Computers and Industrial Engineering
Binary Optimization: On the Probability of a Local Minimum Detection in Random Search
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
On-Demand Chaotic Neural Network for Broadcast Scheduling Problem
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
A novel chaotic neural network with the ability to characterize local features and its application
IEEE Transactions on Neural Networks
A sequential approach for optimal broadcast scheduling in packet radio networks
IEEE Transactions on Communications
ICNC'09 Proceedings of the 5th international conference on Natural computation
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
A probabilistic greedy algorithm for channel assignment in cellular radio networks
IEEE Transactions on Communications
Improved stochastic competitive Hopfield network for polygonal approximation
Expert Systems with Applications: An International Journal
EURASIP Journal on Wireless Communications and Networking - Special issue on theoretical and algorithmic foundations of wireless ad hoc and sensor networks
Broadcast scheduling in packet radio networks using Harmony Search algorithm
Expert Systems with Applications: An International Journal
On-demand chaotic neural network for broadcast scheduling problem
The Journal of Supercomputing
Broadcast scheduling problem for TDMA ad-hoc networks
Proceedings of the 1st International Conference on Wireless Technologies for Humanitarian Relief
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In this paper, we propose a gradual noisy chaotic neural network (G-NCNN) to solve the NP-complete broadcast scheduling problem (BSP) in packet radio networks. The objective of the BSP is to design an optimal time-division multiple-access (TDMA) frame structure with minimal TDMA frame length and maximal channel utilization. A two-phase optimization is adopted to achieve the two objectives with two different energy functions, so that the G-NCNN not only finds the minimum TDMA frame length but also maximizes the total node transmissions. In the first phase, we propose a G-NCNN which combines the noisy chaotic neural network (NCNN) and the gradual expansion scheme to find a minimal TDMA frame length. In the second phase, the NCNN is used to find maximal node transmissions in the TDMA frame obtained in the first phase. The performance is evaluated through several benchmark examples and 600 randomly generated instances. The results show that the G-NCNN outperforms previous approaches, such as mean field annealing, a hybrid Hopfield network-genetic algorithm, the sequential vertex coloring algorithm, and the gradual neural network.