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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Geiger, M.J. |
| Copyright Year | 2007 |
| Description | Author affiliation: Dept. of Ind. Manage., Hohenheim Univ., Stuttgart (Geiger, M.J.) |
| Abstract | The article presents an approach to interactively solve multi-objective optimization problems. While the identification of efficient solutions is supported by computational intelligence techniques on the basis of local search, the search is directed by partial preference information obtained from the decision maker. An application of the approach to biobjective portfolio optimization, modeled as the well-known knapsack problem, is reported, and experimental results are reported for benchmark instances taken from the literature. In brief, we obtain encouraging results that show the applicability of the approach to the described problem. In order to stipulate a better understanding of the underlying structures of biobjective knapsack problems, we also study the characteristics of the search space of instances for which the optimal alternatives are known. As a result, optimal alternatives have been found to be relatively concentrated in alternative space, making the resolution of the studied instances possible with reasonable effort |
| Starting Page | 193 |
| Ending Page | 199 |
| File Size | 491524 |
| Page Count | 7 |
| File Format | |
| ISBN | 1424407028 |
| DOI | 10.1109/MCDM.2007.369436 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-04-01 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computer science Operations research Uncertainty Decision making Pareto optimization Application software Portfolios Computational intelligence |
| Content Type | Text |
| Resource Type | Article |
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