Khalkhali - Department of Mechanical Engineering, Faculty of Engineering, The University of Guilan
Atashkari - Department of Mechanical Engineering, Faculty of Engineering, The University of Guilan
Nariman-zadeh - Department of Mechanical Engineering, Faculty of Engineering, The University of Guilan
Jamali - Department of Mechanical Engineering, Faculty of Engineering, The University of Guilan

Several investigations have demonstrated that improvements, at part-load conditions, in engine performance can be accomplished if the valve-timing is variable. Controlling valve-timing can be used to improve torque and power curve as well as to reduce fuel consumption and emissions. In this study , group method of data handling (GMDH)-type neural networks and evolutionary algorithms (EAs) are firstly used for modelling of the effects of intake valve-timing and engine-speed (N) of a spark-ignition engine on both developed engine-torque (T) and fuel consumption (Fc), using some experimentally obtained training and test data. Using such obtained polynomial neural network models, multi-objective EAs (non-dominated sorting genetic algorithm, NSGA-II) with a new diversity preserving mechanism are secondly used for Pareto-based optimization of the variable valve-timing engine considering two conflicting objectives such as torque (T) and fuel consumption (Fc).

engine, GMDH, Multi-objective optimization, GA, Variable valve-timing