Bio-inspired cooperative optimization with application to bacteria motility

Inspired by bacterial motility, we propose an algorithm for adaptation over networks with mobile nodes. The nodes have limited abilities and they are allowed to cooperate with their neighbors to optimize a common objective function. In contrast to traditional adaptation formulations, an important consideration in this work is the fact that the nodes do not know the form of the cost function beforehand. The nodes can only sense variations in the values of the objective function as they diffuse through the space, such as sensing the variation in the concentration of nutrients in the environment. We propose a technique for the nodes to pick the search vector as a linear combination of the neighbors\´ last steps, by attempting to maximize the nutritional gradient. The procedure enables information to flow from "information-rich" nodes to the other nodes.