Multi-objective Thermal Exchange Optimization algorithm applied to mechanical system design

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Abstract
Engineering system design is a highly relevant and dynamic field, with numerous applications reported in the literature. This type of problem generally encompasses several objectives and constraints, including those arising from mass, energy, and momentum balances, material behavior equations, and a range of environmental, physical, and operational restrictions. To address such challenges, a range of evolutionary optimization strategies has been proposed and evaluated. This work presents a Multi-objective Thermal Exchange Optimization (MTEO) algorithm that integrates concepts of Pareto dominance along with crowding distance strategies. To assess its performance, the proposed algorithm is applied to three well-established mechanical design problems. The results demonstrate that the MTEO algorithm provides accurate approximations of the Pareto front in comparison with conventional evolutionary methods. Specifically, average reductions of approximately 36%, 32%, and 68% were observed for the respective design problems. Furthermore, the MTEO parameters were found to be easy to configure across all applications.

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