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Nowadays, an automotive DI Diesel engine is demanded to provide an adequate power output together with limit-complying NOx and soot emissions so that the development of a specific combustion concept is the result of a trade-off between conflicting objectives. In other words, the development of a low-emission DI diesel combustion concept could be mathematically represented as a multi-objective optimization problem. In recent years, genetic algorithm and CFD simulations were successfully applied to this kind of problem. However, combining GA optimization with actual CFD-3D combustion simulations can be too onerous since a large number of simulations is usually required, resulting in a high computational cost and, thus, limiting the suitability of this method for industrial processes.

Combustion control is assuming a crucial role in reducing engine tailpipe emissions and maximizing performance. The number of actuations influencing the combustion is increasing, and, as a consequence, the control parameters calibrations is becoming challenging. One of the most effective factors influencing performance and efficiency is the combustion phasing: gasoline engines Electronic Control Units (ECU) manage the Spark Advance (SA) in order to set the optimal combustion phase. SA optimal values are usually determined by means of calibration procedures carried out on the test bench by changing SA values while monitoring Brake and Indicated Mean Effective Pressure (BMEP, IMEP), Brake Specific Fuel Consumption (BSFC) and pollutant emissions. The effect of SA on combustion is stochastic, due to the cycle-to-cycle variation: the analysis of mean values requires many engine cycles to be significant of the performance obtained with the given control setting.