A new iterated local search algorithm using genetic crossover for the traveling salesman problem
Proceedings of the 1999 ACM symposium on Applied computing
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
TDMA scheduling algorithms for wireless sensor networks
Wireless Networks
Optimal broadcast scheduling in packet radio networks using mean field annealing
IEEE Journal on Selected Areas in Communications
IEEE Transactions on Neural Networks
Energy efficient spatial TDMA scheduling in wireless networks
Computers and Operations Research
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) in packet radio networks is a well-known NP-complete combinatorial optimization problem. The broadcast scheduling avoids packet collisions by allowing only one node transmission in each collision domain of a time division multiple access (TDMA) network. It also improves the transmission utilization by assigning one transmission time slot to one or more nodes; thus, each node transmits at least once in each time frame. An optimum transmission schedule could minimize the length of a time frame while minimizing the number of idle nodes. In this paper, we propose a new iterated local search (ILS) algorithm that consists of two special perturbation and local search operators to solve the BSPs. Computational experiments are applied to benchmark data sets and randomly generated problem instances. The experimental results show that our ILS approach is effective in solving the problems with only a few runtimes, even for very large networks with 2,500 nodes.