The present study has been taken up to emphasize the role of the hybridization process for optimizing a given reinforced concrete (RC) frame. Although various primary techniques have been hybrid in the past with varying degree of success, the effect of hybridization of enhanced versions of standard optimization techniques has found little attention. The focus of the current study is to see if it is possible to maintain and carry the positive effects of enhanced versions of two different techniques while using their hybrid algorithms. For this purpose, enhanced versions of standard particle swarm optimization (PSO) and a standard gravitational search algorithm (GSA), were considered for optimizing an RC frame. The enhanced version of PSO involves its democratization by considering all good and bad experiences of the particles, whereas the enhanced version of the GSA is made self-adaptive by considering a specific range for certain parameters, like the gravitational constant and a set of agents with the best fitness values. The optimization process, being iterative in nature, has been coded in C++. The analysis and design procedure is based on the specifications of Indian codes. Two distinct advantages of enhanced versions of standard PSO and GSA, namely, better capability to escape from local optima and a faster convergence rate, have been tested for the hybrid algorithm. The entire formulation for optimal cost design of a frame includes the cost of beams and columns. The variables of each element of structural frame have been considered as continuous and rounded off appropriately to consider practical limitations. An example has also been considered to emphasize the validity of this optimum design procedure.
This paper presents an approach based on NURBS (non-uniform rational B-splines) to achieve a seismic response surface (SRS) from a group of points obtained by using an analytical model of RC joints. NURBS based on the genetic algorithm is an important mathematical tool and consists of generalizations of Bezier curves and surfaces and B-splines. Generally, the accuracy of the design process of joints depends on the number of control points that are captured in the results of experimental research on real specimens. The values obtained from the specimens are the best tools to use in seismic analysis, though more expensive when compared to values simulated by SRSs. The SRS proposed in this paper can be applied to obtain surfaces that show site effect results on destructions of beam-column joint, taking into account different site conditions for a specific earthquake. The efficiency of this approach is demonstrated by the retrieval of simulated-versus-analytical results.
Cooling of the hot gas path components plays a key role in modern gas turbines. It allows, due to efficiency reasons, to operate the machines with temperature exceeding components' melting point. The cooling system however brings about some disadvantages as well. If so, we need to enforce the positive effects of cooling and diminish the drawbacks, which influence the reliability of components and the whole machine. To solve such a task we have to perform an optimization which makes it possible to reach the desired goal. The task is approached in the 3D configuration. The search process is performed by means of the evolutionary approach with floatingpoint representation of design variables. Each cooling structure candidate is evaluated on the basis of thermo-mechanical FEM computations done with Ansys via automatically generated script file. These computations are parallelized. The results are compared with the reference case which is the C3X airfoil and they show a potential stored in the cooling system. Appropriate passage distribution makes it possible to improve the operation condition for highly loaded components. Application of evolutionary approach, although most suitable for such problems, is time consuming, so more advanced approach (Conjugate Heat Transfer) requires huge computational power. The analysis is based on original procedure which involves optimization of size and location of internal cooling passages of cylindrical shape within the airfoil. All the channels can freely move within the airfoil cross section and also their number can change. Such a procedure is original.
Static Var Compensator (SVC) is a popular FACTS device for providing reactive power support in power systems and its placement representing the location and size has significant influence on network loss, while keeping the voltage magnitudes within the acceptable range. This paper presents a Firefly algorithm based optimization strategy for placement of SVC in power systems with a view of minimizing the transmission loss besides keeping the voltage magnitude within the acceptable range. The method uses a self-adaptive scheme for tuning the parameters in the Firefly algorithm. The strategy is tested on three IEEE test systems and their results are presented to demonstrate its effectiveness.
