The laws of gravity and mass interactions inspire the gravitational search algorithm (GSA), which finds optimal regions of complex search spaces through the interaction of individuals in a population of particles. Although GSA has proven effective in both science and engineering, it is still easy to suffer from premature convergence especially facing complex problems. In this paper, we proposed a new hybrid algorithm by integrating genetic algorithm (GA) and GSA (GA-GSA) to avoid premature convergence and to improve the search ability of GSA. In GA-GSA, crossover and mutation operators are introduced from GA to GSA for jumping out of the local optima. To demonstrate the search ability of the proposed GA-GSA, 23 complex benchmark test functions were employed, including unimodal and multimodal high-dimensional test functions as well as multimodal test functions with fixed dimensions. Wilcoxon signed-rank tests were also utilized to execute statistical analysis of the results obtained by PSO, GSA, and GA-GSA. Experimental results demonstrated that the proposed algorithm is both efficient and effective.
Calibration of parameters of mathematical models is still a tough task in several engineering problems. Many of the models adopted for the numerical simulations of real phenomena, in fact, are of empirical derivation. Therefore, they include parameters which have to be calibrated in order to correctly reproduce the physical evidence. Thus, the success of a numerical model application depends on the quality of the performed calibration, which can be of great complexity, especially if the number of parameters is higher than one. Calibration is traditionally performed by engineers and researchers through manual trial-and-error procedures. However, since models themselves are increasingly sophisticated, it seems more proper to look at more advanced calibration procedures. In this work, in particular, an optimization technique for a multi-parameter calibration is applied to a two-phase depth-averaged model, already adopted in previous works to simulate morphodynamic processes, such as, for example, the dike erosion by overtopping.
The optimal and reliable performance of doubly fed induction generator is essential for the efficient and optimal operation of wind energy conversion systems. This paper considers the nonlinear dynamic of a DFIG linked to a power grid and presents a new robust model predictive control technique of active and reactive power by the use of the linear matrix inequality in DFIG-based WECS. The control law is obtained through the LMI-based model predictive control that allows considering both economic and tracking factors by optimization of an objective function, constraints on control signal and states of system and effects of nonlinearities, generator parameter uncertainties and external disturbances. Robust stability in the face of bounded disturbances and generator uncertainty is shown using Lyapunov technique. Numerical simulations show that the proposed control method is able to meet the desired specification in active and reactive power control in the presence of varieties of wind speed and pitch angle.
The optimization problem of two or more special-purpose functions of the energy system is subjected to an analysis. Based on experience of our research and general knowledge of partial solutions of energy system optimization at the level of control of production and power energy supply by energy companies in the Czech Republic, a special-purpose (cost) function has been defined. By analysing the special-purpose function, penalty and limitations have been defined. Using the fuzzy logic, a set of suitable solutions for the special-purpose function is accepted. An optimum of the special-purpose function is looked for using the simulated annealing method. The history of electricity consumption is sorted by day and by hour, representing the multidimensional data. When using the cluster analysis, type daytime diagrams of consumption are defined. Type daytime diagrams form prototypes of identified clusters. The so-called self-organizing neural network with Kohonen map attached is used to perform the cluster analysis. The result of our research is presented by an experiment.
The paper presents an issue of determination of the vertical index error of spatial optical scanners. The procedure for numerical calibration is presented and the methodology is verified with experimental data. The proposed procedure is basically intended for numerical calibrations of measured point-cloud data, and also to improve the output quality and to eliminate costs of mechanical calibrations of optical scanners. and V práci je analyzována problematika určení velikosti chyby čtení svislého úhlu při měření prostorovými optickými skenery. Je představen postup jejího numerického určení a metodika je aplikována a ověřena na experimentálních datech. Navržený postup může sloužit k numerickým kalibracím měřených dat mračen bodů, výrazně zvýšit kvalitu výstupů, a také eliminovat náklady vybrané mechanické kalibrace optických skenerů.
