Matching Optimization of Ship Engine and Propeller Based on PSO-GA Algorithm

Abstract

Matching performance of ship engine and propeller has a significant impact on marine propulsion efficiency. In this paper, a hybrid approach combining particle swarm optimization (PSO) and genetic algorithms (GA) is developed for matching optimization of ship engine and propeller. Based on ship theory, the matching performance of ship engine and propeller is analyzed. Considering the diameter, angular speed, picth ratio and disk ratio of propeller, a mathematical model is constructed in which the open-water propeller efficiency is taken as the objective function for matching optimization of ship engine and propeller. Integrating PSO with GA is presented to solve it, in which the mutation operator of GA is introduced to the PSO for the diversity of particles. The effectiveness of the approach is illustrated by a matching optimization example of ship engine and propeller.