scholarly journals Statement of Retraction: Mended grey wolf optimization and Taguchi method with multi-goal optimization for six-phase copper rotor induction motor design

2021 ◽  
pp. 1-1
2022 ◽  
Vol 70 (2) ◽  
pp. 2435-2452
Author(s):  
M. Premkumar ◽  
Pradeep Jangir ◽  
B. Santhosh Kumar ◽  
Mohammad A. Alqudah ◽  
Kottakkaran Sooppy Nisar

2021 ◽  
Vol 1773 (1) ◽  
pp. 012007
Author(s):  
Ghaith M. Fadhil ◽  
Issa A. Abed ◽  
Rasheed S. Jasim

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2282 ◽  
Author(s):  
Chih-Hong Lin

This paper presents an altered grey wolf optimization, the Taguchi method, and finite element analysis (FEA) with two-phase multi-objective optimization for the design of a six-phase copper squirrel cage rotor induction motor (SCSCRIM). The multi-objective optimization design with high-performance property aims to achieve lower starting current, lower losses, lower input power, higher efficiency, higher output torque, and higher power factor. The multi-objective optimization design with high-performance property using the altered grey wolf optimization, the Taguchi method, and FEA in the first-phase program is used for minimizing the starting current, stator iron loss, stator copper loss, and input power. The multi-objective optimization design with high-performance property using the altered grey wolf optimization, the Taguchi method, and FEA in the second-phase program is used for maximizing the efficiency, output torque, and power factor. Finally, the proposed skill with higher performances is evaluated and verified via a two-phase program design and some performance tests.


2020 ◽  
Author(s):  
Kin Meng Wong ◽  
Shirley Siu

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.


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