Web 2.0 has led to the expansion and evolution of web-based communities that enable people to share information and communicate on shared platforms. The inclination of individuals towards other individuals of similar choices, decisions and preferences to get related in a social network prompts the development of groups or communities. The identification of community structure is one of the most challenging task that has received a lot of attention from the researchers. Network community structure detection can be expressed as an optimisation problem. The objective function selected captures the instinct of a community as a group of nodes in which intra-group connections are much denser than inter-group connections. However, this problem often cannot be well solved by traditional optimisation methods due to the inherent complexity of network structure. Therefore, evolutionary algorithms have been embraced to deal with community detection problem. Many objective functions have been proposed to capture the notion of quality of a network community. In this paper, we assessed the performance of four important objective functions namely Modularity, Modularity Density, Community Score and Community Fitness on real-world benchmark networks, using Genetic Algorithm (GA). The performance measure taken to assess the quality of partitions is NMI (Normalized mutual information). From the experimental results, we found that the communities' identified by these objectives have different characteristics and modularity density outperformed the other three objective functions by uncovering the true community structure of the networks. The experimental results provide a direction to researchers on choosing an objective function to measure the quality of community structure in various domains like social networks, biological networks, information and technological networks.
The paper presents results of investigations on modeling and optimizing dynamic features of drive systems in order to minimize amplitudes of forces occurring in kinematic pairs of an electromechanical system with an ansynchronous motor and a vector control unit. The genetic algorithm was applied for optimization of design features of the system. The obtained results of numerical calculations confirmed the accuracy of the applied models and research methods for estimation of dynamic feature of drive systems. The set of design variables selected in the optimization process contained parameters describing design features of gear shafts and settings of control units. and Obsahuje seznam literatury
The demand for mobile communication has been steadily increasing
in recent years. With the limited frequency spectrum, the problem of channel assignment becomes increasingly important, i.e., how do we assign the calls to the available channels so that the interference is minimized while the demand is met? This problém is known to belong to a class of very difficult combinatorial optimization problems. In this paper, we apply the formulation of Ngo and Li with genetic algorithms to ten benchmarking problems. Interference-free Solutions cannot be found for soine of these problems; however, the approach is able to minimize the interference significantly. The results demonstrate the effectiveness of genetic algorithms in searching for optirnal Solutions in this complex optimization problem.
{Graphical models provide an undirected graph representation of relations between the components of a random vector. In the Gaussian case such an undirected graph is used to describe conditional independence relations among such components. In this paper, we consider a continuous-time Gaussian model which is accessible to observations only at time T. We introduce the concept of infinitesimal conditional independence for such a model. Then, we address the corresponding graphical model selection problem, i. e. the problem to estimate the graphical model from data. Finally, simulation studies are proposed to test the effectiveness of the graphical model selection procedure.}
Optimization methods are used to estimate parameters required for routing floods through open compound channels. Besides initial and boundary flow conditions, data required especially include, crosssectional area (A) of flow and conveyance (K) as functions of flow depth (y) for a representative crosssection of the study reach. Thus, instead of optimizing upon channel's geometric and hydraulic parameters, optimization is performed upon non-physical parameters in assumed A(y) and K(y) relationships. The optimization method selected for this application is the Nelder and Mead Simplex Algorithm. The objective function is expressed in terms of the relative differences between observed and simulated stages and discharges, which are evaluated based on the complete numerical solution of St Venant equations. This approach to formulating the optimization problem was applied to unsteady flow data sets for an experimental reach of the River Main in Northern Ireland. Based on statistical analysis, simulated and observed stages were found to be in good agreement. and Parametre potrebné pre kvantifikáciu transformácie povodňových vĺn v otvorených, zložených kanáloch, boli určené optimalizačnou metódou. Okrem počiatočných a okrajových podmienok sú potrebné ďalšie údaje, vrátane plochy priečneho rezu prúdom (A), ako aj vodivosť časti toku (K) ako funkcie hĺbky (y) pre reprezentatívny priečny rez. Namiesto optimalizácie geometrických a hydraulických parametrov kanála, optimalizácia sa vykonala pre nefyzické parametre, predpokladajúc závislosti A(y) a K(y). Vybranou metódou optimalizácie je Nelderov a Meadov Simplex Algoritmus. Funkcia je vyjadrená pomocou relatívnych rozdielov medzi pozorovanými a simulovanými vodnými stavmi a prietokmi, ktoré boli vyčíslené numerickým riešením rovníc St. Venanta. Tento spôsob formulácie optimalizačného problému bol aplikovaný na údaje pre neustálené prúdenie v experimentálnom priamom úseku rieky Main (River Main) v Severnom Írsku. Štatistickou analýzou bolo zistené, že simulované a merané vodné stavy boli veľmi blízke.