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The paper examines the application of systematic and risk-based approaches to problem solving in the agro-industrial complex, which generally prompted research in the field of various model-based methodologies. Of particular interest to this study is the Quality by Design initiative in the agro-industrial complex. Motivated by their need, this paper reports some of the contributions to analysing the existence and availability of the experimental plan. The analysis of the feasibility and adaptation of the nested sampling algorithm for the probabilistic characteristics of the design space is considered. There is described an initial adaptation of the nested sampling algorithm, common for large Bayesian calculations, for the probabilistic characterization of spatial design, which is a key work for practitioners in the agro-industrial complex. The choice-based approach has been found to be effective with the optimization approach, allowing practitioners to take advantage of the choice-based approach with other methods. A step-by-step technology is given for complex and one-time implementations of the original nested sample for spatial design, which further reduces the computational load, thereby allowing solutions of more complex problems. Design-centring methodology is shown as an alternative coding method for methodological choice, providing the practitioner with a convenient format for communicating with process operators. A particular attention is paid to several pronounced problems that arise in the special design of experiments when there are various levels of model uncertainty, which are often encountered in the early stages of model development. The paper presents the results of the study obtained by developing optimal experimental queries in the presence of restrictions. [ABSTRACT FROM AUTHOR]

In this paper, two new hybrid algorithms are proposed to solve global optimization problems. The first algorithm combines the sooty tern optimization algorithm with the arithmetic optimization algorithm, and the second algorithm combines the sooty tern optimization algorithm with the conjugate gradient method. The sooty tern optimization algorithm is a metaheuristic algorithm proposed by Dhiman and Kaur in 2019 Sooty migration and attack behaviors seabirds in nature were the primary inspiration for this algorithm. In this paper, this algorithm was hybridized using the arithmetic optimization algori thm and the conjugate gradient method, and the two algorithms were measured by applying them to 10 functions, and the results of the hybrid algorithms were very good compared to the original algorithm. [ABSTRACT FROM AUTHOR]

With the spread of the Internet and a large number of network users, the problem of congestion on the network has arisen. This paper presents a strategy to reduce network congestion using clustering algorithms, K-means, and shortest path algorithms. The paper aims to solve the problems of congestion control, access to quality of service, reduce time wasted when sending and receiving network users, take advantage of network devices, and provide quality of service without data loss. The results of the proposed system simulation showed its effectiveness and accuracy through the simulation of the system. [ABSTRACT FROM AUTHOR]

One of the profound things that human being has ventured is to demystify the natural process that is happening in nature. Nature inspires technology to solve real-time problems. There are various nature-inspired techniques based on insects that are used in population-based or ecology-based nature-inspired algorithms. In this paper, the nature-inspired Bat Algorithm, which is based on the echolocation behavior of bats, is discussed in detail. The Bat Algorithm has various hybrids which are used in numerous applications; all these hybrids are reviewed and discussed in this paper. Systematic analysis is conducted using Scopus and WoS databases to analyze the research trend for these hybrid algorithms, study its variants and application domains, and predict the future for these nature-inspired algorithms. Numerous experiments based upon year-wise analysis, correlation of related terms, article growth, etc. are conducted and described in this paper. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms show outstanding performance when they are applied to optimization problems with a few variables, i.e. problems with less than a hundred continuous variables. Large-scale global optimization with continuous variables is still a challenging task for a wide range of evolutionary algorithms. Their performance decreases when the number of variables increases because the search space grows exponentially. Classic evolutionary algorithms cannot find a good solution using the allocated resources. A cooperative coevolution approach is a good tool for increasing the performance of an optimizer in solving high-dimensional problems. The approach splits the objective vector into a predefined number of parts (subcomponents), and each part is optimized by its optimizer. This paper makes an effort to solve the problem of selecting the number of subcomponents. The paper represents a novel metaheuristic for solving optimization problems with a huge number of continuous variables. The suggested approach is based on the self-adaptive cooperation of algorithms and the cooperative coevolution approach. Each algorithm has a unique number of subcomponents. The metaheuristic automatically allocates resources between algorithms during the optimization process. Algorithms optimize the same population one by one. The proposed metaheuristic is titled COSACC, coordination of self-adaptive cooperative coevolution algorithms. We have evaluated the proposed algorithm on fifteen problems from the IEEE LSGO CEC'2013 benchmark. The study demonstrates that COSACC outperforms in average cooperative coevolution algorithms with the static number of subcomponents. Wilcoxon test has proven the results of numerical experiments. We have tested COSACC performance with other state-of-the-art metaheuristics, COSACC is a competitive approach. [ABSTRACT FROM AUTHOR]

