Solar assisted heat engine systems: multi-objective optimisation and decision making

Abstract

Identifying, analysing and optimising the effect of various parameters of a solar assisted heat engine system contribute significantly in assessing the system's performance. Optimum parameters can be identified by performing multi-objective optimisation (MOO) considering multiple performance factors. Usually, non-dominated solutions set can be found for these MOO problems and selecting a most suitable solution from non-dominated solutions set is difficult. Hence, in this study, the applicability of multi attribute decision-making methods in MOO problems has been discussed and a decision making procedure is suggested on the basis of average rank to identify the best solution in a Pareto front. Two MOO case-studies of solar-assisted engine systems are optimised by using the multi-objective-Jaya-algorithm and its variants known as multi-team perturbation guiding Jaya algorithm and adaptive multi-team prturbation guiding Jaya algorithm. In both the case studies, the proposed algorithms have improved the performance of the considered systems. Furthermore, the performances of the proposed algorithms are compared using the coverage, spacing, and hypervolume indicators.