Performance Comparison of Particle Swarm Optimization and Genetic Algorithm in Rolling Fin-Tube Heat Exchanger Optimization Design

Abstract

A method for optimization designs of rolling fin-tube heat exchangers was put forward with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), respectively. The length of tube bundles, the row numbers of tubes, the width of heat exchanger core and fin pitch were used as the optimization variables. The allowable pressure drop and heat exchange requirements were considered as restrictive conditions. According to specific design requirements, the volume, weight or pressure drop may be chosen as the optimization objective function. In the same design parameters, ranges of the search variables and restrictive conditions, optimization results compared with GA, the minimum volume, weight and pressure drop PSO could decrease by 3.34%, 4.31% and 14.04%, respectively, and corresponding CPU time could be reduced by 32.39%, 40.23% and 33.45%, respectively. In the fields of optimization designs of heat exchanger, Particle Swarm Optimization is a promising optimization method.