Grey wolf optimization applied to economic load dispatch problems

2016 ◽  
Vol 83 ◽  
pp. 325-334 ◽  
Author(s):  
Moumita Pradhan ◽  
Provas Kumar Roy ◽  
Tandra Pal
Keyword(s):  
Economic Load Dispatch ◽  
Grey Wolf ◽  
Grey Wolf Optimization

scholarly journals An Efficient Chameleon Swarm Algorithm for Economic Load Dispatch Problem

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2770
Author(s):  
Mokhtar Said ◽  
Ali M. El-Rifaie ◽  
Mohamed A. Tolba ◽  
Essam H. Houssein ◽  
Sanchari Deb
Keyword(s):  
Economic Cost ◽  
Economic Load Dispatch ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Best Value ◽  
Sine Cosine Algorithm ◽  
Cost Of Production ◽  
Swarm Algorithm ◽  
Main Item ◽  
Emission Dispatch

Economic Load Dispatch (ELD) is a complicated and demanding problem for power engineers. ELD relates to the minimization of the economic cost of production, thereby allocating the produced power by each unit in the most possible economic manner. In recent years, emphasis has been laid on minimization of emissions, in addition to cost, resulting in the Combined Economic and Emission Dispatch (CEED) problem. The solutions of the ELD and CEED problems are mostly dominated by metaheuristics. The performance of the Chameleon Swarm Algorithm (CSA) for solving the ELD problem was tested in this work. CSA mimics the hunting and food searching mechanism of chameleons. This algorithm takes into account the dynamics of food hunting of the chameleon on trees, deserts, and near swamps. The performance of the aforementioned algorithm was compared with a number of advanced algorithms in solving the ELD and CEED problems, such as Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Earth Worm Algorithm (EWA). The simulated results established the efficacy of the proposed CSA algorithm. The power mismatch factor is the main item in ELD problems. The best value of this factor must tend to nearly zero. The CSA algorithm achieves the best power mismatch values of 3.16 × 10−13, 4.16 × 10−12 and 1.28 × 10−12 for demand loads of 700, 1000, and 1200 MW, respectively, of the ELD problem. The CSA algorithm achieves the best power mismatch values of 6.41 × 10−13 , 8.92 × 10−13 and 1.68 × 10−12 for demand loads of 700, 1000, and 1200 MW, respectively, of the CEED problem. Thus, the CSA algorithm was found to be superior to the algorithms compared in this work.

Ameliorated grey wolf optimization for economic load dispatch problem

Energy ◽  
2019 ◽  
Vol 169 ◽  
pp. 398-419 ◽  
Author(s):  
Diljinder Singh ◽  
J.S. Dhillon
Keyword(s):  
Economic Load Dispatch ◽  
Grey Wolf ◽  
Grey Wolf Optimization

Evaluation of Grey Wolf Optimization Algorithm on Rigid and Flexible Receptor Docking

2020 ◽  
Author(s):  
Kin Meng Wong ◽  
Shirley Siu
Keyword(s):  
Scoring Function ◽  
Ligand Docking ◽  
Receptor Protein ◽  
Pose Prediction ◽  
Grey Wolf ◽  
Success Rates ◽  
Autodock Vina ◽  
Grey Wolf Optimization ◽  
Structure Based Drug Design ◽  
Docking Program

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein in current structure-based drug design. In this paper, we evaluate the performance of grey wolf optimization (GWO) in protein-ligand docking. Two versions of the GWO docking program – the original GWO and the modified one with random walk – were implemented based on AutoDock Vina. Our rigid docking experiments show that the GWO programs have enhanced exploration capability leading to significant speedup in the search while maintaining comparable binding pose prediction accuracy to AutoDock Vina. For flexible receptor docking, the GWO methods are competitive in pose ranking but lower in success rates than AutoDockFR. Successful redocking of all the flexible cases to their holo structures reveals that inaccurate scoring function and lack of proper treatment of backbone are the major causes of docking failures.

Optimal Conductor Selection Using Grey Wolf Optimization

2016 ◽  
Vol 4 (3) ◽  
pp. 39
Author(s):  
Ramanaiah M. LAXMIDEVI ◽  
REDDY M. DAMODAR ◽  
◽  
Keyword(s):  
Grey Wolf ◽  
Grey Wolf Optimization
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