Maté: a tiny virtual machine for sensor networks
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
Network partition for switched industrial Ethernet using genetic algorithm
Engineering Applications of Artificial Intelligence
On Replacement Strategies in Steady State Evolutionary Algorithms
Evolutionary Computation
A knowledge-oriented meta-framework for integrating sensor network infrastructures
Computers & Geosciences
Agent Technologies for Sensor Networks
IEEE Intelligent Systems
Hybrid meta-heuristics algorithms for task assignment in heterogeneous computing systems
Computers and Operations Research
A hybrid honey bees mating optimization algorithm for the probabilistic traveling salesman problem
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
An ant colony optimization algorithm for the bi-objective shortest path problem
Applied Soft Computing
Effective Determination of Mobile Agent Itineraries for Data Aggregation on Sensor Networks
IEEE Transactions on Knowledge and Data Engineering
Modeling architecture for collaborative virtual objects based on services
Journal of Network and Computer Applications
Ant colony algorithm for traffic signal timing optimization
Advances in Engineering Software
Clustering algorithm based on the combination of genetic algorithm and ant colony algorithm
Proceedings of the 2011 International Conference on Innovative Computing and Cloud Computing
Review: An overview of the Internet of Things for people with disabilities
Journal of Network and Computer Applications
A hybrid Hopfield network-genetic algorithm approach for the terminal assignment problem
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Communications Magazine
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Perception nodes in Internet of Things are vulnerable to the external environment and the characteristics of them are stochastic and dynamic. In this paper a new optimization algorithm for Internet of Things to support applications which do not need to discrete the solution space has been proposed. The proposed algorithm which is called TSOIA divides perception nodes into three groups to search the global optimal solution. TSOIA algorithm adopts random search, local search and orientation search to adjust the group size and the step length adaptively. In order to show the performance of the TSOIA algorithm, computer simulations have been conducted and the results obtained are compared with that of the two existing search algorithms. The results of comparison show that the proposed algorithm outperforms other search algorithms in terms of search ability, energy consumption and network delay.