Finding the optimal state of reality is the main purpose of the optimization process. The best variant from many possibilities is selected, and the effectiveness of the given system increases. Optimization has been applied in many real life engineering problems as in hydrological modelling. Within the hydrological case studies, the optimization process serves to estimate the best set of model parameters, or to train model weights in artificial neural networks.
Particle swarm optimization (PSO) is relatively recent optimization technique, which has only a few parameters to adjust, and is easy to implement to the selected problem. The original algorithm was modified by many authors. They
focused on changing the initialization of particles in the swarm, updating the population topology, adding new parameters into the equation, or incorporating shuffling mechanism into the algorithm.
The modifications of PSO algorithm improve the performance of the optimization, prevent the premature convergence, and decrease computation time. Therefore, the main aims of the presented doctoral thesis consist of proposal of a new PSO modification with its implementation in C++ programming language. More PSO variants were compared and analysed, and the best methods based on benchmark problems were applied in two hydrological case studies.
The first case study focused on utilization of PSO algorithms in inverse problem related to estimation of parameters of rainfall-runoff model Bilan. In the second case study, combination of artificial neural networks with PSO methods was introduced for forecasting the Standardized precipitation evapotranspiration drought index.
It was found out, that particle swarm optimization is a suitable tool for solving problems in hydrological modelling. The most effective PSO modifications are the one with adaptive version of parameter of inertia weight, which updates the velocity of particles during searching through the multidimensional space via feedback information. The shuffling mechanism and redistribution of particles into complexes, at which the PSO runs separately, also significantly improve the performance.
The contribution of this doctoral thesis lies in creation of new PSO modification, which was tested on benchmark problems, and was successfully applied in two hydrological case studies. The results of this thesis also extended the utilization of PSO methods in real life engineering optimization problems. All analysed PSO algorithms are available for later use within other research projects.
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:259657 |
| Date | January 2015 |
| Creators | Jakubcová, Michala |
| Contributors | Máca, Petr, Hanel, Martin |
| Publisher | Česká zemědělská univerzita v Praze |
| Source Sets | Czech ETDs |
| Language | Czech |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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