Fillipe Goulart
Felipe Campelo
ABSTRACT
This work explores the influence of three different dominance criteria, namely the Pareto-, epsilon-, and cone epsilon-dominance, on the performance of multiobjective evolutionary algorithms. The approaches are incorporated into two different algorithms, which are then applied to the solution of twelve benchmark problems from the ZDT and DTLZ families. The final results of the algorithms are compared in terms of cardinality, convergence, and diversity of solutions using a statistical methodology designed to indicate whether any of the criteria provides significantly better results over the whole test set. The results obtained suggest that the cone epsilon-approach is an interesting alternative for finding well-distributed fronts without the loss of efficient solutions usually presented by the epsilon-dominance.
- M. Crawley. The R Book. John Wiley & Sons, Chichester, England, 1st. edition, 2007. Google Scholar
Cross Ref
- K. Deb, M. Mohan, and S. Mishra. Towards a quick computation of well-spread Pareto-optimal solutions. In C. M. Fonseca, P. J. Fleming, E. Zitzler, L. Thiele, and K. Deb, editors, Evolutionary Multi-Criterion Optimization, EMO, LNCS 2632, pages 222--236, 2003. Google ScholarDigital Library
- K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182--197, 2002. Google ScholarDigital Library
- K. Deb, L. Thiele, M. Laumanns, and E. Zitzler. Scalable test problems for evolutionary multi-objective optimization. Technical report, Institut Technische Informatik und Kommunikationsnetze, 2001.Google Scholar
- A. P. Engelbrecht. Computational Intelligence -- An Introduction. John Wiley & Sons, 2007. Google ScholarDigital Library
- D. Montgomery. Design and Analysis of Experiments. Wiley, 2008.Google Scholar
- R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2011.Google Scholar
- T. Robic and B. Filipic. DEMO: Differential evolution for multiobjective optimization. In EMO, LNCS 3410, pages 520--533, 2005. Google ScholarDigital Library
- A. Zhou, B.-Y. Qu, H. Li, S.-Z. Zhao, P. N. Suganthan, and Q. Zhang. Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation, 1(1):32--49, 2011.Google ScholarCross Ref
- E. Zitzler, K. Deb, and L. Thiele. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 8(2):173--195, 2000. Google ScholarDigital Library
- E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: Improving the strengh Pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Networks Laboratory, 2001.Google Scholar
Index Terms
- Influence of relaxed dominance criteria in multiobjective evolutionary algorithms
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