Iskanje po katalogu digitalne knjižnice

Opis: Reporting the empirical results of swarm and evolutionary computation algorithms is a challenging task with many possible difficulties. These difficulties stem from the stochastic nature of such algorithms, as well as their inability to guarantee an optimal solution in polynomial time. This research deals with measuring the performance of stochastic optimization algorithms, as well as the confidence intervals of the empirically obtained statistics. Traditionally, the arithmetic mean is used for measuring average performance, but we propose quantiles for measuring average, peak and bad-case performance, and give their interpretations in a relevant context for measuring the performance of the metaheuristics. In order to investigate the differences between arithmetic mean and quantiles, and to confirm possible benefits, we conducted experiments with 7 stochastic algorithms and 20 unconstrained continuous variable optimization problems. The experiments showed that median was a better measure of average performance than arithmetic mean, based on the observed solution quality. Out of 20 problem instances, a discrepancy between the arithmetic mean and median happened in 6 instances, out of which 5 were resolved in favor of median and 1 instance remained unresolved as a near tie. The arithmetic mean was completely inadequate for measuring average performance based on the observed number of function evaluations, while the 0.5 quantile (median) was suitable for that task. The quantiles also showed to be adequate for assessing peak performance and bad-case performance. In this paper, we also proposed a bootstrap method to calculate the confidence intervals of the probability of the empirically obtained quantiles. Considering the many advantages of using quantiles, including the ability to calculate probabilities of success in the case of multiple executions of the algorithm and the practically useful method of calculating confidence intervals, we recommend quantiles as the standard measure of peak, average and bad-case performance of stochastic optimization algorithms.
Ključne besede: algorithmic performance, experimental evaluation, metaheuristics, quantile, confidence interval, stochastic algorithms, evolutionary computation, swarm intelligence, experimental methodology
Objavljeno v DKUM: 28.03.2025; Ogledov: 0; Prenosov: 7
Celotno besedilo (490,48 KB)
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Opis: This doctoral thesis focuses on the efficient management of a power system with a large share of wind-power generation and on the methods for planning the operating reserve. The approach proposed in this research involves the use of data directly from the network’s dispatch center and the forecasting systems, as well as already existing day-ahead plans for wind and load power to determine the regulating reserve for the load frequency control (LFC). The same principle is used to find the optimal regulating reserve distribution (RRD) between the available generation units in the system for automatic generation control. It is developed using iterative power-flow computation with a reactive power correction and a stochastic search algorithm for transmission-loss minimization. Moreover, variations in the generation of individual wind-power plants (WPPs) from the plan are considered in the optimal RRD. A special focus of this research has been put on the Croatian power system due to the accessibility of relevant information and operating data. The obtained results of the testing with actual data from the Croatian power system indicate substantial savings in ancillary service costs for the LFC, while ensuring safe system operation. The considerable impact of variations from the plan for each individual WPP generation on the optimal RRD was also identified. Comparing the results for the daily sum of regulating reserves obtained with the proposed and the currently used approaches, the corresponding costs indicate a total saving of 21.2% for all 12 selected days in 2015 when using the proposed approach. In practice, a part of the reserve can be slow, i.e., with a lower unit price, thus the savings would be even higher. Furthermore, the obtained optimal RRD computed according to the proposed approach was compared with four commonly used RRDs in Croatia. The results obtained using the proposed approach indicate a decrease in the total transmission losses of between 1% and 2%. With a larger share of the generation from the WPPs and with more dispersed locations of the WPPs and regulating power plants a larger reduction in the transmission losses is expected.
Ključne besede: Evolutionary computation, Frequency control, Optimization, Power system, Regulating reserve, Wind power generation
Objavljeno v DKUM: 28.03.2018; Ogledov: 1951; Prenosov: 439
Celotno besedilo (9,03 MB)

Opis: Swarm intelligence is a modern and efficient mechanism for solving hard problems in computer science, engineering, mathematics, economics, medicine and optimization. Swarm intelligence is the collective behavior of decentralized and self-organized systems. This research area is a branch of artificial intelligence and could be viewed as some kind of family relationship with evolutionary computation because both communities share a lot of common characteristics. To date, a lot of swarm intelligence algorithms have been developed and applied to several real-world problems. The main focus of this thesis is devoted to the bat algorithm which is a member of the swarm intelligence community, as developed recently. In line with this, a comprehensive analysis of papers was performed tackling this algorithm. Some hybridizations of the original algorithm were proposed because the preliminary results of this algorithm regarding the optimization of benchmark functions with higher dimensions had not too promising. Extensive experiments showed that the hybridizing the original bat algorithm has beneficial effects on the results of the original bat algorithm. Finally, an experimental study was performed during which we researched for the dependence of an applied randomized method on the results of the original bat algorithm. The results of this study showed that selecting the randomized method had a crucial impact on the results of the original bat algorithm and that the bat algorithm using Levy flights is also suitable for solving the harder optimization problems.
Ključne besede: swarm intelligence, evolutionary computation, bat algorithm, hybridization, review
Objavljeno v DKUM: 06.09.2013; Ogledov: 3471; Prenosov: 335
Celotno besedilo (8,69 MB)