This study demonstrates the outcomes of a research implement for the power and efficiency optimisation of a Rankine cycle heat engine employing the non-dominated sorting genetic algorithm (NSGA-II) algorithm. Two objective functions comprising the efficiency and power were included concurrently maximised. To assess this idea, multi-objective optimisation approach founded on NSGA-II method has been utilised in which following variables have been considered as decision variables: (1) the inlet temperatures of a heat source, (2) the inlet temperatures of a heat sink, (3) temperature difference (x), (4) temperature difference (y), (5) heat conductance and (6) heat capacitance. By applying the addressed multi-objective optimisation approach, Pareto optimal frontier was determined and utilising different decision-making techniques that include the LINMAP, TOPSIS and fuzzy Bellman–Zadeh approaches help us to figure out the final optimal solution.
