Comparison of multi-objective optimisation approaches for inverse magnetostatic problems

Purpose - The aim of the paper is to compare two different approaches to multi-objective optimisation in magnetostatics; in this way, the case study is investigated as a possible benchmark.Design methodology approach - A Tabu search method modified with -constraint algorithm is compared with a multi...

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Vydáno v:Compel Ročník 26; číslo 2; s. 293 - 305
Hlavní autoři: Carcangiu, S., Di Barba, P., Fanni, A., Mognaschi, M.E., Montisci, A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bradford Emerald Group Publishing Limited 01.01.2007
Témata:
Algorithms
Approximation
Benchmarks
Electrical engineering
Electromagnetism
Mathematical models
Neural networks
Optimization techniques
Statistical analysis
Studies
Neural nets
Optimization techniques
Benchmarking
ISSN:0332-1649, 2054-5606
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Shrnutí:Purpose - The aim of the paper is to compare two different approaches to multi-objective optimisation in magnetostatics; in this way, the case study is investigated as a possible benchmark.Design methodology approach - A Tabu search method modified with -constraint algorithm is compared with a multi-objective multi-individual evolution strategy. The case study is the automated shape design of a magnetic pole. In order to reduce the computational cost of solving the direct problem, which requires repeated analyses of the magnetic field, a neural network has been used to approximate the objective functions that depend on the design variables.Findings - An approximation of the Pareto front for each method is obtained. A twofold comparison between the two methods is made, based on both the result accuracy and the computational cost.Originality value - Two different methods were already tested on a case study proposed as a benchmark for multi-objective optimization in magnetostatics. The paper represents a contribution to bridge the gap between analytical and numerical benchmarks in electromagnetism.
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ISSN:0332-1649
2054-5606
DOI:10.1108/03321640710727656