Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Hi-index | 0.00 |
Thermoelectric devices are indeed device of future as they are green cooling devices. Tough still under research, performance of these devices is main concern to engineers for their suitability for practical use. In the present work, the two main concern i.e. Coefficient of Performance (COP) and Rate of Refrigeration (ROR) of such devices are simultaneously addressed. NSGA-II is used for finding Pareto-optimal solutions under three different settings for ambient conditions. Mathematical model is considered and effect of ambient conditions on optimal performance is also highlighted.The results of optimization are verified by theoretical governing equations for Thermo-Electric Coolers (TEC). It is concluded that Bi-Objective optimization of performance of single stage TEC is possible, relevant and have huge potential for practical use by designers of TEC.