Multi-Objective Optimization of Two-Stage Thermo-Electric Cooler Using Differential Evolution

Abstract

In this chapter, the technical issues of two-stage TEC were discussed. After that, a new method of optimizing the dimension of TECs using differential evolution to maximize the cooling rate and coefficient of performance was proposed. A input current to hot side and cold side of and the number ratio between the hot stage and cold stage are searched the optima solutions. Thermal resistance is taken into consideration. The results of optimization obtained by using differential evolution were validated by comparing with those obtained by using genetic algorithm and show better performance in terms of stability, computational efficiency, robustness. This work revealed that differential evolution more stable than genetic algorithm and the Pareto front obtained from multi-objective optimization balances the important role between cooling rate and coefficient of performance.

The Hybrid Harris Hawks Optimizer-Arithmetic Optimization Algorithm: A New Hybrid Algorithm for Sizing Optimization and Design of Microgrids

“…Combining two or more algorithms to solve the similar problems has been the preferred and an effective way of obtaining the greater overall performance. Therefore, the starting point of hybrid concept is to increase the performance of individually working algorithms in a hybrid structure and to solve the problems with better accuracy and faster convergence rate in a more efficient manner [4]- [6].…”