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.
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 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.}
The problem to maximize the information divergence from an exponential family is generalized to the setting of Bregman divergences and suitably defined Bregman families., Johannes Rauh., and Obsahuje bibliografické odkazy
A model experimental investigation of the accuracy of ground station coordinates, determined by Doppler satellite observations is made, depending on the number of passes, satellite altitude, orbit inclination, frequency of Doppler transmitter, intervals of Doppler counts, etc. Using satellite pases at altitudes of 1000, 3000 a 5000 km, optimization of the Doppler observations is also achieved.
In the framework of a stochastic optimization problem, it is assumed that the stochastic characteristics of optimized system are estimated from randomly right-censored data. Such a case is frequently encountered in time-to-event or lifetime studies. The analysis of precision of such a solution is based on corresponding theoretical properties of estimated stochastic characteristics. The main concern is to show consistency of optimal solution even in the random censoring case. Behavior of solutions for finite data sizes is studied with the aid of randomly generated example.