A novel particle swarm and genetic algorithm hybrid method for diesel engine performance optimization

Abstract

Heuristic methods have been a successful tool for optimizing engine parameters in both simulation and experimental testing. An improved hybrid method applying both the particle swarm optimization method and genetic algorithm was developed, tested, and compared with a basic particle swarm method for improving engine emissions and performance. A computational comparison between the particle swarm optimization–genetic algorithm hybrid, basic particle swarm optimization, and basic genetic algorithm was done using standard test problems. Computational results indicated improvements in both the efficiency and effectiveness of the present hybrid method. Engine testing was performed under steady-state conditions at 1400 r/min at 4.15 bar brake mean effective pressure. The basic particle swarm optimization and the hybrid particle swarm optimization–genetic algorithm method were applied to the test apparatus and used to locate the optimum neighborhood of the engine operation. A single-objective function representing NOx, particulate matter, hydrocarbon, CO, and fuel consumption was used in this application. The hybrid method was able to locate a narrow window of operation which showed 27% lower NOx emissions and 60% lower particulate matter emissions than the standard particle swarm optimization method. The hybrid method was able to locate the improvements using similar dynamometer time, indicating that the hybrid method is more efficient and more effective. Trends relating combustion characteristics and input parameters were observed and are discussed with regard to future improvements to heuristic methods for optimizing diesel engine performance.