Evolutionary method to optimize workplan mobile agent for the transport network application

This paper explains how to optimize the workplans of mobile agents (MAs) in order to enhance their performance. A MA travels through transport network looking for useful information and services. This information should be available daily to assist travelers to use multimodal transportation system. Our objective is to maximize the number of satisfied transport travelers. In other words, we attempt to increase the satisfaction of transport customers during their travels. So, we intend to optimize task assignment to a set of servers which can offer information with preset cost and processing time. With this intention, we adopted an approach based on evolutionary programs in order to solve our combinatorial problem. Therefore, it is very important to design an efficient representational scheme of a chromosome and develop effective genetic operators. We create a new representation of the chromosome where we integrated the constraints of our problem