Associate Professor Tsung-Che Chiang

Department of Computer Science and Information Engineering
National Taiwan Normal University

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Shao-Wen Chen and Tsung-Che Chiang, Evolutionary many-objective optimization by MO-NSGA-II with enhanced mating selection, Proc. of IEEE World Congress on Computational Intelligence (WCCI), IEEE Congress on Evolutionary Computation (CEC), pp. 1397 - 1404, Beijing, July, 2014.

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Abstract

Many-objective optimization deals with problems with more than three objectives. The rapid growth of non-dominated solutions with the increase of the number of objectives weakens the search ability of Pareto-dominance- based multiobjective evolutionary algorithms. MO-NSGA-II strengthens its dominance-based predecessor, NSGA-II, by guiding the search process with reference points. In this paper, we further improve MO-NSGA-II by enhancing its mating selection mechanism with a hierarchical selection and a neighborhood concept based on the reference points. Experimental results confirm that the proposed ideas lead to better solution quality.

Benchmark problem instances

• DTLZ1-4 problems
  K. Deb, L. Thiele, M. Laumanns, and E. Zitzler, "Scalable test problems for evolutionary multi-objective optimization," in A. Abraham et al. (Eds.) Evolutionary Multi-objective Optimization, pp. 105–145, 2005.)

Benchmark algorithms

• MO-NSGA-II
  K. Deb and H. Jain, "Handling many-objective problems using an improved NSGA-II procedure," in Proc. IEEE Congress on Evolutionary Computation (CEC), 2012.
  (MO-NSGA-II is the early version of the NSGA-III, and there are some differences between them.)