A New Method for Multiobjective Optimization Based on Learning Automata
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.
[1] C.A.C. Coello, "A comprehensive survey of evolutionary-based
multiobjective optimization techniques," Knowledge and Information
Science 1 (1999) 269-308.
[2] C.M. Fonseca, P.J. Fleming, "An overview of evolutionary algorithms in
multiobjective optimization," Evolutionary Computation 3 (1995) 1-16.
[3] E. Zitzler, "Evolutionray algorithms for mulriobjective optimization:
methods and applications," Ph.D. Tesis, Swiss Federal Institute of
Technology, Zurich, 1999.
[4] 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.
[5] B.J. Oommen, E.V. de St. Criox, "Graph partitioning using learning
automata," IEEE Trans. Comput. 45 (1996) 195-208.
[6] X. Zeng, J. Zhou, C. Vasseur, "A strategy for controlling non-linear
systems using a learning automaton," Automatica 36 (2000) 1517-1524.
[7] C.K.K. Tang P. Mars, "Games of stochastic learning automata and
adaptive signal processing," IEEE Trans. Syst., Man, Cyber. 23 (1993)
851-856.
[8] X. Zeng, Z. Liu, "A learning automaton based algorithm for
optimization of continuous complex function," Inform. Sci. 174 (2005)
165-175.
[9] H. Beygi, M.R. Meybodi, "A new action-set learning automaton for
function optimization," Int. J. of the Franklin Inst. 343 (2006) 27-47.
[10] Y. Jin, M. Olhofer, and B. Sendho,"Dynamic weighted aggregation for
evolutionrary multiobjective optimization: Why dose it work and how?"
In Proc. GECCO 200.
[11] E. Zitzler, K. Deb, and L. Thiele,"Comparison of multiobjective
evolution algorithms: empirical results," Evolutionary Computation,
8(2): 173-195, 2000.
[1] C.A.C. Coello, "A comprehensive survey of evolutionary-based
multiobjective optimization techniques," Knowledge and Information
Science 1 (1999) 269-308.
[2] C.M. Fonseca, P.J. Fleming, "An overview of evolutionary algorithms in
multiobjective optimization," Evolutionary Computation 3 (1995) 1-16.
[3] E. Zitzler, "Evolutionray algorithms for mulriobjective optimization:
methods and applications," Ph.D. Tesis, Swiss Federal Institute of
Technology, Zurich, 1999.
[4] 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.
[5] B.J. Oommen, E.V. de St. Criox, "Graph partitioning using learning
automata," IEEE Trans. Comput. 45 (1996) 195-208.
[6] X. Zeng, J. Zhou, C. Vasseur, "A strategy for controlling non-linear
systems using a learning automaton," Automatica 36 (2000) 1517-1524.
[7] C.K.K. Tang P. Mars, "Games of stochastic learning automata and
adaptive signal processing," IEEE Trans. Syst., Man, Cyber. 23 (1993)
851-856.
[8] X. Zeng, Z. Liu, "A learning automaton based algorithm for
optimization of continuous complex function," Inform. Sci. 174 (2005)
165-175.
[9] H. Beygi, M.R. Meybodi, "A new action-set learning automaton for
function optimization," Int. J. of the Franklin Inst. 343 (2006) 27-47.
[10] Y. Jin, M. Olhofer, and B. Sendho,"Dynamic weighted aggregation for
evolutionrary multiobjective optimization: Why dose it work and how?"
In Proc. GECCO 200.
[11] E. Zitzler, K. Deb, and L. Thiele,"Comparison of multiobjective
evolution algorithms: empirical results," Evolutionary Computation,
8(2): 173-195, 2000.
@article{"International Journal of Information, Control and Computer Sciences:51207", author = "M. R. Aghaebrahimi and S. H. Zahiri and M. Amiri", title = "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.", keywords = "Function optimization, Multiobjective optimization,
Learning automata.", volume = "3", number = "1", pages = "32-4", }