Design optimization and analysis of selected thermal devices using self-adaptive Jaya algorithm

• Published in: Energy conversion and management 2017-05, Vol.140, p.24-35
• Main Authors: Rao, R.V., More, K.C.
• Format: Article
• Language: English
• Subjects: Adaptive algorithms; Adaptive control; Case studies; Computer applications; Cooling towers; Design; Design optimization; Genetic algorithms; Honeycomb construction; Jaya algorithm; Machine learning; Multiple objective analysis; Optimization; Pareto optimization; Response surface methodology; Self-adaptive Jaya algorithm; Thermal devices
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2017.02.068

•Self-adaptive Jaya algorithm is proposed for optimal design of thermal devices.•Optimization of heat pipe, cooling tower, heat sink and thermo-acoustic prime mover is presented.•Results of the proposed algorithm are better than the other optimization techniques.•The proposed algorithm may be conveniently used for the optimization of other devices. The present study explores the use of an improved Jaya algorithm called self-adaptive Jaya algorithm for optimal design of selected thermal devices viz; heat pipe, cooling tower, honeycomb heat sink and thermo-acoustic prime mover. Four different optimization case studies of the selected thermal devices are presented. The researchers had attempted the same design problems in the past using niched pareto genetic algorithm (NPGA), response surface method (RSM), leap-frog optimization program with constraints (LFOPC) algorithm, teaching-learning based optimization (TLBO) algorithm, grenade explosion method (GEM) and multi-objective genetic algorithm (MOGA). The results achieved by using self-adaptive Jaya algorithm are compared with those achieved by using the NPGA, RSM, LFOPC, TLBO, GEM and MOGA algorithms. The self-adaptive Jaya algorithm is proved superior as compared to the other optimization methods in terms of the results, computational effort and function evalutions.