Mixed grey wolf optimizer for the joint denoising and unmixing of multispectral images

Martin, Benoı̂t; Marot, Julien; Bourennane, Salah

doi:10.1016/j.asoc.2018.10.019

Cited by 35 publications

(34 citation statements)

References 54 publications

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“…A novel method named mixed GWO was recently proposed in order to effectively handle a problem with both discrete and continuous variables. This algorithm is obtained by combining two improved methods: the improved discrete and global continuous GWO algorithms 52 . The main flowchart of the proposed algorithm is described in Figure 7.…”

Section: System Studiesmentioning

confidence: 99%

“…In recent times, a novel method named adaptive mixed Grey Wolf Optimizer (amixed GWO), which can handle a problem with both discrete and continuous variables, was been obtained by combining the improved discrete and global continuous GWO proposed in Reference 52. This algorithm has been tested and has shown its effectiveness and superiority among a number of biology‐based methods such us GWO, particle swarm optimization (PSO), artificial bee colony (ABC), 53 tree seed algorithm (TSA), 54 genetic algorithm (GA), 55 and simulated annealing (SA) 56 .…”

Section: Introductionmentioning

confidence: 99%

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Stability improvement of interconnected AC / DC multiarea AGC power systems using optimized virtual synchronous power strategy based on eigenvalues sensitivity and adaptive mixed GWO

Tebib

Boudour

2021

Int Trans Electr Energ Syst

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Summary In this paper, a tuning approach for HVDC control technology based on virtual synchronous power (VSP) concept is developed and reflected in an extended multiarea automatic generation control (AGC) system including renewable energy sources (RES). A novel, nature‐inspired algorithm named adaptive mixed grey wolf (amixed GWO), which combines two improved versions of GWO, was recently proposed in order to effectively handle a problem with both discrete and continuous variables. This algorithm was first applied to optimize the VSP‐based controller parameters based on integral square error (ISE). The performance of the proposed algorithm has been compared with FMINCON's interior‐point algorithm to demonstrate its positive effects on the system stability. For better dynamic performances, a new optimization approach based on eigenvalues sensitivity analysis was proposed for the purpose of achieving optimal VSP control variables tuning for different power system structures. Simulation tests were performed to show the higher level of stability performance achieved by the proposed approach compared to the one based on ISE. Simulation results also revealed that the amixed GWO technique has better tuning capability in comparison with the other applied GWO versions.

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Section: System Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stability improvement of interconnected AC / DC multiarea AGC power systems using optimized virtual synchronous power strategy based on eigenvalues sensitivity and adaptive mixed GWO

Tebib

Boudour

2021

Int Trans Electr Energ Syst

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“…This mechanism of location updating creates a simple algorith m framework for GWO, is easy to imp lement on a computer, and requires few parameters to be adjusted. Therefore, this method has been successfully utilized in the fields of economic load dispatch (ELD) problems [23][24], automatic control [25], image processing [26], strategic bidding in the energy market [27], machine learning [28], and aerial vehicle path planning in unmanned combat [29], among others.…”

Section: Introductionmentioning

confidence: 99%

Grey Wolf Optimizer With a Novel Weighted Distance for Global Optimization

Jia

2020

IEEE Access

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In this paper, a new grey wolf optimizer (GWO) variant based on a novel weighted distance (WD) called the GW O-WD algorith m is presented to solve global optimization problems. First, a modified position-updating equation formu lated using the proposed strategy is emp loyed to obtain additional informat ion and improved global solutions. Then, several of the worst indiv iduals are eliminated and repositioned using an elimination and repositioning strategy to improve the capability of the algorithm and avoid falling into local optima. The performance of the algorith m is verified by utilizing 23 widely used benchmark test functions, the IEEE CEC2014 test suite and three well-known engineering design problems. The simulat ion results of the proposed algorith m are co mpared with those of the standard GW O algorithm, three GW O variants and several existing methods, and the proposed algorithm is revealed to be very competitive and, in many cases, superior.

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“…The GWO algorithm has been proved to be efficient and capable to solve the problems human met. It has been applied in diagnosis of diseases [6], dataset clustering [7], denoising of images [8], feature selection [9], economic load dispatch problems [10], multi‐objective problems [11], and image segmentations [12]. Then, a lot of improvements are proposed worldwide to the original GWO algorithm together with the random walk [13], chaos [14], binaries [15], and levy flights [16].…”

Section: Introductionmentioning

confidence: 99%