Multi-start stochastic competitive Hopfield neural network for frequency assignment problem in satellite communications

Abstract

The objective of the frequency assignment problem (FAP) is to minimize cochannel interference between two satellite systems by rearranging frequency assignment. In this paper, we first propose a competitive Hopfield neural network (CHNN) for FAP. Then we propose a stochastic CHNN (SCHNN) for the problem by introducing stochastic dynamics into the CHNN to help the network escape from local minima. In order to further improve the performance of the SCHNN, a multi-start strategy or re-start mechanism is introduced into the SCHNN. The multi-start strategy or re-start mechanism super-imposed on the SCHNN is characterized by alternating phases of cooling and reheating the stochastic dynamics, thus provides a means to achieve an effective dynamic or oscillating balance between intensification and diversification during the search. Furthermore, dynamic weighting coefficient setting strategy is adopted in the energy function to satisfy the constraints and improve the objective of the problem simultaneously. The proposed multi-start SCHNN (MS-SCHNN) is tested on a set of benchmark problems and a large number of randomly generated instances. Simulation results show that the MS-SCHNN is better than several typical neural network algorithms such as GNN, TCNN, NCNN and NCNN-VT, and metaheuristic algorithm such as hybrid SA.