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

scholarly journals Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 950 ◽  
Author(s):  
Jianguo Zhou ◽  
Xuejing Huo ◽  
Xiaolei Xu ◽  
Yushuo Li
Keyword(s):  
Extreme Point ◽  
Extreme Learning Machine ◽  
Carbon Price ◽  
Grey Wolf Optimizer ◽  
Symmetric Mode ◽  
Grey Wolf ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Learning Machine

Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new novel hybrid model for carbon price prediction. The proposed model consists of an extreme-point symmetric mode decomposition, an extreme learning machine, and a grey wolf optimizer algorithm. Firstly, the extreme-point symmetric mode decomposition is employed to decompose the carbon price into several intrinsic mode functions and one residue. Then, the partial autocorrelation function is utilized to determine the input variables of the intrinsic mode functions, and the residue of the extreme learning machine. In the end, the grey wolf optimizer algorithm is applied to optimize the extreme learning machine, to forecast the carbon price. To illustrate the superiority of the proposed model, the Hubei, Beijing, Shanghai, and Guangdong carbon price series are selected for the predictions. The empirical results confirm that the proposed model is superior to the other benchmark methods. Consequently, the proposed model can be employed as an effective method for carbon price series analysis and forecasting.

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.

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