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dc.contributor.author | Cruz-Duarte, Jorge M.![]() |
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dc.contributor.author | Ortiz-Bayliss, Jose C.![]() |
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dc.contributor.author | Amaya, Ivan![]() |
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dc.contributor.author | Pillay, Nelishia![]() |
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dc.date.accessioned | 2021-08-26T13:59:14Z | |
dc.date.available | 2021-08-26T13:59:14Z | |
dc.date.issued | 2021-06 | |
dc.description.abstract | Optimisation has been with us since before the first humans opened their eyes to natural phenomena that inspire technological progress. Nowadays, it is quite hard to find a solver from the overpopulation of metaheuristics that properly deals with a given problem. This is even considered an additional problem. In this work, we propose a heuristic-based solver model for continuous optimisation problems by extending the existing concepts present in the literature. We name such solvers ‘unfolded’ metaheuristics (uMHs) since they comprise a heterogeneous sequence of simple heuristics obtained from delegating the control operator in the standard metaheuristic scheme to a high-level strategy. Therefore, we tackle the Metaheuristic Composition Optimisation Problem by tailoring a particular uMH that deals with a specific application. We prove the feasibility of this model via a two-fold experiment employing several continuous optimisation problems and a collection of diverse population-based operators with fixed dimensions from ten well-known metaheuristics in the literature. As a high-level strategy, we utilised a hyper-heuristic based on Simulated Annealing. Results demonstrate that our proposed approach represents a very reliable alternative with a low computational cost for tackling continuous optimisation problems with a tailored metaheuristic using a set of agents. We also study the implication of several parameters involved in the uMH model and their influence over the solver performance. | en_ZA |
dc.description.department | Companion Animal Clinical Studies | en_ZA |
dc.description.department | Computer Science | en_ZA |
dc.description.librarian | pm2021 | en_ZA |
dc.description.uri | http://www.mdpi.com/journal/applsci | en_ZA |
dc.identifier.citation | Cruz-Duarte, J.M.; Ortiz-Bayliss, J.C.; Amaya, I.; Pillay, N. Global Optimisation through Hyper-Heuristics: Unfolding Population-Based Metaheuristics. Applied Sciences 2021, 11, 5620. https://doi.org/10.3390/app11125620. | en_ZA |
dc.identifier.issn | 2076-3417 (online) | |
dc.identifier.other | 10.3390/ app11125620 | |
dc.identifier.uri | http://hdl.handle.net/2263/81519 | |
dc.language.iso | en | en_ZA |
dc.publisher | MDPI | en_ZA |
dc.rights | © 2021 by the authors. Licensee: MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_ZA |
dc.subject | Metaheuristic | en_ZA |
dc.subject | Hyper-heuristic | en_ZA |
dc.subject | Optimisation | en_ZA |
dc.subject | Algorithm | en_ZA |
dc.subject | Unfolded metaheuristic | en_ZA |
dc.title | Global optimisation through hyper-heuristics : unfolding population-based metaheuristics | en_ZA |
dc.type | Article | en_ZA |