自我调节的群体，社会记忆，速度和动力学
On Self-Regulated Swarms, Societal Memory, Speed and Dynamics
摘要：
我们提出了一种自调节群（Self-Regulated Swarm，SRS）算法，该算法结合了群体智能的优点，通过在景观中放置集体信息素来形成社会环境记忆或认知地图（适当地平衡我们的动态搜索策略的探索/利用性质），通过一个简单的进化机制，通过与当地环境特征相关的直接繁殖过程，调节上述探索性群体种群，在全球范围内加速。为了测试他的自适应响应和鲁棒性，我们已经回到了不同的动态多模态复杂函数以及动态优化控制问题，测量反应速度和性能。最后的比较与标准的遗传算法（GAs），细菌觅食策略（BFOA），以及最近的协同进化方法。SRS能够证明快速的自适应响应，同时优于其他方法获得的结果。此外，还发现了一些成功的行为，其中最有趣的一个例子是，目前SRS集体群体的生物启发蚂蚁样代理能够跟踪约65%的移动峰值，其移动速度比组成精确群体跟踪系统的单个个体的速度快10倍。
英文摘要：
We propose a Self-Regulated Swarm (SRS) algorithm which hybridizes the advantageous characteristics of Swarm Intelligence as the emergence of a societal environmental memory or cognitive map via collective pheromone laying in the landscape (properly balancing the exploration/exploitation nature of our dynamic search strategy), with a simple Evolutionary mechanism that trough a direct reproduction procedure linked to local environmental features is able to self-regulate the above exploratory swarm population, speeding it up globally. In order to test his adaptive response and robustness, we have recurred to different dynamic multimodal complex functions as well as to Dynamic Optimization Control problems, measuring reaction speeds and performance. Final comparisons were made with standard Genetic Algorithms (GAs), Bacterial Foraging strategies (BFOA), as well as with recent Co-Evolutionary approaches. SRS's were able to demonstrate quick adaptive responses, while outperforming the results obtained by the other approaches. Additionally, some successful behaviors were found. One of the most interesting illustrate that the present SRS collective swarm of bio-inspired ant-like agents is able to track about 65% of moving peaks traveling up to ten times faster than the velocity of a single individual composing that precise swarm tracking system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
