Improved binary artificial bee colony algorithm

The artificial bee colony (ABC) algorithm is an evolutionary optimization algorithm based on swarm intelligence and inspired by the honey bees’ food search behavior. Since the ABC algorithm has been developed to achieve optimal solutions by searching in the continuous search space, modification is r...

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Vydáno v:	Frontiers of information technology & electronic engineering Ročník 22; číslo 8; s. 1080 - 1091
Hlavní autor:	Durgut, Rafet
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Hangzhou Zhejiang University Press 01.08.2021 Springer Nature B.V
Témata:	Algorithms Communications Engineering Computer Hardware Computer Science Computer Systems Organization and Communication Networks Electrical Engineering Electronics and Microelectronics Evolutionary algorithms Heuristic methods Instrumentation Libraries Networks Operations research Optimization Research Article Search algorithms Source code Swarm intelligence Artificial bee colony Binary optimization 二进制优化 TP301.6 人工蜂群 Uncapacitated facility location problem (UFLP) 无容量限制的设施选址位置问题 (UFLP)
ISSN:	2095-9184, 2095-9230
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Abstract	The artificial bee colony (ABC) algorithm is an evolutionary optimization algorithm based on swarm intelligence and inspired by the honey bees’ food search behavior. Since the ABC algorithm has been developed to achieve optimal solutions by searching in the continuous search space, modification is required to apply it to binary optimization problems. In this study, we modify the ABC algorithm to solve binary optimization problems and name it the improved binary ABC (IbinABC). The proposed method consists of an update mechanism based on fitness values and the selection of different decision variables. Therefore, we aim to prevent the ABC algorithm from getting stuck in a local minimum by increasing its exploration ability. We compare the IbinABC algorithm with three variants of the ABC and other meta-heuristic algorithms in the literature. For comparison, we use the well-known OR-Library dataset containing 15 problem instances prepared for the uncapacitated facility location problem. Computational results show that the proposed algorithm is superior to the others in terms of convergence speed and robustness. The source code of the algorithm is available at https://github.com/rafetdurgut/ibinABC .
AbstractList	The artificial bee colony (ABC) algorithm is an evolutionary optimization algorithm based on swarm intelligence and inspired by the honey bees’ food search behavior. Since the ABC algorithm has been developed to achieve optimal solutions by searching in the continuous search space, modification is required to apply it to binary optimization problems. In this study, we modify the ABC algorithm to solve binary optimization problems and name it the improved binary ABC (IbinABC). The proposed method consists of an update mechanism based on fitness values and the selection of different decision variables. Therefore, we aim to prevent the ABC algorithm from getting stuck in a local minimum by increasing its exploration ability. We compare the IbinABC algorithm with three variants of the ABC and other meta-heuristic algorithms in the literature. For comparison, we use the well-known OR-Library dataset containing 15 problem instances prepared for the uncapacitated facility location problem. Computational results show that the proposed algorithm is superior to the others in terms of convergence speed and robustness. The source code of the algorithm is available at https://github.com/rafetdurgut/ibinABC. The artificial bee colony (ABC) algorithm is an evolutionary optimization algorithm based on swarm intelligence and inspired by the honey bees’ food search behavior. Since the ABC algorithm has been developed to achieve optimal solutions by searching in the continuous search space, modification is required to apply it to binary optimization problems. In this study, we modify the ABC algorithm to solve binary optimization problems and name it the improved binary ABC (IbinABC). The proposed method consists of an update mechanism based on fitness values and the selection of different decision variables. Therefore, we aim to prevent the ABC algorithm from getting stuck in a local minimum by increasing its exploration ability. We compare the IbinABC algorithm with three variants of the ABC and other meta-heuristic algorithms in the literature. For comparison, we use the well-known OR-Library dataset containing 15 problem instances prepared for the uncapacitated facility location problem. Computational results show that the proposed algorithm is superior to the others in terms of convergence speed and robustness. The source code of the algorithm is available at https://github.com/rafetdurgut/ibinABC .
Author	Durgut, Rafet
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Cites_doi	10.1023/A:1008202821328 10.1016/j.cie.2014.08.016 10.1080/0952813X.2013.782347 10.1016/j.future.2017.05.044 10.1016/j.asoc.2018.01.001 10.1016/j.asoc.2015.04.007 10.1007/s10462-009-9114-9 10.3906/elk-1203-104 10.1016/j.swevo.2018.08.015 10.1016/j.swevo.2016.06.001 10.1016/j.advengsoft.2016.01.008 10.1155/2017/8404231 10.1016/j.compstruc.2016.03.001 10.1038/scientificamerican0792-66 10.1016/j.swevo.2011.08.001 10.1007/s10462-012-9328-0 10.1002/9780470496916 10.1016/j.compbiolchem.2007.09.005 10.1016/j.asoc.2011.08.038 10.1016/j.advengsoft.2013.12.007 10.1007/s10462-017-9605-z 10.1016/j.future.2019.03.032 10.1016/j.asoc.2010.04.024 10.1057/jors.1990.166 10.1007/s10898-007-9149-x 10.1007/978-3-642-81283-5_8 10.1007/s13042-020-01094-7 10.1109/INISTA.2011.5946125 10.1109/ICNN.1995.488968
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Keywords	Artificial bee colony Binary optimization 二进制优化 TP301.6 人工蜂群 Uncapacitated facility location problem (UFLP) 无容量限制的设施选址位置问题 (UFLP)
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Snippet	The artificial bee colony (ABC) algorithm is an evolutionary optimization algorithm based on swarm intelligence and inspired by the honey bees’ food search...
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SubjectTerms	Algorithms Communications Engineering Computer Hardware Computer Science Computer Systems Organization and Communication Networks Electrical Engineering Electronics and Microelectronics Evolutionary algorithms Heuristic methods Instrumentation Libraries Networks Operations research Optimization Research Article Search algorithms Source code Swarm intelligence
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Title	Improved binary artificial bee colony algorithm
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