Copyright © 2008 The Institute of Electronics, Information and Communication Engineers
Regular Section -- Letters -- Artificial Intelligence and Cognitive Science |
-Similar Elimination to Enhance Search Performance of Multiobjective Evolutionary Algorithms
1 The authors are with the Faculty of Engineering, Shinshu University, Nagano-shi, 380–8553 Japan. E-mail: ahernan{at}shinshu-u.ac.jp
In this paper, we propose 
-similar elimination to improve the search performance of multiobjective evolutionary algorithms in combinatorial optimization problems. This method eliminates similar individuals in objective space to fairly distribute selection among the different regions of the instantaneous Pareto front. We investigate four eliminating methods analyzing their effects using NSGA-II. In addition, we compare the search performance of NSGA-II enhanced by our method and NSGA-II enhanced by controlled elitism.
Key Words: multiobjective evolutionary algorithms, -similar elimination, controlled elitism, selection
Manuscript received August 21, 2007.
Reference
[1] K. Deb, Multi-Objective Optimization using Evolutionary Algorithms, John Wiley & Sons, 2001. [2] K. Deb and T. Goel, "Controlled elitist non-dominated sorting genetic algorithms for better convergence," Proc. First Intl. Conf. on Evolutionary Multi-Criterion Optimization, LNCS, vol.1993, pp.67–81, Springer, 2001. [3] E. Zitzler, Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications, PhD Thesis, Swiss Federal Institute of Technology, Zurich, 1999. [4] H. Aguirre and K. Tanaka, "Selection, drift, recombination, and mutation in multiobjective evolutionary algorithms on scalable MNK-landscapes," Proc. Third Intl. Conf. on Evolutionary Multi-Criterion Optimization, LNCS, vol.3410, pp.355–369, Springer, 2005. [5] P. Czyzak and A. Jaszkiewicz, "Pareto-simulated annealing-A metaheuristic technique for multi-objective combinatorial optimization," Journal of Multi-Criteria Decision Analysis, vol.7, no.1, pp.34–47, 1998.
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