Optimising real parameters using the information of a mesh of solutions: VMO algorithm

Population-based Meta-heuristics are algorithms that can obtain very good results for complex continuous optimisation problems, using the information of a population of solutions. In these algorithms the distribution of solutions is crucial because it has a strong influence of the exploration new regions. In this work, we present a population algorithm, Variable Mesh Optimisation (VMO), in which a set of nodes (potential solutions) is distributed as a mesh. This mesh is initially homogeneously distributed, and then the mesh evolves to a heterogeneous structure resampling the space toward the best neighbours, maintaining at the same time a controlled diversity (avoiding solutions too close to each other). We use a benchmark of multimodal continuous functions to study the influence of the different components of the proposal, and to compare the proposed algorithm with other basic population-based metaheuristics in the literature. The results show that VMO is a very competitive algorithm.