Modified multi-strategy artificial bee colony algorithm for optimising node coverage problem

被引：1
作者：
Zhou X. [1 ]
Liu Y. [1 ]
Wan J. [1 ]
Wang M. [1 ]
机构：
[1] School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, Jiangxi
来源：
International Journal of Wireless and Mobile Computing | 2020年 / 19卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Artificial bee colony; Node coverage optimisation problem; Superior information learning;
D O I：
10.1504/IJWMC.2020.111217
中图分类号：
学科分类号：
摘要：
To enhance exploitation for artificial bee colony (ABC) algorithm, we propose a modified multi-strategy ABC variant in which a superior information learning strategy is employed. In this strategy, the individuals can learn superior information from an exemplar which has better fitness value, and the exemplar is no longer acted by the global best individual or the elite group. In experiments, 22 well-known test functions are used and six well-established ABC variants are involved in the comparison. The results show that our approach performs better on most of test functions. Furthermore, our approach is applied to solve the node coverage optimisation problem in wireless sensor network. To this end, an improved Boolean sensing model is used to model the objective function, and simulation results indicate that our approach can provide promising performance. © 2020 Inderscience Enterprises Ltd.
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收藏
页码：292 / 301
页数：9
相关论文
共 22 条
  • [1] Bose D., Biswas S., Vasilakos A.V., Laha S., Optimal filter design using an improved artificial bee colony algorithm, Information Sciences, 281, pp. 443-461, (2014)
  • [2] Cui L., Li G., Lin Q., Du Z., Gao W., Chen J., Lu N., A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation, Information Sciences, 367, 368, pp. 1012-1044, (2016)
  • [3] Das S., Abraham A., Chakraborty U.K., Konar A., Differential evolution using a neighborhood-based mutation operator, IEEE Transactions on Evolutionary Computation, 13, 3, pp. 526-553, (2009)
  • [4] Gao W., Liu S., A modified artificial bee colony algorithm, Computers and Operations Research, 39, 3, pp. 687-697, (2012)
  • [5] Karaboga D., An Idea Based on Honey Bee Swarm for Numerical Optimization, (2005)
  • [6] Karaboga D., Gorkemli B., Ozturk C., Karaboga N., A comprehensive survey: artificial bee colony (ABC) algorithm and applications, Artificial Intelligence Review, 42, 1, pp. 21-57, (2014)
  • [7] Kong D., Chang T., Dai W., Wang Q., Sun H., An improved artificial bee colony algorithm based on elite group guidance and combined breadth-depth search strategy, Information Sciences, 442, 443, pp. 54-71, (2018)
  • [8] Kumar S., Lai T.H., Balogh J., On k-coverage in a mostly sleeping sensor network, Wireless Networks, 14, 3, pp. 277-294, (2008)
  • [9] Li S., Xu C., Pan W., Pan Y., Sensor deployment optimization for detecting maneuvering targets, Proceedings of the 8th International Conference on Information Fusion, pp. 1629-1635, (2005)
  • [10] Meng A., Chen Y., Yin H., Chen S., Crisscross optimization algorithm and its application, Knowledge-Based Systems, 67, pp. 218-229, (2014)
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