
Open access
Date
2001-07Type
- Report
ETH Bibliography
yes
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Abstract
After adequately demonstrating the ability to solve different two-objective optimization problems, multi-objective evolutionary algorithms (MOEAs) must now show their efficacy in handling problems having more than two objectives. In this paper, we have suggested three different approaches for systematically designing test problems for this purpose. The simplicity of construction, scalability to any number of decision variables and objectives, knowledge of exact shape and location of the resulting Pareto-optimal front, and introduction of controlled difficulties in both converging to the true Pareto-optimal front and maintaining a widely distributed set of solutions are the main features of the suggested test problems. Because of the above features, they should be found useful in various research activities on MOEAs, such as testing the performance of a new MOEA, comparing different MOEAs, and better understanding of the working principles of MOEAs. Show more
Permanent link
https://doi.org/10.3929/ethz-a-004284199Publication status
publishedJournal / series
TIK ReportVolume
Publisher
ETH Zurich, Computer Engineering and Networks LaboratoryOrganisational unit
02640 - Inst. f. Technische Informatik und Komm. / Computer Eng. and Networks Lab.
Related publications and datasets
Is previous version of: http://hdl.handle.net/20.500.11850/46449
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ETH Bibliography
yes
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