Tuning quantum multi-swarm optimization for dynamic tasks

Abstract

Heuristic approaches already proved their efficiency for the cases where real-world problems dynamically change in time and there is no effective way of prediction of the changes. Among them a mixed multi-swarm optimization (mSO) is regarded as the most efficient. The approach is a hybrid solution and it is based on two types of particle swarm optimization (PSO): pure PSO and quantum swarm optimization (QSO). Both types are applied in a set of simultaneously working sub-swarms. In spite of the fact that there appeared a series of publications discussing properties of this approach the motion mechanism of quantum particles was just briefly studied, and there is still some research to do. This paper presents the results of our research on this subject. The novelty is based on a new type of distributions of particles in a quantum cloud. Obtained results allow to derive some guidelines of an effective tuning of the mechanism of distribution in the quantum cloud and show that further improvement of mSO is possible. © 2008 Springer-Verlag Berlin Heidelberg.