Rao algorithms for multi-objective optimization of selected thermodynamic cycles

This work proposes multi-objective Rao algorithms. The basic Rao algorithms are modified for solving multi-objective optimization problems. The proposed algorithms have no algorithm-specific parameters and no metaphorical meaning. Based on the interaction of the population with best, worst, and randomly selected solutions, the proposed algorithms explore the search space. The proposed algorithms handle multiple objectives simultaneously based on dominance principles and crowding distance evaluation. In addition, multi-attribute decision-making method-based selection scheme for identifying the best solutions from the Pareto fronts is included. The proposed algorithm performances are investigated on a case study of solar-assisted Brayton heat engine system and a case study of Stirling heat engine system to see whether there can be any improvement in the performances of the considered systems. Furthermore, the efficiencies of the Rao algorithms are evaluated in terms of spacing, hypervolume, and coverage metrics. The results obtained by the proposed algorithms are compared with those obtained by the latest advanced optimization algorithms. It is observed that the results obtained by the proposed algorithms are superior. The performances of the considered case studies are improved by the application of the proposed optimization algorithms. The proposed optimization algorithms are simple, robust, and can be easily implemented to solve different engineering optimization problems.