A new hybrid algorithm based on grey wolf optimizer and cuckoo search for parameter extraction of solar photovoltaic models

2020 ◽  
Vol 203 ◽  
pp. 112243 ◽  
Author(s):  
Wen Long ◽  
Shaohong Cai ◽  
Jianjun Jiao ◽  
Ming Xu ◽  
Tiebin Wu
Keyword(s):  
Hybrid Algorithm ◽  
Cuckoo Search ◽  
Parameter Extraction ◽  
Grey Wolf Optimizer ◽  
Solar Photovoltaic ◽  
Grey Wolf

Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm

2021 ◽  
pp. 107754632110034
Author(s):  
Ololade O Obadina ◽  
Mohamed A Thaha ◽  
Kaspar Althoefer ◽  
Mohammad H Shaheed
Keyword(s):  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Robot Manipulator ◽  
Moving Average ◽  
Identification Problem ◽  
Whale Optimization Algorithm ◽  
Grey Wolf Optimizer ◽  
Parametric Modelling ◽  
Grey Wolf ◽  
Whale Optimization

This article presents a novel hybrid algorithm based on the grey-wolf optimizer and whale optimization algorithm, referred here as grey-wolf optimizer–whale optimization algorithm, for the dynamic parametric modelling of a four degree-of-freedom master–slave robot manipulator system. The first part of this work consists of testing the feasibility of the grey-wolf optimizer–whale optimization algorithm by comparing its performance with a grey-wolf optimizer, whale optimization algorithm and particle swarm optimization using 10 benchmark functions. The grey-wolf optimizer–whale optimization algorithm is then used for the model identification of an experimental master–slave robot manipulator system using the autoregressive moving average with exogenous inputs model structure. Obtained results demonstrate that the hybrid algorithm is effective and can be a suitable substitute to solve the parameter identification problem of robot models.

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

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2147 ◽  
Author(s):  
Zhihang Yue ◽  
Sen Zhang ◽  
Wendong Xiao
Keyword(s):  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Optimization Method ◽  
Grey Wolf Optimizer ◽  
Local Optimum ◽  
Grey Wolf ◽  
Optimal Search ◽  
Fireworks Algorithm ◽  
Wide Range ◽  
Search Capability

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.

scholarly journals Grey Wolf Optimizer-Based Array Reconfiguration to Enhance Power Production from Solar Photovoltaic Plants under Different Scenarios

2021 ◽  
Vol 13 (24) ◽  
pp. 13627
Author(s):  
Astitva Kumar ◽  
Mohammad Rizwan ◽  
Uma Nangia ◽  
Muhannad Alaraj
Keyword(s):  
Power Loss ◽  
Fill Factor ◽  
Maximum Power ◽  
Performance Ratio ◽  
Grey Wolf Optimizer ◽  
Solar Photovoltaic ◽  
Solar Pv ◽  
Grey Wolf ◽  
Partial Shading ◽  
Power Enhancement

The extraction of maximum power is a big challenge in solar photovoltaic-based power plants due to varying atmospheric and meteorological parameters. The concept of array reconfiguration is applied for the maximum power extraction in solar PV plants. Using this approach, the occurrence of multiple peaks in P-V and I-V characteristics during partial shade can be smoothened and reduced significantly. Partial shading due to the movement of the cloud is considered in the research. The cloud movement mainly because of velocity and wind direction is used for creating various shading conditions. The main focus is to reduce the power losses during partial shading using a nature-inspired optimization approach to reconfigure the array for different types of shading conditions. A grey wolf optimizer-based bridge-linked total cross-tied (GWO-BLTCT) configuration is proposed in this paper. The performance of the proposed topology is compared with standard and hybrid topologies, namely, series-parallel, total cross-tied, BLTCT, and SuDoKu-BLTCT, based on performance indicators such as fill factor, performance ratio, power enhancement, and power loss. The proposed GWO-BLTCT outperforms the remaining topologies due to the least power loss and high fill factor. It also has the highest average power enhancement and performance ratio with 23.75% and 70.02% respectively.

