An Efficient Information Exchange Strategy in a Distributed Computing System - Application to the CARP

Distributed computation models have been widely used to enhance the performance of traditional evolutionary algorithms, and have been implemented on parallel computers to speed up the computation. In this paper, we introduce a multi-agent model conceived as a conceptual and practical framework for distributed genetic algorithms used both to reduce execution time and get closer to optimal solutions. Instead of using expensive parallel computing facilities, our distributed model is implemented on easily available networked personal computers (PCs). In order to show that the parallel co-evolution of different sub-populations may lead to an efficient search strategy, we design a new information exchange strategy based on different dynamic migration window methods and a selective migration model. To evaluate the proposed approach, different kinds of experiments have been conducted on an extended set of Capacitated Arc Routing Problem(CARP). Obtained results are useful for optimization practitioners and show the efficiency of our approach.