| An efficient and accurate solution methodology for bilevel multi-objective programming problems using a hybrid evolutionary-local-search algorithm. | |
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MedLine Citation:
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PMID: 20560758 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Bilevel optimization problems involve two optimization tasks (upper and lower level), in which every feasible upper level solution must correspond to an optimal solution to a lower level optimization problem. These problems commonly appear in many practical problem solving tasks including optimal control, process optimization, game-playing strategy developments, transportation problems, and others. However, they are commonly converted into a single level optimization problem by using an approximate solution procedure to replace the lower level optimization task. Although there exist a number of theoretical, numerical, and evolutionary optimization studies involving single-objective bilevel programming problems, not many studies look at the context of multiple conflicting objectives in each level of a bilevel programming problem. In this paper, we address certain intricate issues related to solving multi-objective bilevel programming problems, present challenging test problems, and propose a viable and hybrid evolutionary-cum-local-search based algorithm as a solution methodology. The hybrid approach performs better than a number of existing methodologies and scales well up to 40-variable difficult test problems used in this study. The population sizing and termination criteria are made self-adaptive, so that no additional parameters need to be supplied by the user. The study indicates a clear niche of evolutionary algorithms in solving such difficult problems of practical importance compared to their usual solution by a computationally expensive nested procedure. The study opens up many issues related to multi-objective bilevel programming and hopefully this study will motivate EMO and other researchers to pay more attention to this important and difficult problem solving activity. |
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Authors:
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Kalyanmoy Deb; Ankur Sinha |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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Title: Evolutionary computation Volume: 18 ISSN: 1530-9304 ISO Abbreviation: Evol Comput Publication Date: 2010 |
Date Detail:
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Created Date: 2010-08-03 Completed Date: 2010-11-05 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9513581 Medline TA: Evol Comput Country: United States |
Other Details:
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Languages: eng Pagination: 403-49 Citation Subset: IM |
Affiliation:
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Finland Distinguished Professor (FiDiPro), Department of Mechanical Engineering, Indian Institute of Technology Kanpur, PIN 208016, India. deb@iitk.ac.in |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Evolution* Models, Statistical Software |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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