Competitive Environments Evolve Better Solutions for Complex Tasks
Proceedings of the 5th International Conference on Genetic Algorithms
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Directed flood-routing framework for wireless sensor networks
Proceedings of the 5th ACM/IFIP/USENIX international conference on Middleware
Hybrid sensor networks: a small world
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
Wireless Sensor Networks and Applications (Signals and Communication Technology)
Wireless Sensor Networks and Applications (Signals and Communication Technology)
Wireless Sensor Networks: Technology, Protocols, and Applications
Wireless Sensor Networks: Technology, Protocols, and Applications
New methods for competitive coevolution
Evolutionary Computation
Enhanced tree routing for wireless sensor networks
Ad Hoc Networks
IEEE Communications Magazine
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This work proposes a cooperative coevolutionary algorithm for the design of a wireless sensor network considering complex network metrics. It is proposed an heuristic based on cooperative coevolution to find a network configuration such that its communication structure presents a small value for the average shortest path length and a high cluster coefficient. This configuration considers a cluster based network, where the cluster heads have two communication radii. The mathematical model of the cluster head location problem was developed to determine the nodes which will be configured as cluster heads. This model was adopted within the coevolutionary algorithm. We describe how the problem can be partitioned and how the fitness computation can be divided such that the cooperative coevolution model is feasible. The results reveal that our methodology allows the configuration of networks with more than a hundred nodes with two specifics complex network measurements allowing the reduction of energy consumption and the data transmission delay.