动态环境中蚂蚁和细菌的计算趋化性
Computational Chemotaxis in Ants and Bacteria over Dynamic Environments
摘要：
趋化性可以被定义为生物体对定向刺激的先天行为反应，其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指导其运动。这对细菌寻找食物很重要（例如，葡萄糖）游向最高浓度的食物分子，或逃离毒药。基于自组织计算方法和类似的stigmergic概念，我们得到了一个新的群体智能算法。从这些观察中得出的结论是，真社会性昆虫（如蚁群）和细菌都有类似的基于耻辱的自然机制，以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，以解决社会群体的集体适应基于真实的蚁群行为（SSA算法）跟踪极值在动态环境和高度多模态的复杂功能中描述的著名的德容测试套件。随后，为了比较的目的，最近的人工细菌觅食模型（BFOA算法）的基础上类似的stigmergic功能进行了描述和分析。最终结果表明，SSA集体智慧是能够应付和快速适应不可预见的情况下，即使在同一合作觅食期间，社会被要求处理两个不同的和矛盾的目的，而优于BFOA的适应速度。结果表明，该方法可以很好地处理严重的动态优化问题。
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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