A mixed branch-and-bound and neural network approach for the broadcast scheduling problem
Design and application of hybrid intelligent systems
A Discrete-Time Quantized-State Hopfield Neural Network
Annals of Mathematics and Artificial Intelligence
A portable and scalable algorithm for a class of constrained combinatorial optimization problems
Computers and Operations Research
Genetic Programming and Evolvable Machines
Hybrid meta-heuristics algorithms for task assignment in heterogeneous computing systems
Computers and Operations Research
Neural Networks - 2005 Special issue: IJCNN 2005
Broadcast scheduling in wireless sensor networks using fuzzy Hopfield neural network
Expert Systems with Applications: An International Journal
On the application of linear transformations for genetic algorithms optimization
International Journal of Knowledge-based and Intelligent Engineering Systems
A robust knowledge-based plant searching strategy
Expert Systems with Applications: An International Journal
A combinatorial algorithm for the TDMA message scheduling problem
Computational Optimization and Applications
An efficient algorithm to find broadcast schedule in ad hoc TDMA networks
Journal of Computer Systems, Networks, and Communications
On-Demand Chaotic Neural Network for Broadcast Scheduling Problem
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
Hybrid meta-heuristics algorithms for task assignment in heterogeneous computing systems
Computers and Operations Research
A sequential approach for optimal broadcast scheduling in packet radio networks
IEEE Transactions on Communications
Solving inequality constraints job scheduling problem by slack competitive neural scheme
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Efficient algorithms to solve Broadcast Scheduling problem in WiMAX mesh networks
Computer Communications
New formulation for scheduling problem in multi-hop wireless sensor networks
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
Enhanced genetic algorithm for solving broadcast scheduling problem in TDMA based wireless networks
COMSNETS'10 Proceedings of the 2nd international conference on COMmunication systems and NETworks
Cross-layer transmission scheduling for direct-sequence spread-spectrum ad hoc networks
MILCOM'09 Proceedings of the 28th IEEE conference on Military communications
IEEE Transactions on Neural Networks
Mutation Hopfield neural network and its applications
Information Sciences: an International Journal
A probabilistic greedy algorithm for channel assignment in cellular radio networks
IEEE Transactions on Communications
Computers and Industrial Engineering
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
Capacity Optimization in TDMA Ad-Hoc Networks
Wireless Personal Communications: An International Journal
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Broadcast scheduling problem for TDMA ad-hoc networks
Proceedings of the 1st International Conference on Wireless Technologies for Humanitarian Relief
A rock-paper-scissors evolutionary algorithm for the TDMA broadcast scheduling problem
Computers and Operations Research
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The broadcast scheduling problem (BSP) arises in frame design for packet radio networks (PRNs). The frame structure determines the main communication parameters: communication delay and throughput. The BSP is a combinatorial optimization problem which is known to be NP-hard. To solve it, we propose an algorithm with two main steps which naturally arise from the problem structure: the first one tackles the hardest contraints and the second one carries out the throughput optimization. This algorithm combines a Hopfield neural network for the constraints satisfaction and a genetic algorithm for achieving a maximal throughput. The algorithm performance is compared with that of existing algorithms in several benchmark cases; in all of them, our algorithm finds the optimum frame length and outperforms previous algorithms in the resulting throughput.