Multi-objective optimization using bat algorithm for shell and tube heat exchangers

Optimization is carried out based on multivariable functions such as effectiveness and total cost. These two objective functions are plotted by considering various design parameters such as baffle cuts, baffle spacing's, pitch, tube length, tube layout pattern in order to get Pareto optimal solutions and it captures the trade-off between those two objectives. This multi-objective optimization is performed using a novel computational technique called bat algorithm. This algorithm is based on echolocation features of micro bats and frequency tuning technique to increase the diversity of the solutions in the population. It proves to be very efficient with a typical start. In order to verify the algorithm a case study has been taken from the open literature. This paper also presents the results of genetic algorithm for different tube layout patterns. The Pareto fronts obtained from both genetic algorithm and bat algorithm are found to be similar in nature. From the Pareto optimal solution of the bat algorithm the cost is lowered by a maximum of 13.7% and minimum of 9.2%. It is found that the effectiveness has been reduced by about 3% when the cost minimization taken place. (C) 2016 Elsevier Ltd. All rights reserved.