A novel modified JAYA algorithm for heat exchanger optimization

In general, algorithm modification is changing or alternating some aspects of the original algorithms with improving their performances. This work aims to introduce and implement a novel modified Jaya algorithm (MJ) to optimize fins and tube heat exchangers. The objective functions used in the current work are to minimize total cost and maximize effectiveness. The optimization results of the MJ were compared with the standard JAYA algorithm and another two different algorithms, namely the Grey Wolf Optimizer (GWO) and Sine Cosine Algorithms (SCA), to examine the MJ performance improvement. A MATLAB inhouse code was used to obtain the results of the different optimizing algorithms. Each of the four algorithms optimized the heat exchanger at three different values of population size, which are 25, 50, and 100, and three different numbers of runs, 20, 40, and 80, to determine the optimal solution. The results showed that MJ outperforms the standard JAYA algorithm and SCA in all cases studied. MJ performs better than GWO at low and medium populations,25 and 50. Still, at a population size of 100, MJ and GWO perform equally, with the advantage that MJ obtains less average execution time to find optimal solutions than GWO. The time increase of GWO over MJ is 450.56% at maximum and 52.86% at minimum.