Journal ArticleDOI
A non-convex economic load dispatch problem with valve loading effect using a hybrid grey wolf optimizer
TLDR
The proposed grey wolf optimizer (GWO) which is a swarm intelligence is hybridized with a local search algorithm, to improve convergence properties and is proved to be a powerful method for ELD problem or for any other similar problems in the power system domain.Abstract:
Economic load dispatch (ELD) is a crucial problem in the power system which is tackled by distributing the required generation power through a set of units to minimize the fuel cost required. This distribution is subject to two main constraints: (1) equality and inequality related to power balance and power output, respectively. In the optimization context, ELD is formulated as a non-convex, nonlinear, constrained optimization problem which cannot be easily solved using calculus-based techniques. Several optimization algorithms have been adapted. Due to the complexity nature of ELD search space, the theoretical concepts of these optimization algorithms have been modified or hybridized. In this paper, the grey wolf optimizer (GWO) which is a swarm intelligence is hybridized with $$\beta$$
-hill climbing optimizer (
$$\beta$$
HC) which is a local search algorithm, to improve convergence properties. GWO is very powerful in a wide search, while $$\beta$$
HC is very powerful in deep search. By combining the wide and deep search ability in a single optimization framework, the balance between the exploration and exploitation is correctly managed. The proposed hybrid algorithm is named $$\beta$$
-GWO which is evaluated using five different test cases of ELD problems: 3 generating units with 850 MW; 13 generating units with 1800 MW; 13 generating units with 2520 MW; 40 generating units with 10,500 MW; and 80 generating units with 21,000 MW. $$\beta$$
-GWO is comparatively measured using 49 comparative methods. The results obtained by $$\beta$$
-GWO outperform others in most test cases. In conclusion, the proposed $$\beta$$
-GWO is proved to be a powerful method for ELD problem or for any other similar problems in the power system domain.read more
Citations
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Journal ArticleDOI
Orthogonal learning covariance matrix for defects of grey wolf optimizer: Insights, balance, diversity, and feature selection
Jiao Hu,Huiling Chen,Ali Asghar Heidari,Ali Asghar Heidari,Mingjing Wang,Xiaoqin Zhang,Ying Chen,Zhifang Pan +7 more
TL;DR: This paper develops a GWO variant enhanced with a covariance matrix adaptation evolution strategy (CMAES), levy flight mechanism, and orthogonal learning (OL) strategy named GWOCMALOL, which could reach higher classification accuracy and fewer feature selections than other optimization algorithms.
Journal ArticleDOI
Image segmentation of Leaf Spot Diseases on Maize using multi-stage Cauchy-enabled grey wolf algorithm
Helong Yu,Jiuman Song,Cheng Chen,Ali Heidari,Jiawen Liu,Huiling Chen,Atef Zaguia,Majdi Mafarja +7 more
TL;DR: In this paper , a multi-stage grey wolf optimizer (MGWO) was proposed to improve the performance of the basic GWO by dividing the search process into three stages and using different population updating strategies.
Journal ArticleDOI
Gene selection for microarray data classification based on Gray Wolf Optimizer enhanced with TRIZ-inspired operators
Osama Ahmad Alomari,Sharif Naser Makhadmeh,Sharif Naser Makhadmeh,Sharif Naser Makhadmeh,Mohammed Azmi Al-Betar,Mohammed Azmi Al-Betar,Zaid Abdi Alkareem Alyasseri,Zaid Abdi Alkareem Alyasseri,Iyad Abu Doush,Iyad Abu Doush,Ammar Kamal Abasi,Ammar Kamal Abasi,Mohammed A. Awadallah,Mohammed A. Awadallah,Raed Abu Zitar +14 more
TL;DR: A new hybrid filter-wrapper approach using robust Minimum Redundancy Maximum Relevancy (rMRMR) as a filter approach to choose the top-ranked genes and it achieves the best results in four out of nine datasets and it obtains remarkable results on the remaining datasets.
Journal ArticleDOI
Dynamic economic emission dispatch considering renewable energy generation: A novel multi-objective optimization approach
TL;DR: A moth-flame optimization algorithm based on position disturbance updating strategy (MFO_PDU) was proposed aiming at the non-convex, non-linear and high-dimensional characteristics of HDEED problem and contributed in reducing the fuel cost and pollutant emissions of power generation system, and further improving the utilization and penetration rate of renewable energy.
Journal ArticleDOI
Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem
TL;DR: The gradient-based optimizer (GBO) as discussed by the authors is a new metaheuristic algorithm inspired by Newton's method that integrates both the gradient search rule and local escaping operator.
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