Metaheuristic based single and multiobjective optimization of thermoelectric generator

Abstract

Analytical optimization methods are not applicable for some complex practical problems that lead to explore the numerical/metaheuristic optimization methods. Thermoelectric devices are also complex systems and need metaheuristic methods for their performance optimization. This paper analyses various metaheuristic techniques for performance optimization of thermoelectric generator (TEG). Single and multiobjective optimizations of TEG have been performed to optimize its power output and efficiency. The hot and cold side temperatures, load resistance, length of semiconductor legs, and fill factor of the TEG are used as performance parameters. Single-objective optimization is performed using 13-metaheuristic algorithms and their results are compared in terms of solution optimality and computational time acceptability. It has been observed that the cuckoo search (CS) algorithm is the most suitable in terms of accuracy and speed. CS produced the optimum power output and efficiency of 19.20837 W and 2.4427 % respectively in minimum time. The multiobjective optimization is also performed for simultaneous optimization of the power output and efficiency. Shannon entropy approach is found to be the best technique to determine optimal point on the Pareto front for multiobjective optimization. The optimal values of decision variables, Th Tc, RL, L and FF for multiobjective optimization are found to be 321.195 K, 281.195 K, 0.14250 Ω, 0.5 mm and 0.9 respectively and it produced optimum power output and efficiency of 19.20837 W and 2.25932 % respectively. The statistical and box plot analysis have also been carried out for all the algorithms. The determination of the most suitable algorithm for the faster performance optimization of the TEG, hereby provides the pathways to ascertain the most accurate solution for the power output and efficiency obtainable from the TEG system and hence, delivering the ability to decide the right parameters for the construction of the TEG system with the best performance.