动态环境中蚂蚁和细菌的计算趋化性

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摘要翻译：
趋化性可以定义为生物体对定向刺激的一种先天行为反应，在这种反应中，细菌和其他单细胞或多细胞生物体根据其环境中的某些化学物质来指导它们的运动。这对于细菌通过游向食物分子的最高浓度来寻找食物（如葡萄糖）或逃离毒物是很重要的。基于自组织计算方法和相似的污名概念，我们导出了一种新的群体智能算法。从这些观察中得到的启示是，作为蚁群的生态昆虫和细菌都有类似的基于污名的自然机制，以便形成连贯和复杂的全球集体行为模式。考虑到上述特点，我们将提出一个简单的模型来解决社会群体的集体适应问题，该模型基于真实蚁群行为（SSA算法），用于跟踪动态环境中的极值和众所周知的De Jong测试套件中描述的高度多峰复杂函数。随后，为了进行比较，描述和分析了一种基于相似污迹特征的人工细菌觅食模型（BFOA算法）。结果表明，即使在相同的合作觅食期内，群体被要求处理两种不同的、相互矛盾的目的时，SSA集体智能也能够应对和快速适应不可预见的情况，而在适应速度上优于BFOA。结果表明，该方法能较好地解决严重的动态优化问题。
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英文标题：
《Computational Chemotaxis in Ants and Bacteria over Dynamic Environments》
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作者：
Vitorino Ramos, C. M. Fernandes, A. C. Rosa, A. Abraham
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最新提交年份：
2007
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Multiagent Systems        多智能体系统
分类描述：Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Quantitative Biology        数量生物学
二级分类：Populations and Evolution        种群与进化
分类描述：Population dynamics, spatio-temporal and epidemiological models, dynamic speciation, co-evolution, biodiversity, foodwebs, aging; molecular evolution and phylogeny; directed evolution; origin of life
种群动力学；时空和流行病学模型；动态物种形成；协同进化；生物多样性；食物网；老龄化；分子进化和系统发育；定向进化；生命起源
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一级分类：Quantitative Biology        数量生物学
二级分类：Quantitative Methods        定量方法
分类描述：All experimental, numerical, statistical and mathematical contributions of value to biology
对生物学价值的所有实验、数值、统计和数学贡献
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英文摘要：
  Chemotaxis can be defined as an innate behavioural response by an organism to a directional stimulus, in which bacteria, and other single-cell or multicellular organisms direct their movements according to certain chemicals in their environment. This is important for bacteria to find food (e.g., glucose) by swimming towards the highest concentration of food molecules, or to flee from poisons. Based on self-organized computational approaches and similar stigmergic concepts we derive a novel swarm intelligent algorithm. What strikes from these observations is that both eusocial insects as ant colonies and bacteria have similar natural mechanisms based on stigmergy in order to emerge coherent and sophisticated patterns of global collective behaviour. Keeping in mind the above characteristics we will present a simple model to tackle the collective adaptation of a social swarm based on real ant colony behaviors (SSA algorithm) for tracking extrema in dynamic environments and highly multimodal complex functions described in the well-know De Jong test suite. Later, for the purpose of comparison, a recent model of artificial bacterial foraging (BFOA algorithm) based on similar stigmergic features is described and analyzed. Final results indicate that the SSA collective intelligence is able to cope and quickly adapt to unforeseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes, while outperforming BFOA in adaptive speed. Results indicate that the present approach deals well in severe Dynamic Optimization problems.
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PDF链接：
https://arxiv.org/pdf/0712.0744

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关键词：Intelligence Quantitative Environments Mathematical Presentation ant 模型 环境 based stigmergic