The cyclic modular approach is proposed for mechatronic object design. The approach is based on a new conceptual model of the object and a new algorithm of its design. The model consists of invariant and changeable parts. The parts have a hierarchical structure. The proposed algorithm allows for creating the object from the basis principle to the construction step by step. It makes it possible to design an adequate object in all forms of its representations: structure, schematic diagram, mathematical model and construction. Each of these forms has an invariant part, i.e. the structure of the functioning process of the object. Application of the proposed approach reduces the time needed for the object design.
Scheduling of multiobjective problems has gained the interest of the researchers. Past many
decades, various classical techniques have been developed to address the multiobjective problems,
but evolutionary optimizations such as genetic algorithm, particle swarm, tabu search
method and many more are being successfully used. Researchers have reported that hybrid
of these algorithms has increased the efficiency and effectiveness of the solution. Genetic
algorithms in conjunction with Pareto optimization are used to find the best solution for
bi-criteria objectives. Numbers of applications involve many objective functions, and application
of the Pareto front method may have a large number of potential solutions. Selecting
a feasible solution from such a large set is difficult to arrive the right solution for the decision
maker. In this paper Pareto front ranking method is proposed to select the best parents for
producing offspring’s necessary to generate the new populations sets in genetic algorithms.
The bi-criteria objectives minimizing the machine idleness and penalty cost for scheduling
process is solved using genetic algorithm based Pareto front ranking method. The algorithm
is coded in Matlab, and simulations were carried out for the crossover probability of 0.6,
0.7, 0.8, and 0.9. The results obtained from the simulations are encouraging and consistent
for a crossover probability of 0.6.
One of the least expensive and safest diagnostic modalities routinely used is ultrasound imaging. An attractive development in this field is a two-dimensional (2D) matrix probe with three-dimensional (3D) imaging. The main problems to implement this probe come from a large number of elements they need to use. When the number of elements is reduced the side lobes arising from the transducer change along with the grating lobes that are linked to the periodic disposition of the elements. The grating lobes are reduced by placing the elements without any consideration of the grid. In this study, the Binary Bat Algorithm (BBA) is used to optimize the number of active elements in order to lower the side lobe level. The results are compared to other optimization methods to validate the proposed algorithm.
A spectrum defragmentation problem in elastic optical networks was considered under the assumption that all connections can be realized in switching nodes. But this assumption is true only when the switching fabric has appropriate combinatorial properties. In this paper, we consider a defragmentation problem in one architecture of wavelength-spacewavelength switching fabrics. First, we discuss the requirements for this switching fabric, below which defragmentation does not always end with success. Then, we propose defragmentation algorithms and evaluate them by simulation. The results show that proposed algorithms can increase the number of connections realized in the switching fabric and reduce the loss probability.
The paper presents optimization of power line geometrical parameters aimed to reduce the intensity of the electric field and magnetic field intensity under an overhead power line with the use of a genetic algorithm (AG) and particle swarm optimization (PSO). The variation of charge distribution along the conductors as well as the sag of the overhead line and induced currents in earth wires were taken into account. The conductor sag was approximated by a chain curve. The charge simulation method (CSM) and the method of images were used in the simulations of an electric field, while a magnetic field were calculated using the Biot–Savart law. Sample calculations in a three-dimensional system were made for a 220 kV single – circuit power line. A comparison of the used optimization algorithms was made.
The goal of this paper is to explore and to provide tools for the investigation of the problems of unit-length scheduling of incompatible jobs on uniform machines. We present two new algorithms that are a significant improvement over the known algorithms. The first one is Algorithm 2 which is 2-approximate for the problem Qm|pj = 1, G = bisubquartic|Cmax. The second one is Algorithm 3 which is 4-approximate for the problem Qm|pj = 1, G = bisubquartic|ΣCj, where m ∈ {2, 3, 4}. The theory behind the proposed algorithms is based on the properties of 2-coloring with maximal coloring width, and on the properties of ideal machine, an abstract machine that we introduce in this paper.
