A 0-1 bat algorithm for cellular network optimisation: a systematic study on mapping techniques

Many research efforts are deployed today in order to design techniques that allow continuous metaheuristics to also solve binary problems. However, knowing that no work thoroughly studied these techniques, such a task is still difficult since these techniques are still ambiguous and misunderstood. The bat algorithm (BA) is a continuous algorithm that has been recently adapted using one of these techniques. However, that work suffered from several shortfalls. This paper conducts a systematic study in order to investigate the efficiency and usefulness of discretising continuous metaheuristics. This is done by proposing five binary variants of the BA (BBAs) based on the principal mapping techniques existing in the literature. As problem benchmark, two optimisation problems in cellular networks, the antenna positioning problem (APP) and the reporting cell problem (RCP) are used. The proposed BBAs are evaluated using several types, sizes and complexities of data. Two of the top-ranked algorithms designed to solve the APP and the RCP, the population-based incremental learning (PBIL) and the differential evolution (DE) algorithm are taken as comparison basis. Several statistical tests are conducted as well.