RETRACTED: Optimization of surface grinding operations using a differential evolution approach

Krishna, A. Murali

doi:10.1016/j.jmatprotec.2006.10.010

Cited by 34 publications

(1 citation statement)

References 12 publications

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“…From Tables 5 and 6, it can be concluded that the HGOANM developed in this paper to solve robot gripper, side crash, multiple disc clutch brake and grinding problems gives better results than those obtained by other researchers (Baskar et al, 2004;Gupta et al, 2001;Huang et al, 2015;Khalilpourazari & Khalilpourazary, 2018;Krishna, 2007;Krishna & Rao, 2006;Lee et al, 2011;Lin & Li, 2008;Pawar et al, 2010;Pawar & Rao, 2013;Saravanan et al, 2002;Slowik & Slowik, 2008;Wen et al, 1992;Yildiz, 2020a;Yildiz, Yildiz, Sait, & Li, 2019;Zhang et al, 2014) in the literature.…”

Section: Optimization Model Of the Grinding Processmentioning

confidence: 71%

Robust design of a robot gripper mechanism using new hybrid grasshopper optimization algorithm

et al. 2021

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Structural design and optimization are important topics for the control and design of industrial robots. The motivation behind this research is to design a robot gripper mechanism. To explore robust design of the robot gripper mechanism, a new optimization approach based on a grasshopper optimization algorithm and Nelder–Mead algorithm is developed for requiring a fast and accurate solution. Additionally, a vehicle side crash design problem, a multi‐clutch disc problem, and a manufacturing optimization problem are solved with the developed method to show the advantages of the new technique (HGOANM). Both engineering comparisons and production problem results in which HGOANM is applied are compared with the latest optimization techniques in the literature. The results of the problems resolved in this article reveal that the developed HGOANM is an essential optimization approach by solving real‐world engineering problems quickly and accurately.

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Section: Optimization Model Of the Grinding Processmentioning

confidence: 71%

Robust design of a robot gripper mechanism using new hybrid grasshopper optimization algorithm

et al. 2021

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Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model

Srinivasan

Zhao

Liew

2008

Hydrological Processes

143

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Abstract:With the popularity of complex hydrologic models, the time taken to run these models is increasing substantially. Comparing and evaluating the efficacy of different optimization algorithms for calibrating computationally intensive hydrologic models is becoming a nontrivial issue. In this study, five global optimization algorithms (genetic algorithms, shuffled complex evolution, particle swarm optimization, differential evolution, and artificial immune system) were tested for automatic parameter calibration of a complex hydrologic model, Soil and Water Assessment Tool (SWAT), in four watersheds. The results show that genetic algorithms (GA) outperform the other four algorithms given model evaluation numbers larger than 2000, while particle swarm optimization (PSO) can obtain better parameter solutions than other algorithms given fewer number of model runs (less than 2000). Given limited computational time, the PSO algorithm is preferred, while GA should be chosen given plenty of computational resources. When applying GA and PSO for parameter optimization of SWAT, small population size should be chosen.

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Modeling and Optimization of Machining Processes

Rao

2010

Springer Series in Advanced Manufacturing

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RETRACTED: Optimization of surface grinding operations using a differential evolution approach

Cited by 34 publications

References 12 publications

Robust design of a robot gripper mechanism using new hybrid grasshopper optimization algorithm

Robust design of a robot gripper mechanism using new hybrid grasshopper optimization algorithm

Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model

Modeling and Optimization of Machining Processes

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