Abstract
This paper demonstrates how adaptive population-sizing and epsilon-dominance archiving can be combined with the Nondominated Sorted Genetic Algorithm-II (NSGAII) to enhance the algorithm’s efficiency, reliability, and ease-of-use. Four versions of the enhanced Epsilon Dominance NSGA-II (ε-NSGAII) are tested on a standard suite of evolutionary multiobjective optimization test problems. Comparative results for the four variants of the (ε-NSGAII demonstrate that adapting population size based on online changes in the epsilon dominance archive size can enhance performance. The best performing version of the (ε-NSGAII is also compared to the original NSGAII and the (εMOEA on the same suite of test problems. The performance of each algorithm is measured using three running performance metrics, two of which have been previously published, and one new metric proposed by the authors. Results of the study indicate that the new version of the NSGAII proposed in this paper demonstrates improved performance on the majority of two-objective test problems studied.
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References
Deb, K., Mohan, M., Mishra, S.: A Fast Multi-objective Evolutionary Algorithm for Finding Well-Spread Pareto-Optimal Solutions. KenGAL, Report No. 2003002. Indian Institute of Technology, Kanpur, India (2003)
Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combining Convergence and Diversity in Evolutionary Multiobjective Optimization. Evolutionary Computation 10(3), 263–282 (2002)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Trans. Evol. Computation 6(2), 182–197 (2002)
Reed, P., Minsker, B.S., Goldberg, D.E.: Simplifying Multiobjective Optimization: An Automated Design Methodology for the Nondominated Sorted Genetic Algorithm-II. Water Resources Research 39(7), 1196–1201 (2003)
Harik, G.R., Cuantu-Paz, E., Goldberg, D.E., Miller, B.L.: The Gambler’s Ruin Problem, Genetic Algorithms, and the Sizing of Populations. In: Proceedings of the 1997 IEEE Conference on Evolutionary Computation, pp. 7–12. IEEE Press, Piscataway (1997)
Goldberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, Norwell (2002)
Deb, K., Jain, S.: Running Performance Metrics for Evolutionary Multi-Objective Optimization. KanGAL, Report No. 2002004. Indian Institute of Technology, Kanpur, India (2002)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8(2), 125–148 (2000)
Deb, K.: Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems. Evolutionary Computation 7(3), 205–230 (1999)
Reed, P., Devireddy, V.: Groundwater Monitoring Design: A Case Study Combining epsilon-Dominance Archiving and Automatic Parameterization for the NSGA-II. In: Coello-Coello, C. (ed.) Applications of Multi-Objective Evolutionary Algorithms. World Scientific, New York (2004)(In Press)
Reed, P., Devireddy, V.: Using Interactive Archives in Evolutionary Multiobjective Optimization: Case Studies for Long-Term Groundwater Monitoring Design. In: The International Environmental Modeling and Software Society Conference, Osnabruck, Germany (2004) (In Press)
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Kollat, J.B., Reed, P.M. (2005). The Value of Online Adaptive Search: A Performance Comparison of NSGAII, ε-NSGAII and εMOEA. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31880-4_27
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DOI: https://doi.org/10.1007/978-3-540-31880-4_27
Publisher Name: Springer, Berlin, Heidelberg
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