Authors N.A. Polkovnikova, V.M. Kureichik
Month, Year 02, 2015 @en
Index UDC 004.891
Abstract 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.

Download PDF

Keywords Evolutionary process; multiobjective optimization; front Pareto; genetic algorithm; decision support system.
References 1. Vasil'ev V.I., Il'yasov B.G. Intellektual'nye sistemy upravleniya. Teoriya i praktika: Uch. posobie [Intelligent control systems. Theory and practice: textbook]. Moscow: Radiotekhnika, 2009, 392 p.
2. Makarov I.M., Lokhin V.M., Man'ko S.V., Romanov M.P. Iskusstvennyy intellekt i intellektual'nye sistemy upravleniya [Artificial intelligence and intelligent control systems]. Moscow: Nauka, 2006, 333 p.
3. Kureychik V.V., Kureychik V.M., Rodzin S.I. Teoriya evolyutsionnykh vychisleniy [The theory of evolutionary computation]. Moscow: Izdatel'skaya firma «Fiziko-matematicheskaya literatura», 2012, 260 p.
4. Polkovnikova N.A. Mnogokriterial'naya optimizatsiya na osnove evolyutsionnykh algoritmov v sisteme podderzhki prinyatiya resheniy [Multiobjective optimization-based evolutionary algorithms in the system of decision support], Matematicheskoe i komp'yuternoe modelirovanie:
materialy Pervoy mezhdunarodnoy nauchno-prakticheskoy konferentsii (5–7 sentyabrya 2014 g.) [Mathematical and computer modeling: proceedings of the First international scientificpractical conference (5-7 September 2014)]. Novorossiysk: GMU im. admirala F.F. Ushakova, 2014, pp. 32-34.
5. Polkovnikova N.A., Kureichik V.M. Hybrid expert system development using computer-aided software engineering tools, Knowledge-Based Software Engineering 11th Joint Conference, JCKBSE 2014, Volgograd, Russia, September 17-20, 2014. Springer, 2014, Vol. 466, pp. 433-445.
6. Smits G., Kotanchek M. Pareto-Front exploitation in symbolic regression, Genetic programming theory and practice II, chapter 17, 2005. Springer, 2005, pp. 283-299.
7. Hohm T., Zitzler E. A Multiobjective evolutionary algorithm for numerical parameter space characterization of reaction diffusion systems, International Conference on Pattern Recognition in Bioinformatics (PRIB 2009), Heidelberg, Germany, 2009. Springer. pp. 162-174.
8. Zitzler E., Laumanns M., Thiele L. SPEA2: Improving the Strength Pareto Evolutionary algorithm for multiobjective optimization, Evolutionary Methods for Design, Optimization, and Control, 2002, pp. 95-100.
9. Brockhoff D., Zitzler E. Objective reduction in evolutionary multiobjective optimization: theory and applications, Evolutionary Computation, 2009, No. 17(2), pp. 135-166.
10. Siegfried T., Bleuler S., Laumanns M., Zitzler E., Kinzelbach W. Multi-Objective Groundwater Management Using Evolutionary Algorithms, IEEE Transactions on Evolutionary Computation, 2009, No. 13(2), pp. 229-242.
11. Xiao-Bing Hu, M. Wang, E. Di Paolo. Calculating complete and exact Pareto front for multiobjective optimization: a new deterministic approach for discrete problems, IEEE Transactions on cybernetics, 2013, Vol. 43, No. 3, pp. 1088-1101.
12. El-Ghazali Talbi. Metaheuristics from design to implementation – 2009, Wiley, 593 p.
13. Mukhopadhyay A., Maulik U., Bandyopadhyay S. Multiobjective genetic algorithm-based fuzzy clustering of categorical attributes, IEEE transactions on evolutionary computation, 2009, Vol. 13, No. 5, pp. 991-1005.
14. Kenneth A. De Jong. Evolutionary computation. A unified approach. The MIT Press, 2006, 256 p.
15. Antonov A.V. Sistemnyy analiz: Ucheb. dlya vuzov [System analysis: the Textbook for high schools]. 2 nd ed. Moscow: Vyssh. shk., 2006, 454 p.

Comments are closed.