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On the Need for Random Baseline Comparisons in Metaheuristic Search

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Title:On the Need for Random Baseline Comparisons in Metaheuristic Search
Authors:Soper, Daniel
Keywords:Intelligent Decision Support for Logistics and Supply Chain Management
Metaheuristic search, Optimization, Random search
Date Issued:03 Jan 2018
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.
Pages/Duration:10 pages
URI/DOI:http://hdl.handle.net/10125/50045
ISBN:978-0-9981331-1-9
DOI:10.24251/HICSS.2018.158
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Intelligent Decision Support for Logistics and Supply Chain Management


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