Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization 
algorithms

Abstract

This study explores the use of teaching-learning-based optimization (TLBO) and artificial bee colony (ABC) algorithms 
for determining the optimum operating conditions of combined Brayton and inverse Brayton cycles. Maximization of thermal 
efficiency and specific work of the system are considered as the objective functions and are treated simultaneously 
for multi-objective optimization. Upper cycle pressure ratio and bottom cycle expansion pressure of the system are 
considered as design variables for the multi-objective optimization. An application example is presented to demonstrate 
the effectiveness and accuracy of the proposed algorithms. The results of optimization using the proposed algorithms are 
validated by comparing with those obtained by using the genetic algorithm (GA) and particle swarm optimization (PSO) on 
the same example. Improvement in the results is obtained by the proposed algorithms. The results of effect of variation 
of the algorithm parameters on the convergence and fitness values of the objective functions are reported.