SCH-EGA: An Efficient Hybrid Algorithm for the Frequency Assignment Problem

Abstract

This paper proposes a hybrid stochastic competitive Hopfield neural network-efficient genetic algorithm (SCH-EGA) approach to tackle the frequency assignment problem (FAP). The objective of FAP is to minimize the cochannel interference between satellite communication systems by rearranging the frequency assignments so that they can accommodate the increasing demands. In fact, as SCH-EGA algorithm owns the good adaptability, it can not only deal with the frequency assignment problem, but also cope with the problems of clustering, classification, the maximum clique problem and so on. In this paper, we first propose five optimal strategies to build an efficient genetic algorithm(EGA) which is the component of our hybrid algorithm. Then we explore different hybridizations between the Hopfield neural network and EGA. With the help of hybridization, SCH-EGA makes up for the defects in the Hopfield neural network and EGA while fully using the advantages of the two algorithms. © Springer-Verlag Berlin Heidelberg 2013.