Cruz-Duarte, Jorge M.Ortiz-Bayliss, Jose C.Amaya, IvanPillay, Nelishia2021-08-262021-08-262021-06Cruz-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/app11125620.2076-3417 (online)10.3390/ app11125620http://hdl.handle.net/2263/81519Optimisation 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© 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/).MetaheuristicHyper-heuristicOptimisationAlgorithmUnfolded metaheuristicArticle