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.