Scale Factor Inheritance Mechanism in Distributed Differential Evolution
This article proposes a distributed differential evolution which employs a novel self-adaptive scheme, namely scale factor inheritance. In the proposed algorithm, the population is distributed over several sub-populations allocated according to a ring topology. Each sub-population is characterized by its own scale factor value. With a probabilistic criterion, that individual displaying the best performance is migrated to the neighbor population and replaces a pseudo-randomly selected individual of the target sub-population. The target sub-population inherits not only this individual but also the scale factor if it seems promising at the current stage of evolution. In addition, a perturbation mechanism enhances the exploration feature of the algorithm. The proposed algorithm has been run on a set of various test problems and then compared to two sequential differential evolution algorithms and three distributed differential evolution algorithms recently proposed in literature and representing state-of-the-art in the field. Numerical results show that the proposed approach seems very efficient for most of the analyzed problems, and outperforms all other algorithms considered in this study.
Citation : Weber, M., Tirronen, V. and Neri, F. (2010) Scale Factor Inheritance Mechanism in Distributed Differential Evolution. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 14, (11), pages 1187-1207
ISSN : 1432-7643
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)