A portable and scalable algorithm for a class of constrained combinatorial optimization problems
Computers and Operations Research
On the application of linear transformations for genetic algorithms optimization
International Journal of Knowledge-based and Intelligent Engineering Systems
Minimizing interference in satellite communications using transiently chaotic neural networks
Computers & Mathematics with Applications
Competitive Hopfield network combined with estimation of distribution for maximum diversity problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Expert Systems with Applications: An International Journal
Frequency assignment problem in satellite communications using differential evolution
Computers and Operations Research
Expert Systems with Applications: An International Journal
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A novel neural-network approach called gradual neural network (GNN) is presented for a class of combinatorial optimization problems of requiring the constraint satisfaction and the goal function optimization simultaneously. The frequency assignment problem in the satellite communication system is efficiently solved by GNN as the typical problem of this class. The goal of this NP-complete problem is to minimize the cochannel interference between satellite communication systems by rearranging the frequency assignment so that they can accommodate the increasing demands. The GNN consists of N×M binary neurons for the N-carrier-M-segment system with the gradual expansion scheme of activated neurons. The binary neural network achieves the constrain satisfaction with the help of heuristic methods, whereas the gradual expansion scheme seeks the cost optimization. The capability of GNN is demonstrated through solving 15 instances in practical size systems, where GNN can find far better solutions than the existing algorithm