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This paper presents a multi-objective immune memetic algorithm to the challenge of solving the Frequency Assignment Problem (FAP) in cellular networks seeking the minimization of the network's total interference, the maximum interference and the number of used frequencies. The originality of the proposed approach lies in integrating a FAP-specific local search into its evolutionary process instead of crossover and mutation, as well as a guided diversification strategy for better performances. Moreover, the algorithm is supplemented with a clonal selection, inherited from Artificial Immune Systems (AIS), which aims to improve the algorithm exploration and exploitation abilities. Computational experiments performed over COST259 instances show the efficiency of the newly proposed evolutionary multi-objective algorithm and corroborated by the comparisons we did with the most frequently referred algorithm in the related literature. Furthermore, the effect of the main parameters and the interaction between them is analyzed using statistical tools. [ABSTRACT FROM AUTHOR]

This paper presents a novel multi-objective hybrid metaheuristic that combines an artificial immune system (AIS) with genetic operators to address the multi-objective Frequency Assignment Problem (FAP) in cellular mobile networks, seeking to minimize three objectives simultaneously: Total interference, Maximal interference, and the number of used frequencies. The proposed approach inherits its evolutionary cycle from a multi-objective immune network supplemented by a FAP-specific local search as well as clonal selection for a better intensification. Moreover, a genetic mutation is applied at each generation to enhance the exploration of the research space. Computational experiments performed over COST259 instances and based on the hypervolume metric, show the efficiency of the newly proposed hybrid algorithm, and corroborated by the comparisons we did with the most frequently referred algorithms in the related literature. Furthermore, the effect of the main parameters and the interaction between them is analyzed using statistical tools. • This manuscript proposes an effective multi-objective hybrid immune algorithm (SPIA) to solve the frequency assignment problem in cellular mobile networks. • The evolutionary cycle of SPIA is inherited from AIS, hybridized with genetic operators, and a proposed tailored Local search to improve its exploration and exploitation abilities. • Initial solutions are generated using a proposed method favoring feasible and diversified solutions. • The behavior of the main SPIA factors are analyzed using statistical tests. • Computational experiments performed over COST259 instances and based on the hypervolume metric show the efficiency of the newly proposed hybrid multi-objective algorithm. [ABSTRACT FROM AUTHOR]