Multi-objective optimization of the Stirling heat engine through self-adaptive Jaya algorithm

Abstract

A Stirling engine presents an excellent theoretical output equivalent to the output of a Carnot engine, and it emits low levels of pollution and requires less maintenance. In this paper, a solar-powered Stirling engine is considered for optimization with multiple criteria. Hence, the present study explores the use of an improved Jaya algorithm called the self-adaptive Jaya algorithm and the basic Jaya algorithm, which are used for maximizing output power and thermal efficiency and minimizing pressure losses of the entire Stirling system. The key feature is that the self-adaptive Jaya algorithm automatically determines the population size. Hence, the user does not need to concentrate on choosing the population size. The comparison of the proposed algorithm is made with those obtained by using the non-dominated sorting genetic-algorithm-II (NSGA-II), an optimizer inbuilt in MATLAB (function gamultiobj), Front-based Yin-Yang-Pair Optimization (FYYPO), Multi-Objective Grey Wolf Optimizers (MOGWOs), Teaching–Learning-Based Optimization (TLBO), tutorial training and self-learning inspired teaching-learning based optimization (TS-TLBO), the decision-making methods like linear programming technique for multi-dimensional analysis of preference (LINMAP), technique for order of preference by similarity to ideal solution (TOPSIS), and Bellman-Zadeh, and experimental results. The results achieved by using Jaya and self-adaptive Jaya algorithms are compared with the results achieved by using the NSGA-II, gamultiobj, FYYPO, MOGWO, TLBO, TS-TLBO, LINMAP, TOPSIS, Bellman-Zadeh, and experimental results. The Jaya algorithm and its improved version are proved to be better compared to other optimization algorithms with respect to the computational effort and function evaluations.