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.
