Population-based variable neighbourhood search algorithm applied to unconstrained continuous optimisation

This work presents a population-based variable neighbourhood search approach for unconstrained continuous optimisation, called PRVNS. The main contributions of the proposed algorithm are to evolve a population of individuals (i. e., candidate solutions) and to allow each individual adapts its own neighbourhood search area accordingly to its performance. The adaptive amplitude control allows individuals to autonomously exploit and explore promising regions in the search space. Several unconstrained continuous benchmark functions with a high number of dimensions (d = 250) are used to evaluate the algorithm's performance. The PRVNS results are compared with the results obtained by some well known population-based approaches: differential evolution (DE), particle swarm optimisation (PSO) and artificial bee colony (ABC). Also, the standard VNS algorithm is considered in the experiments. The results and analyses suggest that the PRVNS approach is a promising and competitive algorithm for unconstrained continuous optimisation.