Nash-Pareto Genetic Algorithm for the Frequency Assignment Problem | Zendy

Fatma Laidoui | Zendy; Malika Bessedik | Zendy; Fatima Benbouzid Si-Tayeb | Zendy; Nawfel Bengherbia | Zendy; Massyl Yacine Khelil | Zendy

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Nash-Pareto Genetic Algorithm for the Frequency Assignment Problem

Author(s) -

Fatma Laidoui,

Malika Bessedik,

Fatima Benbouzid Si-Tayeb,

Nawfel Bengherbia,

Massyl Yacine Khelil

Publication year - 2018

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2018.07.262

Subject(s) - computer science , nash equilibrium , mathematical optimization , pareto principle , convergence (economics) , genetic algorithm , game theory , algorithm , multi objective optimization , pareto optimal , mathematics , mathematical economics , machine learning , economics , economic growth

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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