NDLI: Improving hypervolume-based multiobjective evolutionary algorithms by using objective reduction methods

Content Provider	IEEE Xplore Digital Library
Author	Brockhoff, D. Zitzler, E.
Copyright Year	2007
Description	Author affiliation: Comput. Eng. & Network Lab., Zurich (Brockhoff, D.; Zitzler, E.)
Abstract	Hypervolume based multiobjective evolutionary algorithms (MOEA) nowadays seem to be the first choice when handling multiobjective optimization problems with many, i.e., at least three objectives. Experimental studies have shown that hypervolume-based search algorithms as SMS-EMOA can outperform established algorithms like NSGA-II and SPEA2. One problem remains with most of the hypervolume based algorithms: the best known algorithm for computing the hypervolume needs time exponentially in the number of objectives. To save computation time during hypervolume computation which can be better spent in the generation of more solutions, we propose a general approach how objective reduction techniques can be incorporated into hypervolume based algorithms. Different objective reduction strategies are developed and then compared in an experimental study on two test problems with up to nine objectives. The study indicates that the (temporary) omission of objectives can improve hypervolume based MOEAs drastically in terms of the achieved hypervolume indicator values.
Starting Page	2086
Ending Page	2093
File Size	187469
Page Count	8
File Format	PDF
ISBN	9781424413393
DOI	10.1109/CEC.2007.4424730
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-25
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Evolutionary computation Principal component analysis Testing Decision making Pareto analysis Data mining Optimization methods Pareto optimization Visualization Computational modeling
Content Type	Text
Resource Type	Article

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