scholarly journals Optimization of Complex System Reliability using Hybrid Grey Wolf Optimizer

2021 ◽  
Vol 4 (2) ◽  
pp. 241-256
Author(s):  
Ganga Negi ◽  
◽  
Anuj Kumar ◽  
Sangeeta Pant ◽  
Mangey Ram ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Optimization Technique ◽  
Cuckoo Search ◽  
Optimization Methods ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Reliability Allocation ◽  
Search Optimization ◽  
Cuckoo Search Optimization ◽  
Comparison Of The Results

Reliability allocation to increase the total reliability has become a successful way to increase the efficiency of the complex industrial system designs. A lot of research in the past have tackled this problem to a great extent. This is evident from the different techniques developed so far to achieve the target. Stochastic metaheuristics like simulated annealing, Tabu search (TS), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CS), Genetic Algorithm (GA), Grey wolf optimization technique (GWO) etc. have been used in recent years. This paper proposes a framework for implementing a hybrid PSO-GWO algorithm for solving some reliability allocation and optimization problems. A comparison of the results obtained is done with the results of other well-known methods like PSO, GWO, etc. The supremacy/competitiveness of the proposed framework is demonstrated from the numerical experiments. These results with regard to the time taken for the computation and quality of solution outperform the previously obtained results by the other well-known optimization methods.

Urmia lake water depth modeling using extreme learning machine-improved grey wolf optimizer hybrid algorithm

2021 ◽  
Vol 146 (1-2) ◽  
pp. 833-849
Author(s):  
Ali Kozekalani Sales ◽  
Enes Gul ◽  
Mir Jafar Sadegh Safari ◽  
Hadi Ghodrat Gharehbagh ◽  
Babak Vaheddoost
Keyword(s):  
Extreme Learning Machine ◽  
Water Depth ◽  
Lake Water ◽  
Hybrid Algorithm ◽  
Urmia Lake ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Learning Machine ◽  
Improved Grey Wolf Optimizer

A novel hybrid algorithm based on Biogeography-Based Optimization and Grey Wolf Optimizer

2018 ◽  
Vol 67 ◽  
pp. 197-214 ◽  
Author(s):  
Xinming Zhang ◽  
Qiang Kang ◽  
Jinfeng Cheng ◽  
Xia Wang
Keyword(s):  
Hybrid Algorithm ◽  
Grey Wolf Optimizer ◽  
Grey Wolf

scholarly journals Grey-Wolf-Based Wang’s Demons for Retinal Image Registration

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 659 ◽  
Author(s):  
Sayan Chakraborty ◽  
Ratika Pradhan ◽  
Amira S. Ashour ◽  
Luminita Moraru ◽  
Nilanjan Dey
Keyword(s):  
Image Registration ◽  
Retinal Image ◽  
Firefly Algorithm ◽  
Cuckoo Search ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Swarm Optimization ◽  
Registration Error ◽  
Registration Process ◽  
Fast Processing

Image registration has an imperative role in medical imaging. In this work, a grey-wolf optimizer (GWO)-based non-rigid demons registration is proposed to support the retinal image registration process. A comparative study of the proposed GWO-based demons registration framework with cuckoo search, firefly algorithm, and particle swarm optimization-based demons registration is conducted. In addition, a comparative analysis of different demons registration methods, such as Wang’s demons, Tang’s demons, and Thirion’s demons which are optimized using the proposed GWO is carried out. The results established the superiority of the GWO-based framework which achieved 0.9977 correlation, and fast processing compared to the use of the other optimization algorithms. Moreover, GWO-based Wang’s demons performed better accuracy compared to the Tang’s demons and Thirion’s demons framework. It also achieved the best less registration error of 8.36 × 10−5.

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