Computational experience with generalized simulated annealing over continuous variables
American Journal of Mathematical and Management Sciences
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Particle swarm with speciation and adaptation in a dynamic environment
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Stigmergic Optimization (Studies in Computational Intelligence)
Stigmergic Optimization (Studies in Computational Intelligence)
B-Cell Algorithm as a Parallel Approach to Optimization of Moving Peaks Benchmark Tasks
CISIM '07 Proceedings of the 6th International Conference on Computer Information Systems and Industrial Management Applications
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Locating and tracking multiple dynamic optima by a particle swarm model using speciation
IEEE Transactions on Evolutionary Computation
Multiswarms, exclusion, and anti-convergence in dynamic environments
IEEE Transactions on Evolutionary Computation
Properties of Quantum Particles in Multi-Swarms for Dynamic Optimization
Fundamenta Informaticae
| Hi-index | 0.00 |
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.