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Towards a quick computation of well-spread Pareto-optimal solutions. (English) Zbl 1036.90525
Fonseca, Carlos M. (ed.) et al., Evolutionary multi-criterion optimization. Second international conference, EMO 2003, Faro, Portugal, April 8–11, 2003. Proceedings. Berlin: Springer (ISBN 3-540-01869-7/pbk). Lect. Notes Comput. Sci. 2632, 222-236 (2003).
Summary: The trade-off between obtaining a good distribution of Pareto-optimal solutions and obtaining them in a small computational time is an important issue in evolutionary multi-objective optimization (EMO). It has been well established in the EMO literature that although SPEA produces a better distribution compared to NSGA-II, the computational time needed to run SPEA is much larger. In this paper, we suggest a clustered NSGA-II which uses an identical clustering technique to that used in SPEA for obtaining a better distribution. Moreover, we propose a steady-state MOEA based on \(\epsilon\)-dominance concept and efficient parent and archive update strategies. Based on a comparative study on a number of two and three objective test problems, it is observed that the steady-state MOEA achieves a comparable distribution to the clustered NSGA-II with a much less computational time.
For the entire collection see [Zbl 1018.00013].

90C29 Multi-objective and goal programming
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
90C59 Approximation methods and heuristics in mathematical programming
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