Migrants selection and replacement in distributed evolutionary algorithms for Dynamic Optimization

Abstract

Many distributed systems (task scheduling, moving priorities, mobile environments, ...) can be linked as Dynamic Optimization Problems (DOPs), since they require to pursue an optimal value that changes over time. We have focused on the utilization of Distributed Genetic Algorithms (dGAs), one of the domains still to be investigated for DOPs. A dGA essentially decentralizes the population in islands which cooperate through migrations of individuals. In this article, we analyze the effect of the migrants selection and replacement on the performance of dGAs for DOPs. Quality and distance based criteria are tested using a comprehensive set of benchmarks. Results show the benefits and drawbacks of each setting for DOPs. © Springer International Publishing Switzerland 2013.