Modelling competitive Hopfield networks for the maximum clique problem
Computers and Operations Research
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
A neural model for the p-median problem
Computers and Operations Research
Minimizing Interference in Satellite Communications Using Chaotic Neural Networks
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
Differential evolution and particle swarm optimisation in partitional clustering
Computational Statistics & Data Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A gradual neural-network approach for frequency assignment in satellite communication systems
IEEE Transactions on Neural Networks
Neural techniques for combinatorial optimization with applications
IEEE Transactions on Neural Networks
Design and analysis of maximum Hopfield networks
IEEE Transactions on Neural Networks
A columnar competitive model for solving combinatorial optimization problems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
| Hi-index | 12.05 |
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.