A novel multi-surrogate multi-objective decision-making optimization algorithm in induction heating. Issue 1 (7th January 2020)

• Record Type: Journal Article
• Authors: Baldan, Marco; Nikanorov, Alexander; Nacke, Bernard
• Is Part Of: Compel. Volume 39:Issue 1(2020)
• Page Start: 144
• Page End: 157
• Publication Date: 2020-01-07
• Subjects: Induction heating -- Optimal control -- Finite element analysis -- Multiobjective optimization; Electrical engineering -- Data Processing -- Periodicals; Electrical engineering -- Mathematics -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Data Processing -- Periodicals; Electronics -- Mathematics -- Periodicals; 621.3
• DOI: 10.1108/COMPEL-05-2019-0222
• Languages: English
• ISSNs: 0332-1649

Abstract

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.