Multi-objective Optimization in Geometric Design of Active Magnetic Bearing based on Force Slew Rate, Overall Volume, and Total Losses Considerations through Genetic Algorithms

Yadav, Vinay Kumar; Kumar, Parveen; Bhushan, Gian

doi:10.1007/s40032-021-00746-z

Cited by 5 publications

(1 citation statement)

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“…Yadav et al used a multiobjective genetic algorithm to optimize the rotational speed of the equipment. He determined optimal values of design parameters related to active magnetic bearing (AMB) geometry and electromagnetic actuators [14]. However, the experimental process is not rigorous enough, ignoring the influence of different environments.…”

Section: Related Workmentioning

confidence: 99%

Application of Multiobjective Optimization Integrating Numerical and Scientific Computing in Graph Theory Coloring Algorithm

2022

Mathematical Problems in Engineering

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In the current era of increasingly complex social life, as people’s demand quality is getting higher and higher, the solution of the problem often has multiple indicators to achieve the optimum. This forces the graph coloring problem (GCP) to become complicated, and it is difficult to directly obtain the optimal solution, which brings new challenges to the solution of the problem. In response to this problem, the GCP field is of great research significance. With the in-depth study of GCP, the research on multiobjective optimization (MOO) in graph coloring algorithm (GCA) is gradually carried out. Its performance advantage is of great significance for solving multicondition constraint problems. This paper aims to study the application of Genetic Algorithm (GA) in GCA. Through the analysis and research of GA, and the fusion of numerical analysis and scientific computing, it can be applied to the construction of the neighbor distinguishable uniform V-full coloring algorithm (AVDEVTCA) to solve the AVDEVTC problem. This paper describes the basic theory of MOO and graph coloring. It conducts an experimental analysis of the algorithm performance and uses the relevant theoretical formulas to explain it. The results show that the algorithm takes 5754.142 S seconds to test 21325415 images and can color a large number of results that cannot be done manually in less time. It has greatly improved in terms of time and manpower saving and greatly improved practicability and work efficiency.

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Section: Related Workmentioning

confidence: 99%