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A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses

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Abstract

In many technical fields, single-objective optimization procedures in continuous domains involve expensive numerical simulations. In this context, an improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide fast convergence speed, high solution accuracy and robust performance over a wide range of problems. It implements enhancements of the ABC structure and hybridizations with interpolation strategies. The latter are inspired by the quadratic trust region approach for local investigation and by an efficient global optimizer for separable problems. Each modification and their combined effects are studied with appropriate metrics on a numerical benchmark, which is also used for comparing AsBeC with some effective ABC variants and other derivative-free algorithms. In addition, the presented algorithm is validated on two recent benchmarks adopted for competitions in international conferences. Results show remarkable competitiveness and robustness for AsBeC.

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Notes

  1. http://www3.ntu.edu.sg/home/EPNSugan/index_files/CEC2015/CEC2015.htm.

  2. http://coco.gforge.inria.fr.

  3. http://www.nag.co.uk/numeric/MB/start.asp.

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Acknowledgments

The authors would like to thank GE Avio S.r.l. and its Engineering Technologies department, especially Ing. F. Bertini and Ing. E. Spano. Their collaboration was fundamental for shaping the AsBeC algorithm within an industrial application framework. We are also grateful to Prof. E. Benini (Università di Padova) for his comments and reviews.

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Correspondence to Enrico Ampellio.

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Ampellio, E., Vassio, L. A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses. Swarm Intell 10, 99–121 (2016). https://doi.org/10.1007/s11721-016-0121-6

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