A novel multi-surrogate multi-objective decision-making optimization algorithm in induction heating

verfasst von: Marco Baldan, Alexandre Nikanorov, Bernard Nacke
Abstract: Purpose: Most of optimal design or control engineering problems present conflicting objectives that need to be simultaneously minimized or maximized. Often, however, it is a priori known that some functions have greater importance than other. This paper aims to present a novel multi-surrogate, multi-objective, decision-making (DM) optimization algorithm, which is suitable for time-consuming simulations. Its performances have been compared, on the one hand with a standard decision-making algorithm (iTDEA), on the other with a self-adaptive evolutionary algorithm (AMALGAM*). The comparison concerns numerical tests and an optimal control task in induction heating. Design/methodology/approach: In particular, the algorithm makes use of surrogates (meta-models) to concentrate the field evaluations at the most promising areas of the design space. The effect of the decision-maker is instead to drive the search to given regions of the Pareto front. The synergy between surrogates and the decision-maker leads to a greater effectiveness of the optimization search. For the field analysis of the optimal control task, a coupled electromagnetic-thermal FEM model has been developed. Findings: The novel algorithms outperform both iTDEA and AMALGAM* in all done tests. Practical implications: The algorithm could be applied to other computationally intensive multi-objective real-life problems whenever a preference between the objectives is known. Originality/value: The combination of surrogates and a decision-maker is beneficial with time-consuming multi-objective optimization problems.
Organisationseinheit(en): Institut für Elektroprozesstechnik
Typ: Artikel
Journal: COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering
Band: 39
Seiten: 144-157
Anzahl der Seiten: 14
ISSN: 0332-1649
Publikationsdatum: 07.01.2020
Publikationsstatus: Veröffentlicht
ASJC Scopus Sachgebiete: Angewandte Mathematik, Elektrotechnik und Elektronik, Angewandte Informatik, Theoretische Informatik und Mathematik
Elektronische Version(en): https://doi.org/10.1108/COMPEL-05-2019-0222 (Zugang: Geschlossen)

BibTeX

@article{313b9eeb71a84444b29bb0df3956a3d9,
title = "A novel multi-surrogate multi-objective decision-making optimization algorithm in induction heating",
abstract = "Purpose: Most of optimal design or control engineering problems present conflicting objectives that need to be simultaneously minimized or maximized. Often, however, it is a priori known that some functions have greater importance than other. This paper aims to present a novel multi-surrogate, multi-objective, decision-making (DM) optimization algorithm, which is suitable for time-consuming simulations. Its performances have been compared, on the one hand with a standard decision-making algorithm (iTDEA), on the other with a self-adaptive evolutionary algorithm (AMALGAM*). The comparison concerns numerical tests and an optimal control task in induction heating. Design/methodology/approach: In particular, the algorithm makes use of surrogates (meta-models) to concentrate the field evaluations at the most promising areas of the design space. The effect of the decision-maker is instead to drive the search to given regions of the Pareto front. The synergy between surrogates and the decision-maker leads to a greater effectiveness of the optimization search. For the field analysis of the optimal control task, a coupled electromagnetic-thermal FEM model has been developed. Findings: The novel algorithms outperform both iTDEA and AMALGAM* in all done tests. Practical implications: The algorithm could be applied to other computationally intensive multi-objective real-life problems whenever a preference between the objectives is known. Originality/value: The combination of surrogates and a decision-maker is beneficial with time-consuming multi-objective optimization problems.",
keywords = "Finite element analysis, Induction heating, Multiobjective optimization, Optimal control",
author = "Marco Baldan and Alexandre Nikanorov and Bernard Nacke",
year = "2020",
month = jan,
day = "7",
doi = "10.1108/COMPEL-05-2019-0222",
language = "English",
volume = "39",
pages = "144--157",
journal = "COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering",
issn = "0332-1649",
publisher = "Emerald Group Publishing Ltd.",
number = "1",
}