Resonance assignment remains one of the hardest stages in RNA tertiary structure determination with the use of Nuclear Magnetic Resonance spectroscopy. We propose an evolutionary algorithm being a tool for an automatization of the procedure. NOE pathway, which determines the assignments, is constructed during an analysis of possible connections between resonances within aromatic and anomeric region of 2D-NOESY spectra resulting from appropriate NMR experiments. Computational tests demonstrate the performance of the evolutionary algorithm as compared with the exact branch-and-cut procedure applied for the experimental and simulated spectral data for RNA molecules.
This paper presents the current stage of the development of EA-MOSGWA – a tool for identifying causal genes in Genome Wide Association Studies (GWAS). The main goal of GWAS is to identify chromosomal regions which are associated with a particular disease (e.g. diabetes, cancer) or with some quantitative trait (e.g height or blood pressure). To this end hundreds of thousands of Single Nucleotide Polymorphisms (SNP) are genotyped. One is then interested to identify as many SNPs as possible which are associated with the trait in question, while at the same time minimizing the number of false detections.
The software package MOSGWA allows to detect SNPs via variable selection using the criterion mBIC2, a modified version of the Schwarz Bayesian Information Criterion. MOSGWA tries to minimize mBIC2 using some stepwise selection methods, whereas EA-MOSGWA applies some advanced evolutionary algorithms to achieve the same goal. We present results from an extensive simulation study where we compare the performance of EA-MOSGWA when using different parameter settings. We also consider using a clustering procedure to relax the multiple testing correction in mBIC2. Finally we compare results from EA-MOSGWA with the original stepwise search from MOSGWA, and show that the newly proposed algorithm has good properties in terms of minimizing the mBIC2 criterion, as well as in minimizing the misclassification rate of detected SNPs.
The problem of reconstructing an unknown disturbance under measuring a part of phase coordinates of a system of linear differential equations is considered. Solving algorithm is designed. The algorithm is based on the combination of ideas from the theory of dynamical inversion and the theory of guaranteed control. The algorithm consists of two blocks: the block of dynamical reconstruction of unmeasured coordinates and the block of dynamical reconstruction of an input.
The frictional resistance coefficient of ventilation of a roadway in a coal mine is a very important technical parameter in the design and renovation of mine ventilation. Calculations based on empirical formulae and field tests to calculate the resistance coefficient have limitations. An inversion method to calculate the mine ventilation resistance coefficient by using a few representative data of air flows and node pressures is proposed in this study. The mathematical model of the inversion method is developed based on the principle of least squares. The measured pressure and the calculated pressure deviation along with the measured flow and the calculated flow deviation are considered while defining the objective function, which also includes the node pressure, the air flow, and the ventilation resistance coefficient range constraints. The ventilation resistance coefficient inversion problem was converted to a nonlinear optimisation problem through the development of the model. A genetic algorithm (GA) was adopted to solve the ventilation resistance coefficient inversion problem. The GA was improved to enhance the global and the local search abilities of the algorithm for the ventilation resistance coefficient inversion problem.
Today’s human-computer interaction systems have a broad variety of applications in which automatic human emotion recognition is of great interest. Literature contains many different, more or less successful forms of these systems. This work emerged as an attempt to clarify which speech features are the most informative, which classification structure is the most convenient for this type of tasks, and the degree to which the results are influenced by database size, quality and cultural characteristic of a language. The research is presented as the case study on Slavic languages.
The concept of a hybrid scheme with connection of SIDH and ECDH is nowadays very popular. In hardware implementations it is convenient to use a classical key exchange algorithm, which is based on the same finite field as SIDH. Most frequently used hybrid scheme is SIDH-ECDH. On the other hand, using the same field as in SIDH, one can construct schemes over Fpn, like Diffie-Hellman or XTR scheme, whose security is based on the discrete logarithm problem. In this paper, idea of such schemes will be presented. The security of schemes, which are based on the discrete logarithm problem over fields Fp; Fp2 ; Fp4 ; Fp6 and Fp8 , for primes p used in SIDH, will be analyzed. At the end, the propositions of practical applications of these schemes will be presented.