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Volume 12, Issue 3
Anycast Routing Algorithm Based on Krill Herd Optimization for Wireless Sensor Networks

Yun-li Gu, Xin Xu, Ya-ping Cheng and Yan-juan Zhang

J. Info. Comput. Sci. , 12 (2017), pp. 234-240.

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  • Abstract
Krill herd optimization is a novel bionic swarm intelligence optimization method, but currently it is mostly used only in the field of engineering optimization. Since node’s energy is limited and establishing effective routing is difficult in wireless sensor networks, in this paper, we try to apply krill herd optimization in anycast routing algorithm for wireless sensor networks. The krill are moved to the high fitness area (anycast paths with better energy consumption condition) through induced motion, foraging movement and random diffusion behaviors. Moreover, crossover and mutation operators in genetic reproduction mechanisms are adopted for improving the ability of accelerating optimization speed and breaking away from the local optimum. In comparison with ant colony optimization, simulation experiments results show that the performances of the proposed algorithm are better in terms of convergence speed, optimization results and scalability.
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@Article{JICS-12-234, author = {Yun-li Gu, Xin Xu, Ya-ping Cheng and Yan-juan Zhang}, title = {Anycast Routing Algorithm Based on Krill Herd Optimization for Wireless Sensor Networks}, journal = {Journal of Information and Computing Science}, year = {2024}, volume = {12}, number = {3}, pages = {234--240}, abstract = {Krill herd optimization is a novel bionic swarm intelligence optimization method, but currently it is mostly used only in the field of engineering optimization. Since node’s energy is limited and establishing effective routing is difficult in wireless sensor networks, in this paper, we try to apply krill herd optimization in anycast routing algorithm for wireless sensor networks. The krill are moved to the high fitness area (anycast paths with better energy consumption condition) through induced motion, foraging movement and random diffusion behaviors. Moreover, crossover and mutation operators in genetic reproduction mechanisms are adopted for improving the ability of accelerating optimization speed and breaking away from the local optimum. In comparison with ant colony optimization, simulation experiments results show that the performances of the proposed algorithm are better in terms of convergence speed, optimization results and scalability. }, issn = {1746-7659}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jics/22482.html} }
TY - JOUR T1 - Anycast Routing Algorithm Based on Krill Herd Optimization for Wireless Sensor Networks AU - Yun-li Gu, Xin Xu, Ya-ping Cheng and Yan-juan Zhang JO - Journal of Information and Computing Science VL - 3 SP - 234 EP - 240 PY - 2024 DA - 2024/01 SN - 12 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jics/22482.html KW - krill herd optimization, wireless sensor networks, anycast, routing algorithm. AB - Krill herd optimization is a novel bionic swarm intelligence optimization method, but currently it is mostly used only in the field of engineering optimization. Since node’s energy is limited and establishing effective routing is difficult in wireless sensor networks, in this paper, we try to apply krill herd optimization in anycast routing algorithm for wireless sensor networks. The krill are moved to the high fitness area (anycast paths with better energy consumption condition) through induced motion, foraging movement and random diffusion behaviors. Moreover, crossover and mutation operators in genetic reproduction mechanisms are adopted for improving the ability of accelerating optimization speed and breaking away from the local optimum. In comparison with ant colony optimization, simulation experiments results show that the performances of the proposed algorithm are better in terms of convergence speed, optimization results and scalability.
Yun-li Gu, Xin Xu, Ya-ping Cheng and Yan-juan Zhang. (2024). Anycast Routing Algorithm Based on Krill Herd Optimization for Wireless Sensor Networks. Journal of Information and Computing Science. 12 (3). 234-240. doi:
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