Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/86331
Title: Sampling-based adaptive bounding evolutionary algorithm for continuous optimization problems
Authors: Luo, Linbo
Hou, Xiangting
Zhong, Jinghui
Cai, Wentong
Ma, Jianfeng
Keywords: Search Space Narrowing
Adaptive Bounding Evolutionary Algorithm
Issue Date: 2016
Source: Luo, L., Hou, X., Zhong, J., Cai, W., & Ma, J. (2017). Sampling-based adaptive bounding evolutionary algorithm for continuous optimization problems. Information Sciences, 382-383, 216-233.
Series/Report no.: Information Sciences
Abstract: This paper proposes a novel sampling-based adaptive bounding evolutionary algorithm termed SABEA that is capable of dynamically updating the search space during the evolution process for continuous optimization problems. The proposed SABEA adopts two bounding strategies, namely fitness-based bounding and probabilistic sampling-based bounding, to select a set of individuals over multiple generations and leverage the value information from these individuals to update the search space of a given problem for improving the solution accuracy and search efficiency. To evaluate the performance of this method, SABEA is applied on top of the classic differential evolution (DE) algorithm and a DE variant, and SABEA is compared to a state-of-the-art Distribution-based Adaptive Bounding Genetic Algorithm (DABGA) on a set of 27 selected benchmark functions. The results show that SABEA can be used as a complementary strategy for further enhancing the performance of existing evolutionary algorithms and it also outperforms DABGA. Finally, a practical problem, namely the model calibration for an agent-based simulation, is used to further evaluate SABEA. The results show SABEA’s applicability to diverse problems and its advantages over the traditional genetic algorithm-based calibration method and DABGA.
URI: https://hdl.handle.net/10356/86331
http://hdl.handle.net/10220/43997
ISSN: 0020-0255
DOI: http://dx.doi.org/10.1016/j.ins.2016.12.023
Rights: © 2016 Elsevier Inc. This is the author created version of a work that has been peer reviewed and accepted for publication by Information Sciences, Elsevier Inc. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.ins.2016.12.023].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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