SAC '98 Proceedings of the 1998 ACM symposium on Applied Computing
Journal of Global Optimization
A Memetic Pareto Evolutionary Approach to Artificial Neural Networks
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Linkage crossover operator for genetic algorithms
Linkage crossover operator for genetic algorithms
Minimizing Interference in Satellite Communications Using Chaotic Neural Networks
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
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
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Satellite communications technology has a tremendous impact in refining our world. The frequency assignment problem is of a fundamental importance when it comes to providing high-quality transmissions in satellite communication systems. The NP-complete frequency assignment problem in satellite communications involves the rearrangement of frequencies of one set of carriers while keeping the other set fixed in order to minimize the largest and total interference among carriers. In this paper, we present a number of algorithms, based on differential evolution, to solve the frequency assignment problem. We investigate several schemes ranging from adaptive differential evolution to hybrid algorithms in which heuristic is embedded within differential evolution. The effectiveness and robustness of our proposed algorithms is demonstrated through solving a set of benchmark problems and comparing the results with a number of previously proposed techniques that solve the same problem. Experimental results show that our proposed algorithms, in general, and hybrid ones in particular, outperform the existing algorithms both in terms of the quality of the solutions and computational time.