Design Optimization of Heat Pipes Using Elitism-Based Self-Adaptive Multipopulation Jaya Algorithm

Abstract

In this paper, an elitism-based self-adaptive multipopulation Jaya algorithm is proposed for the design optimization of heat pipes. The design of a heat pipe involves a number of geometric and physical parameters with high complexity. These processes are based on trial and error. General design approaches become tedious and time consuming. These processes do not guarantee the achievement of an optimal design. Therefore, metaheuristic-based computational methods are preferred. Hence, this study proposes a new optimization method for the design optimization of heat pipes using the elitism-based self-adaptive multipopulation Jaya algorithm. The search mechanism of the Jaya algorithm is enhanced in this work by using the multipopulation search scheme with elitism, which searches for the optimal solution in different search spaces of the problem simultaneously. This method is used for the single as well as multi-objective design optimizations of heat pipes. Minimization of the total mass of the heat pipe is considered as a single-objective problem with an array of working fluids, namely, methanol, ethanol, and ammonia. Furthermore, maximization of heat transfer rate and minimization of thermal resistance of a heat pipe are carried out simultaneously by using a priori approach. The results obtained by the elitism-based self-adaptive multipopulation Jaya algorithm are found superior as compared to the teaching-learning-based optimization, global best algorithm, Jaya, and self-adaptive multipopulation Jaya algorithms in the case of single-objective optimization, and in the case of multi-objective optimization it is found superior compared to the niched Pareto genetic algorithm, grenade-explosion method, teaching/learning-based optimization, Jaya, self-adaptive Jaya, and self-adaptive multipopulation Jaya algorithms.