Tütüncü, KemalAllahverdi, Novruz2020-03-262020-03-2620109.78145E+12https://dx.doi.org/10.1145/1839379.1839415https://hdl.handle.net/20.500.12395/2563911th International Conference on Computer Systems and Technologies, CompSysTech'10 -- 17 June 2010 through 18 June 2010 -- Sofia -- 81605In this study, single and also multi-objective (MO) genetic algorithms (GAs) were used for optimisation of performance and emissions of a diesel engine. Population space and initial population of both GAs were obtained by Artificial Neural Network (ANN). Specific fuel consumption (Sfc), NO x, power (P), torque (Tq) and air-flow rate (Afr) were reduced to %7.7, %8.51, %30, %4 and %7.4 respectively whereas HC increased at the rate of %10.5 by traditional single objective GA. HC, CO2, P and Sfc were reduced to %17.6, %30.05, %31.8 and %14.5 respectively whereas NOx increased at the rate of %13 by using multiobjective GA with Nondominated Sorting Genetic Algorithm II (NSGA II). %14.5 fuel reduction against %31 power reduction have never been obtained in the previous studies. This shows the effective usage of MOGA with NSGA II in optimisation of fuel diesel engine performance parameters. Copyright © 2010 ACM.en10.1145/1839379.1839415info:eu-repo/semantics/closedAccessANNDiesel engine performanceMulti-objective genetic algorithmNSGA IIOptimizationConference Object471197204N/A