MULTIOBJECTIVE OPTIMIZATION ON THE BASE OF EVOLUTIONARY ALGORITHMS
The paper shows the solution of multi-criteria optimization for decision support using evolutionary algorithm that finds a Pareto-optimal front in order to minimize the two objective functions. Evolving optimization process of the fittest individuals creates moving Pareto front to the optimal set of solutions. Since the operator knows in advance which of the criteria is interested more, on the resulting Pareto front considered separate decisions, the best on most important criteria that can reduce and simplify the automated solution to the problem multi-criteria selection in decision making. Multiobjective optimization using the developed evolutionary algorithm is implemented to determine the fuel supply parameters of main marine engine at full load in order to obtain minimum value of two objective functions: the content of nitrogen oxides in exhaust gases and specific fuel consumption. The basic operations of multiobjective evolutionary algorithm in plotting the Pareto frontier are: crossing operations (crossover, recombination), mutations, fitness calculations and selection. The chromosome of modified evolutionary algorithm is a set of values of four fuel supply parameters. In the paper implemented modified algorithm SPEA2, obtained Pareto-optimal front that contains solutions to support operator in selection the operating mode of the main marine engine: with a minimum specific fuel consumption, with a minimum content of nitrogen oxides in exhaust gases or a compromise. However, final selection of the optimal values of injection parameters should be determined by operator, according to operating conditions of the engine.