Abstract
This paper presents a hybrid multi-objective genetic algorithm which combines the main notion of game theory Nash equilibrium with Pareto-optimality to solve the multi-objective Frequency Assignment Problem (FAP) in mobile networks. The game is coupled with genetic algorithm to accelerate convergence and produce Nash equilibrium and Pareto non-dominated solutions simultaneously. The proposed hybrid approach produces high quality solutions as proved by several performed tests and corroborated by the comparison with the most referred multi-objective optimization algorithms such as NSGA-II and SPEA2 on well-known Philadelphia and COST259 FAP instances. Furthermore, the effect of some parameters is discussed.
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