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On the Need for Random Baseline Comparisons in Metaheuristic Search

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dc.contributor.author Soper, Daniel
dc.date.accessioned 2017-12-28T00:46:49Z
dc.date.available 2017-12-28T00:46:49Z
dc.date.issued 2018-01-03
dc.identifier.isbn 978-0-9981331-1-9
dc.identifier.uri http://hdl.handle.net/10125/50045
dc.description.abstract A wide variety of organizations now regularly rely on established metaheuristic search algorithms in order to find solutions to otherwise intractable optimization problems. Unfortunately, neither the developers of these algorithms nor the organizations that rely on them typically assess the algorithms’ performance against a baseline random search strategy, opting instead to compare a specific algorithm’s performance against that of other metaheuristic search algorithms. This paper reveals the folly of such behavior, and shows by means of an optimization case study that simple random or nearly random search algorithms can, in certain circumstances, substantially outperform several of the most widely used metaheuristic search algorithms in finding solutions to optimization problems. The implications of the observed results for both organizations and researchers are presented and discussed.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 51st Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Intelligent Decision Support for Logistics and Supply Chain Management
dc.subject Metaheuristic search, Optimization, Random search
dc.title On the Need for Random Baseline Comparisons in Metaheuristic Search
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2018.158
Appears in Collections: Intelligent Decision Support for Logistics and Supply Chain Management


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