A Population Based Hybrid Metaheuristic for the ρ-median Problem
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| 54010_1.pdf | 312Kb | Adobe PDF | View |
| Title | A Population Based Hybrid Metaheuristic for the ρ-median Problem |
|---|---|
| Author | Pullan, Wayne John |
| Publication Title | IEEE Congress on Evolutionary Computation |
| Editor | Zbigniew Michalewicz |
| Year Published | 2008 |
| Place of publication | Hong Kong |
| Publisher | IEEE |
| Abstract | The p-median problem is one of choosing p facilities from a set of candidates to satisfy the demands of n clients such that the overall cost is minimised. In this paper, PBS, a population based hybrid search algorithm for the pmedian problem is introduced. The PBS algorithm uses a genetic algorithm based meta-heuristic, primarily based on cut and paste crossover operators, to generate new starting points for a hybrid local search. For larger p-median instances, PBS is able to effectively utilise a number of computer processors. It is shown empirically that PBS is able to effectively solve p-median problems for a large range of the commonly used p-median benchmark instances. |
| Peer Reviewed | Yes |
| Published | Yes |
| Publisher URI | http://ieeexplore.ieee.org/servlet/opac?punumber=4625778 |
| Alternative URI | http://dx.doi.org/10.1109/CEC.2008.4630779 |
| Copyright Statement | Copyright 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. |
| ISBN | 978-1-42441921-3 |
| Conference name | 2008 IEEE Congress on Evolutionary Computation |
| Location | Hong Kong |
| Date From | 2008-06-01 |
| Date To | 2008-06-06 |
| URI | http://hdl.handle.net/10072/22925 |
| Date Accessioned | 2009-03-06 |
| Date Available | 2009-05-13T07:45:38Z |
| Language | en_AU |
| Research Centre | Institute for Integrated and Intelligent Systems |
| Faculty | Faculty of Science, Environment, Engineering and Technology |
| Subject | Neural, Evolutionary and Fuzzy Computation |
| Publication Type | Conference Publications (Full Written Paper - Refereed) |
| Publication Type Code | e1 |
Please use this identifier to cite this record: http://hdl.handle.net/10072/22925
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