Titlebook: Applications of Evolutionary Computing; EvoWorkshops 2009: E Mario Giacobini,Anthony Brabazon,Penousal Machado Conference proceedings 2009

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混沌

GRAIN

Location Discovery in Wireless Sensor Networks Using a Two-Stage Simulated Annealinghe estimations, propose a two-stage Simulated Annealing to solve the LD problem using this model, and discuss the results obtained. We will put a special stress on the improvements obtained by using our proposed technique.

Indicative

Swarm Intelligence Inspired Multicast Routing: An Ant Colony Optimization Approachction by the heuristics and a proper reinforcement proportion to the destination nodes is studied in the case experiments. Three types of heuristics are tested, and the results show that a modest heuristic reinforcement to the destination nodes can accelerate the convergence of the algorithm and achieve better results.

溫順

Efficient Signal Processing and Anomaly Detection in Wireless Sensor Networks network is used to process, classify and compress time series of event observations on sensor node level. The mechanism is lightweight and efficient. Based on simple computations, each node is able to report locally suspicious behavior. A system-wide decision is subsequently performed at a base station.

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責(zé)怪

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https://doi.org/10.1007/978-3-642-00418-6ven virtual topology while minimizing the resource usage. We develop and experiment with different evolutionary algorithm components. As a result, we propose a suitable evolutionary algorithm and show that it can be successfully used for this problem. Overall, the results are promising and promote further study.

引導(dǎo)

Hemant Choudhary,Kai Li,Robin D. Rogers the benchmarks from the OR-library show that the DPSO obtains better results when compared with traditional heuristic algorithms, and also outperforms the GA-based algorithm with faster convergence speed.