An Adaptive Particle Swarm Optimization for Engine Parameter Optimization

An adaptive particle swarm optimization is proposed to improve the fuel efficiency and reduce exhaust emissions in diesel engine. The proposed algorithm introduces an adaptive inertia weight and modified mutation mechanism for velocity and particle update when the global best particle falls into possible local optima. The proposed PSO has been evaluated on 8 benchmark functions, together with other adaptive PSO algorithms. The results show the proposed algorithm has faster convergence, better solution accuracy and reliability in comparison with other algorithms. Diesel engine must meet the increasing demands for higher efficiency and cleaner exhaust gases, which is a multi-objective optimization problem. In the paper, diesel engine optimization problem is firstly converted to single objective optimization problem, and then the proposed PSO is adopted to find optimal engine operation parameters. Results demonstrate potential of future application in car.