Details
| Original language | English |
|---|---|
| Pages (from-to) | 144-157 |
| Number of pages | 14 |
| Journal | COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering |
| Volume | 39 |
| Issue number | 1 |
| Publication status | Published - 7 Jan 2020 |
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
ASJC Scopus subject areas
- Mathematics(all)
- Applied Mathematics
- Engineering(all)
- Electrical and Electronic Engineering
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Computational Theory and Mathematics
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In: COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol. 39, No. 1, 07.01.2020, p. 144-157.
Research output: Contribution to journal › Article › Research
}
TY - JOUR
T1 - A novel multi-surrogate multi-objective decision-making optimization algorithm in induction heating
AU - Baldan, Marco
AU - Nikanorov, Alexandre
AU - Nacke, Bernard
PY - 2020/1/7
Y1 - 2020/1/7
N2 - 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.
AB - 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.
KW - Finite element analysis
KW - Induction heating
KW - Multiobjective optimization
KW - Optimal control
UR - http://www.scopus.com/inward/record.url?scp=85077588933&partnerID=8YFLogxK
U2 - 10.1108/COMPEL-05-2019-0222
DO - 10.1108/COMPEL-05-2019-0222
M3 - Article
VL - 39
SP - 144
EP - 157
JO - COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering
JF - COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering
SN - 0332-1649
IS - 1
ER -