of this algorithm. Finding the global optimum precisely requires balancing the exploration and exploitation (i.e., good equilibrium) and this balance can be achieved using GWO (Faris et al, 2018).…”

Section: Problem Formulationmentioning

confidence: 99%

“…The behavior of this process can be represented mathematically as in Eqs. (9-11) (Faris et al, 2018):…”

Section: Gwo Hunting Processmentioning

confidence: 99%

GWRA: grey wolf based reconstruction algorithm for compressive sensing signals

Aziz

Singh

El‐Sawy

et al. 2019

PeerJ Computer Science

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The recent advances in compressive sensing (CS) based solutions make it a promising technique for signal acquisition, image processing and other types of data compression needs. In CS, the most challenging problem is to design an accurate and efficient algorithm for reconstructing the original data. Greedy-based reconstruction algorithms proved themselves as a good solution to this problem because of their fast implementation and low complex computations. In this paper, we propose a new optimization algorithm called grey wolf reconstruction algorithm (GWRA). GWRA is inspired from the benefits of integrating both the reversible greedy algorithm and the grey wolf optimizer algorithm. The effectiveness of GWRA technique is demonstrated and validated through rigorous simulations. The simulation results show that GWRA significantly exceeds the greedy-based reconstruction algorithms such as sum product, orthogonal matching pursuit, compressive sampling matching pursuit and filtered back projection and swarm based techniques such as BA and PSO in terms of reducing the reconstruction error, the mean absolute percentage error and the average normalized mean squared error.

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Grey wolf optimizer: a review of recent variants and applications

Cited by 765 publications

References 117 publications

Comparing Single-Objective Optimization Protocols for Calibrating the Birds Nest Aquifer Model—A Problem Having Multiple Local Optima

Comparing Single-Objective Optimization Protocols for Calibrating the Birds Nest Aquifer Model—A Problem Having Multiple Local Optima

W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks

GWRA: grey wolf based reconstruction algorithm for compressive sensing signals

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