Multiobjective Optimization of the Benchmark TEAM Problem Using Gradient-Based Approach and Adaptive Weight Determination

In this article, a new approach is proposed to find Pareto optimum solutions for the benchmark TEAM problem by using a gradient-based optimization algorithm and an adaptive weight determination scheme. An air-cored multiturn winding problem with three conflicting objective functions is considered, a...

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Bibliographic Details
Published in:IEEE transactions on magnetics 2020-03, Vol.56 (3), p.1-4
Main Authors: Ryu, Namhee, Seo, Minsik, Min, Seungjae
Format: Article
Language:English
Subjects:
Adaptive algorithms
Adaptive weights
Benchmark testing
Benchmarks
Classification
Genetic algorithms
gradient-based optimization
Linear programming
Magnetism
multiobjective optimization
Multiple objective analysis
Optimization
Pareto front
Pareto optimization
Pareto optimum
Programming
Sensitivity
Shape
Sorting algorithms
Weight
Windings
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Summary:In this article, a new approach is proposed to find Pareto optimum solutions for the benchmark TEAM problem by using a gradient-based optimization algorithm and an adaptive weight determination scheme. An air-cored multiturn winding problem with three conflicting objective functions is considered, and the solutions are obtained using the sensitivity information of the objective functions. Using the adaptive weight determination scheme, the values of the different weights are updated iteratively during the optimization process, and evenly distributed solutions are gradually obtained in the objective space. To evaluate the effectiveness of the proposed approach, the obtained solutions are quantitatively compared with those of the nondominated sorting genetic algorithm, and it is confirmed that more diverse and evenly distributed solutions could be obtained using the proposed approach.
ISSN:0018-9464
1941-0069
DOI:10.1109/TMAG.2019.2957567