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A New Method for Multiobjective Optimization Based on Learning Automata
Abstract:The necessity of solving multi dimensional complicated scientific problems beside the necessity of several objective functions optimization are the most motive reason of born of artificial intelligence and heuristic methods. In this paper, we introduce a new method for multiobjective optimization based on learning automata. In the proposed method, search space divides into separate hyper-cubes and each cube is considered as an action. After gathering of all objective functions with separate weights, the cumulative function is considered as the fitness function. By the application of all the cubes to the cumulative function, we calculate the amount of amplification of each action and the algorithm continues its way to find the best solutions. In this Method, a lateral memory is used to gather the significant points of each iteration of the algorithm. Finally, by considering the domination factor, pareto front is estimated. Results of several experiments show the effectiveness of this method in comparison with genetic algorithm based method.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1056404Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1397
 C.A.C. Coello, "A comprehensive survey of evolutionary-based multiobjective optimization techniques," Knowledge and Information Science 1 (1999) 269-308.
 C.M. Fonseca, P.J. Fleming, "An overview of evolutionary algorithms in multiobjective optimization," Evolutionary Computation 3 (1995) 1-16.
 E. Zitzler, "Evolutionray algorithms for mulriobjective optimization: methods and applications," Ph.D. Tesis, Swiss Federal Institute of Technology, Zurich, 1999.
 M. Reyes-Sierra, Coello C.A.C., "Multi-objective Particle Swarm Optimizers: A Survey of State-of-the-Art," Intl. J. of Copmut. Intell. Res. (3) (2006) 287-308.
 B.J. Oommen, E.V. de St. Criox, "Graph partitioning using learning automata," IEEE Trans. Comput. 45 (1996) 195-208.
 X. Zeng, J. Zhou, C. Vasseur, "A strategy for controlling non-linear systems using a learning automaton," Automatica 36 (2000) 1517-1524.
 C.K.K. Tang P. Mars, "Games of stochastic learning automata and adaptive signal processing," IEEE Trans. Syst., Man, Cyber. 23 (1993) 851-856.
 X. Zeng, Z. Liu, "A learning automaton based algorithm for optimization of continuous complex function," Inform. Sci. 174 (2005) 165-175.
 H. Beygi, M.R. Meybodi, "A new action-set learning automaton for function optimization," Int. J. of the Franklin Inst. 343 (2006) 27-47.
 Y. Jin, M. Olhofer, and B. Sendho,"Dynamic weighted aggregation for evolutionrary multiobjective optimization: Why dose it work and how?" In Proc. GECCO 200.
 E. Zitzler, K. Deb, and L. Thiele,"Comparison of multiobjective evolution algorithms: empirical results," Evolutionary Computation, 8(2): 173-195, 2000.