Affiliation:
1. University of Ioannina, Greece
2. University of Patras, Greece
Abstract
This chapter discusses the workings of PSO in two research fields with special importance in real-world applications, namely noisy and dynamic environments. Noise simulation schemes are presented and experimental results on benchmark problems are reported. In addition, we present the application of PSO on a simulated real world problem, namely the particle identification by light scattering. Moreover, a hybrid scheme that incorporates PSO in particle filtering methods to estimate system states online is analyzed, and representative experimental results are reported. Finally, the combination of noisy and continuously changing environments is shortly discussed, providing illustrative graphical representations of performance for different PSO variants. The text focuses on providing the basic concepts and problem formulations, and suggesting experimental settings reported in literature, rather than on the bibliographical presentation of the (prohibitively extensive) literature.
Reference48 articles.
1. Arnold, D. V. (2001). Local performance of evolution strategies in the presence of noise. Ph.D. thesis, Department of Computer Science, University of Dortmund, Germany.
2. Arnold, D. V. (2002). Noisy optimization with evolution strategies. Boston: Kluwer Academic Publishers.
3. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
4. Bartz-Beilstein, T., Blum, D., & Branke, J. (2007). Particle swarm optimization and sequential sampling in noisy environments. In K.F. Doerner, M. Gendreau, P. Greistorfer, W. Gutjahr, R.F. Hartl, & M. Reimann (Eds.), Metaheuristics (Progress in Complex System Optimization), Operations Research/Computer Science Interfaces series, Vol. 39, Part V (pp. 261-273). Berlin: Springer.
5. Evolutionary algorithms in noisy environments: theoretical issues and guidelines for practice