Abstract
This paper investigates hybridization of multi-objective memetic algorithm and artificial immune system (AIS) for the Frequency Assignment Problem (FAP) in cellular mobile networks. The considered objectives to minimize are the total interference, the maximal interference, and the number of used frequencies. The proposed approach integrates FAP-specific local search into the evolutionary process to overcome the shortcoming of the multi-objective genetic algorithm, as well as clonal selection and receptor editing, which aims to improve the algorithm exploration and exploitation abilities. Based on the hypervolume metric, the proposed hybrid multi-objective algorithm produces high quality solutions as proved by the tests performed over COST259 instances and corroborated by the comparisons with the most frequently referred algorithms in the related literature. Furthermore, the effect and the behaviour of the main parameters of our algorithm and the interaction between them are analysed using the Design of Experiment (DOE).
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