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�������պ����ѧѧ�� 2010, Vol. 36 Issue (3) :373-377    DOI:
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�β���1, �� ƽ1, ������1, �� ��2*
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Optimal deployment in sensor networks based on hybrid artificial fish school algorithm
Liao Canxing1, Zhang Ping1, Li Xingshan1, Zhang Yan2*
1. School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China;
2. Unit 95661 of PLA, Chongqing 400030, China

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Abstract�� A hybrid artificial fish school algorithm was presented for optimal nodes deployment of sensor networks. The hybrid artificial fish school algorithm included two phases. In speed priority phase, a suboptimal solution in the neighborhood of optimum solution was found rapidly by using the artificial fish school algorithm. In accuracy priority phase, taking the suboptimal solution as its initial solution and by using its monotonic convergence of the pattern search method, the solution to global extremum was led to. The merits of global search and rapid optimization of the artificial fish school algorithm were retained, and the search accuracy was improved. Node locations were optimized by artificial fish school algorithm, hybrid artificial fish school algorithm and particle swarm optimization in computer simulation for area coverage problem using the probabilistic detection model. Simulation results show that hybrid artificial fish school algorithm can effectively optimize the nodes deployment of sensor networks to improve coverage.
Keywords�� artificial intelligence   pattern search method   sensor networks   optimization     
Received 2009-07-23;
About author: �β���(1976-),��,���������,��ʿ��,liaocanxing@163.com.
�β���, �� ƽ, ������, �� ��.���ڻ���˹���Ⱥ�㷨�Ĵ����������Ż�[J]  �������պ����ѧѧ��, 2010,V36(3): 373-377
Liao Canxing, Zhang Ping, Li Xingshan, Zhang Yan.Optimal deployment in sensor networks based on hybrid artificial fish school algorithm[J]  JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2010,V36(3): 373-377
http://bhxb.buaa.edu.cn//CN/     ��     http://bhxb.buaa.edu.cn//CN/Y2010/V36/I3/373
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