Bio inspired source seeking: a Hybrid Speeding Up and Slowing Down Algorithm
Bio inspired source seeking: a Hybrid Speeding Up and Slowing Down Algorithm
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生物启发寻源：混合加速和减速算法
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Fumin Zhang
中科院分区：
文献类型：
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作者：
Ayesha Khan;Vivek Mishra;Fumin Zhang
提出了一种新的仿生加速减速策略--混合加速减速策略，以实现多智能体系统在搜索空间中定位多源的分布式控制。混合SU.S.在生物启发的勘探算法和开发算法之间切换。探索算法以非零概率提供工作空间的覆盖。利用算法利用SU.S.策略进行源搜索，而不需要显式的梯度估计。根据代理进行的测量和代理可能拥有的邻居数量，开发在勘探和开发之间切换的条件。在搜索空间有限的情况下，混合搜索算法在定位震源方面的收敛是完全合理的。仿真结果证实，该策略允许每个智能体收敛到源位置之一。混合SU.S.可以用作分布式优化算法，其能够在受限的搜索空间内找到函数的所有极小值。
A novel bio-inspired strategy, the Hybrid Speeding Up Slowing Down (Hybrid SUSD) strategy, is introduced to achieve distributed control of a multi-agent system for the localization of multiple sources in a search space. Hybrid SUSD switches between bio-inspired exploration algorithms and exploitation algorithms. The exploration algorithms provide coverage of the workspace with non-zero probability. The exploitation algorithms leverage the SUSD strategy for source seeking without explicit gradient estimation. Conditions for switching between exploration and exploitation are developed based on measurements taken by an agent and the number of neighbors an agent may have. Given a confined search space, the convergence of the hybrid SUSD to locate a source is rigorously justified. Simulation results confirm that the strategy allows each agent to converge to one of the source locations. The Hybrid SUSD may be used as a distributed optimization algorithm that is able to find all minima of a function over a confined search space.