Multi-target Search Algorithm Based on Swarm Intelligence
Swarm Intelligence becomes a hot research field about multi-robots cooperation, this article presents an improved algorithm for multi-target search based on swarm intelligence. For each particle, an optimum value can be acquired, the behavior of each one can be determined by improved Particle Swarm Optimization, which can help the node decide which to do next In addition, this algorithm is easy to achieve in comparison with others. Large amounts of storage demand can be avoided through the way of exchanging local information, which corresponds with the needs of Swarm Intelligence. This algorithm has been proved by simulation, the result shows that it is more efficient comparing to the strolling algorithm.
Swarm Intelligence Multi-target search Particle Swarm Optimization
Xiao Wang Alei Liang Daoyong Liu Haibing Guan
School of Software Shanghai Jiao Tong University Shanghai, China Dept of Computer Science Shanghai Jiao Tong University Shanghai, China
国际会议
2010 International Conference on Future Information Technology(2010年未来信息技术国际会议 ICFIT 2010)
长沙
英文
1066-1069
2010-12-14（万方平台首次上网日期，不代表论文的发表时间）