Towards a Swarm Optimization Algorithm with “logistic“ Agents

Adaptation and self-organization are two main aspects of swarm mechanisms and collective intelligence. We adress here the global question of controlling the coordination of groups of mobile agents in order to achieve optimization processings. We study in this paper a simple case involving “logistic“ agents –whose internal decision is governed by a logistic map, that is a discrete parametrized quadratic map– slaved to a stochastic environment through their control parameter. The proposed algorithm enables agents to find local minima in the environment. We show that the adaptation process on the control parameter leads to this local optimization. Applications may be envisaged for multi-objective problems.