Article | Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016 | A Novel Metaheuristic Algorithm inspired by Rhino Herd Behavior Linköping University Electronic Press Conference Proceedings
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Title:
A Novel Metaheuristic Algorithm inspired by Rhino Herd Behavior
Author:
Gai-Ge Wang: School of Computer Science and Technology, Jiangsu Normal University, China Xiao-Zhi Gao: Department of Electrical Engineering and Automation, Aalto University, Finland Kai Zenger: Department of Electrical Engineering and Automation, Aalto University, Finland Leandro dos S. Coelho: Industrial and Systems Engineering Graduate Program, University of Parana, Brazil
DOI:
10.3384/ecp171421026
Download:
Full text (pdf)
Year:
2018
Conference:
Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016
Issue:
142
Article no.:
151
Pages:
1026-1033
No. of pages:
8
Publication type:
Abstract and Fulltext
Published:
2018-12-19
ISBN:
978-91-7685-399-3
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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In this paper, inspired by the herding behavior of rhinos, a new kind of swarm-based metaheuristic search method, namely Rhino Herd (RH), is proposed for solving global continuous optimization problems. In various studies of rhinos in nature, the synoptic model is used to describe rhino’s space use and estimate its probability of occurrence within a given domain. The number of rhinos increases year by year, and this increment can be forecasted by several population size updating models. Synoptic model and a population size updating model are formalized and generalized to a general-purpose metaheuristic optimization algorithm. In RH, null model without introducing any influences is generated as the initial herding. This is followed by rhino modification via synoptic model. After that, the population size is updated by a certain population size updating model, and newly-generated rhinos are randomly initialized within the given conditions. RH is benchmarked by fifteen test problems in comparison with biogeography-based optimization (BBO) and stud genetic algorithm (SGA). The results clearly show the superiority of RH in searching for the better function values on most benchmark problems over BBO and SGA.

Keywords: rhino herd, synoptic model, population size updating model, benchmark functions, swarm intelligence

Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Author:
Gai-Ge Wang, Xiao-Zhi Gao, Kai Zenger, Leandro dos S. Coelho
Title:
A Novel Metaheuristic Algorithm inspired by Rhino Herd Behavior
DOI:
http://dx.doi.org/10.3384/ecp171421026
References:
No references available

Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Author:
Gai-Ge Wang, Xiao-Zhi Gao, Kai Zenger, Leandro dos S. Coelho
Title:
A Novel Metaheuristic Algorithm inspired by Rhino Herd Behavior
DOI:
https://doi.org10.3384/ecp171421026
Note: the following are taken directly from CrossRef
Citations:
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