The Maximum and Minimum Ant Colony Optimization Waking Strategy Based on Multi-Principle and Reprocessing

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 812)

Abstract

The sensor’s waking strategy is critical to the sensor network. The basic mathematical model of waking strategy is TSP problem. Some typical intelligent algorithms for TSP problem model include ant colony algorithm, genetic algorithm and so on. On the basis of the maximum and minimum ant colony algorithm, this paper improves the following disadvantages: According to the principle of choosing the city based on the pheromone principle, the non-contract principle is added, The principle of city selection and make it in the process of selecting the city, the priority to follow the principle of non-contract; in a single path after the help of the enumeration algorithm with the special advantages, refer to the enumeration algorithm part of the results of the re-processing of the path, The path can cover the search space. The improved algorithm avoids the occurrence of stagnation to a certain extent, weakening the blindness of search. The possibility of the results is increased by 25%, single results of the calculation time is reduced by 89.79%.

Keywords

Maximum and minimum ant colony algorithm Optimization algorithm Enumeration algorithm Traveling salesman problem Intelligent algorithm Waking strategy 

References

  1. 1.
    Chen, Q.: A logistic distribution routes solving strategy based on the physarum network and ant colony optimization algorithm. In: 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems (2015)Google Scholar
  2. 2.
    Sarigiannidis, P., Louta, M.: A metaheuristic bandwidth allocation scheme for FiWi networks using ant colony optimization, Department of Informatics. IEEE (2015)Google Scholar
  3. 3.
    Wang, Z., Xing, H., Li, T.: A modified ant colony optimization algorithm for network coding resource minimization. IEEE Trans. Evol. Comput. 20(3), 325–342 (2016)CrossRefGoogle Scholar
  4. 4.
    Zuo, L., Shu, L., Dong,S.: A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. Digital Object Identifier  https://doi.org/10.1109/access.2015.2508940
  5. 5.
    Huang, L., Zhang, B.: A novel bi-ant colony optimization algorithm for solving multi-objective service selection problem. In: 2015 11th International Conference on Natural Computation (ICNC) (2015)Google Scholar
  6. 6.
    Luo, Z., Lu, L.: An ant colony optimization-based trustful routing algorithm for wireless sensor networks. In: 2015 4th International Conference on Computer Science and Network Technology (2015)Google Scholar
  7. 7.
    Majumdar, S., Shivashankarm.: An efficient routing algorithm based on ant colony optimisation for VANETs. In: IEEE International Conference on Recent Trends in Electronics Information Communication Technology, India, 20–21 May 2016Google Scholar
  8. 8.
    Yang, J., Zhuang, Y.: An improved ant colony optimization algorithm for solving a complex combinatorial optimization problem. Appl. Soft Comput. 10(2), 653–660 (2010)CrossRefGoogle Scholar
  9. 9.
    Skakov, E., Malysh, V.: Ant colony optimization algorithms for wireless network planning problem solving. IEEE (2015)Google Scholar
  10. 10.
    Lu, E.H.-C.: Ant colony optimization solutions for logistic route planning with pick-up and delivery. In: 2016 IEEE International Conference on System, Man, and Cybernetics SMC (2016)Google Scholar
  11. 11.
    Huaifeng, Z.: Research on Improved Ant Colony Algorithm Based on Genetics,on Computer Applications and Software, vol. 1 (2011). (in Chinese with English abstract)Google Scholar
  12. 12.
    Di, J.: Complex network cluster structure detection - ant colony algorithm based on random walk. J. Softw. 451–464 (2010). (in Chinese with English abstract)Google Scholar
  13. 13.
    Quan, L.: A dynamic volatility and heuristic correction ant colony optimization algorithm. Comput. Res. Dev. 49(3), 620–627 (2012). (in Chinese with English abstract)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Computer Science and SoftwareHebei University of TechnologyTianjinChina

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