<![CDATA[The stability and the fast response are two parameters to evaluate the efficiency of any system, and the acknowledgement of the mathematic model studied and its parameters are strongly required. In order to build the regulation and the control of the system, different methods are used. Some are traditional (PI, PD, PID…); whereas, others are modern (Fuzzy logic, neural networks, statistical algorithms, genetic algorithms, VGPI and so on…).In this paper, we focused on the presentation of a new method which we call the scheduling regulation based on a particle swarm optimization. A stochastic diffusion search method that takes inspiration from the social behaviors of real ants with their environment. Ant colony optimization algorithms (ACO) presents a promising performance which is a self-organized regulation system with no need to the acknowledgment of both the mathematic model and the parameters of the systems from a side, and it can insure the stability and the fast response of the system from another side.]]>
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