In this paper, a novel meta-heuristic search algorithm inspired by rhinoceros’ natural behaviour is proposed, namely rhinoceros search algorithm (RSA). Similar to our earlier version called elephant search algorithm, RSA simplifies certain habitual characteristics of rhinoceros and stream-lines the search operations, thereby reducing the number of operational parameters required to configure the model. Via computer simulation, it is shown that RSA is able to outperform certain classical meta-heuristic algorithms. Different dimensions of optimization problems are tested, and good results are observed by RSA. The RSA is also implemented on permutation flow-shop scheduling problem (PFSP) with some representation method. Four different problem scales are used. Compared with partible swarm optimization (PSO) on PFSP, the RSA outperforms PSO on different problem scales with a 3% improvement.
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The authors are grateful for financial support from the research Grants (1) ‘Nature-Inspired Computing and Metaheuristics Algorithms for Optimizing Data Mining Performance’ from the University of Macau (Grant No. MYRG2016-00069-FST); (2) ‘Temporal Data Stream Mining by Using Incrementally Optimized Very Fast Decision Forest (iOVFDF)’, which are offered by the University of Macau (Grant No. MYRG2015-00128-FST); and (3) ‘A Scalable Data Stream Mining Methodology: Stream-based Holistic Analytics and Reasoning in Parallel‘, from FDCT, Macau SAR government (Grant No. FDCT/126/2014/A3).
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Conflict of interest
All authors declare that they have no conflict of interest.
Human and Animal Rights
This article does not contain any studies with human participants or animals performed by any of the authors.
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