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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hughes, E.J. |
| Copyright Year | 2007 |
| Description | Author affiliation: Cranfield Univ., Shrivenham (Hughes, E.J.) |
| Abstract | Existing evolutionary methods capable of true many-objective optimisation have been limited in their application: for example either initial search directions need to be specified a-priori, or the use of hypervolume limits the search in practice to less than 10 objective dimensions. This paper describes two extensions to the multiple single objective pareto sampling (MSOPS) algorithm. The first provides automatic target vector generation, removing the requirement for initial a-priori designer intervention; and secondly redefines the fitness assignment method to simplify analysis and allow more comprehensive constraint handling. The significant enhancements allow the new MSOPS-II ranking process to be used as part of a general-purpose multi/many objective optimisation algorithm, requiring minimal initial configuration. |
| Starting Page | 3944 |
| Ending Page | 3951 |
| File Size | 2337415 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424413393 |
| DOI | 10.1109/CEC.2007.4424985 |
| 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 | Optimization methods Pareto optimization Sampling methods Space exploration Approximation algorithms Stochastic processes Decision making Measurement |
| Content Type | Text |
| Resource Type | Article |
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