Algorithm For Frequency Assignment Problem Research Articles

Performance assessment of biogeography-based multi-objective algorithm for frequency assignment problem

Performance assessment of biogeography-based multi-objective algorithm for frequency assignment problem

Performance assessment of biogeography-based multi-objective algorithm for frequency assignment problem

Performance assessment of biogeography-based multi-objective algorithm for frequency assignment problem

A hybrid approach based on stochastic competitive Hopfield neural network and efficient genetic algorithm for frequency assignment problem

This paper presents a hybrid efficient genetic algorithm (EGA) for the stochastic competitive Hopfield (SCH) neural network, which is named SCH–EGA. This approach aims to tackle the frequency assignment problem (FAP). The objective of the FAP in satellite communication system is to minimize the co-channel interference between satellite communication systems by rearranging the frequency assignment so that they can accommodate increasing demands. Our hybrid algorithm involves a stochastic competitive Hopfield neural network (SCHNN) which manages the problem constraints, when a genetic algorithm searches for high quality solutions with the minimum possible cost. Our hybrid algorithm, reflecting a special type of algorithm hybrid thought, owns good adaptability which cannot only deal with the FAP, but also cope with other problems including the clustering, classification, and the maximum clique problem, etc. In this paper, we first propose five optimal strategies to build an efficient genetic algorithm. Then we explore three hybridizations between SCHNN and EGA to discover the best hybrid algorithm. We believe that the comparison can also be helpful for hybridizations between neural networks and other evolutionary algorithms such as the particle swarm optimization algorithm, the artificial bee colony algorithm, etc. In the experiments, our hybrid algorithm obtains better or comparable performance than other algorithms on 5 benchmark problems and 12 large problems randomly generated. Finally, we show that our hybrid algorithm can obtain good results with a small size population.

Multiobjective evolutionary algorithm for frequency assignment problem in satellite communications

Satellite communications technology leads to an important improvement in our life and world. The frequency assignment problem (FAP) is a fundamental problem in satellite communication system for providing high-quality transmissions. The whole goal of the FAP in satellite communication system is to minimize co-channel interference between two satellite systems by rearranging frequency assignment. Recently, many metaheuristics, including neural networks and evolutionary algorithms, are proposed for this NP-complete problem. All such algorithms formulate the FAP as a single-objective problem, although it obviously has two objectives and thus essentially is a multiobjective optimization problem. This study explicitly formulates FAP as a multiobjective optimization problem and presents a multiobjective evolutionary algorithm based on decomposition (MOEA/D) with a problem-specific subproblem-dependent heuristic assignment (SHA), called MOEA/D-SHA, for the multiobjective FAP. Simulation results show that the MOEA/D-SHA outperforms significantly general-purpose MOEA/D, and an off-the-shelf multiobjective algorithm, i.e., NSGA-II. The advantages of the MOEA/D-SHA over the state-of-the-art single-objective approaches are also shown.

Engineering-Oriented Hybrid Genetic Algorithm for Frequency Assignment Problem

Since the usable range of the frequency spectrum is limited, the frequency assignment problem (FAP) is important in mobile telephone communication. In this paper, according to the characteristics of engineering- oriented FAP, an engineering-oriented hybrid genetic algorithm (EHGA) based on traditional genetic algorithm (TGA) is proposed, combined with particle swarm optimization (PSO) and simulated annealing (SA). The results obtained by the simulation to a real-word FAP case in GSM show that the algorithm we proposed is a better approach to solve the engineering-oriented FAP.

A two-phase algorithm for frequency assignment in cellular mobile systems

In this paper, we consider the frequency assignment problem (FAP) in a cellular mobile communication system under the assumption that there is no channel interference between two cells separated by more than a certain distance. This special structure is observed in most cellular systems. To handle the considered FAP, we use the pattern approach which fits naturally to the problem. Based on this approach, we are able to formulate the considered FAP into a manageable optimization problem and propose a two-phase heuristic algorithm for the problem. Computational experiments show that our algorithm performs much better in both solution quality and computational time than the recently developed algorithms for FAP. Since the considered FAP well reflects most cellular systems, our algorithm can be applied to many practical situations. >