Solving the problem of constructing the trajectory of a UAV in a complex urban area is a difficult task. The possibility of automated formation of the trajectory of movement, without the operator's participation, allows to reduce errors and simplify the task of monitoring in conditions of a single movement. The paper considers the methodology and algorithm for constructing the trajectory of the UAV movement in the conditions of urban development. The criterion for constructing the motion curve is the possibility of a UAV flying between objects, equidistance from their boundaries, and the possibility of constructing a closed trajectory. The article contains a block diagram of the algorithm, its description, and examples of constructing a trajectory based on simulation. The algorithm obtained in the article made it possible to perform automated construction of the UAV motion curve in a complex environment along an equidistant trajectory. The proposed approach made it possible to build the route 35% faster than the primary algorithm. The proposed computational operations are based on simple calculations and can be obtained onboard the UAV. [ABSTRACT FROM AUTHOR]

The 0/1 Knapsack Problem (KP) is one of the problems in optimization where a set of items with given benefit and weights.The aim is to select a subset of the items in order to maximize the total benefit without exceeding the knapsack capacity. Nowadays, it becomes a big dilemma due to the explosion in the amount of datasets. In this paper, P(KD)P algorithm has been proposed and performed. For effectively calculating the KM matrix using multi- core system, an improved approach of the DP_KP algorithm has been considered. The proposed scheduling and partitioning approaches have accomplished a significant enhancement to the overall performance. Executions were implemented on corei7 with (8 cores). It outperforms sequential KP_DP more than 16-fold speedup and (KD)P more than 5-fold speedup. Its efficiency reaches 0.316, 0.0829 and 0.0979 over the KP_DP, (KD)P, and P(KD)P respectively. [ABSTRACT FROM AUTHOR]

In this paper, an efficient location-based recommender system has been proposed. The proposed method, consists of three main steps. In the first phase of proposed scheme, suggestible items are ranked on the basis of user's visiting logs in his/her profile. In the second phase of the proposed method, we have used a user attribute-based item ranking scheme to solve the problems of unvisited locations and lack of information about new users. Thus, in the second phase, most similar users to target user are detected using cosine similarity first. Then, exploration history of similar user is described as a form of directed graph. In this graph, items are described as graph nodes and user movement between locations is shown as an edge which is used for ranking nodes (items) by PageRank method. Finally, in the third phase of proposed algorithm, a weighted average of rankings vectors is calculated and most suitable items are recommended to the target user. The proposed method was evaluated using Yelp dataset and obtained results were also compared with previous methods. The results show that the hybrid approach used in the proposed method will improve the performance of the recommender systems. [ABSTRACT FROM AUTHOR]

Enrichment of power system stability plays a major role in the current ages. This act is due to the increase of power system structures which causes a low frequency oscillation. These problems are solved by implementing various methods. This paper gives the best solution to solve the low frequency oscillation problems which are based on optimization criteria of a generator and load angle. For better tuning of the controller parameters, Butterfly optimization algorithm is used. The simulation results for Butterfly Algorithm based controller design are compared with the performances of conventional and Firefly Algorithm based controllers. [ABSTRACT FROM AUTHOR]

Submarine cameras are commonly used to track and observe the seabed. They are often used in under-water robotics, marine exploration and oceanography. The under-water cameras face several faces due to under-water optics. As the light travelling through the water is absorbed and scattered due to small particles that brings noise in the captured images. Many approaches have been suggested over the past five years to solve the conventional problems of under-water imaging. This review paper deals with under-water image enhancement (UIE) methods and existing dataset. At last discussion on the identified gaps is included that will help the researchers to overcome the gaps identified. [ABSTRACT FROM AUTHOR]

The artificial neural network has several training functions that can speed up the training process of the standard backpropagation algorithm. Therefore, the purpose of this research is to evaluate the scaled conjugate gradient algorithm's capability and performance, which develops the training function of standard backpropagation to solve computational problems. The dataset used in this paper uses quantitative data from export data of jewelry and valuable goods by the leading destination country, which is processed from documents customs of the Directorate General of Customs and Excise (PEB and PIB) and quoted from the Indonesian Statistical Publication. A network architecture model will be formed and determined based on this data, including 7-7-1, 7-14-1, and 7-21-1. Based on these three models after training and testing, the results show that the model with the best performance and accuracy is 7-14-1 with a performance value of 0.001118426, the lowest among the three other models and an accuracy of 90.9% (higher than the two models other). So it can be concluded that the SCG algorithm with the 7-14-1 model can be used to solve computational problems, as evidenced by the best performance and accuracy values. [ABSTRACT FROM AUTHOR]

Now a day's test case optimization is necessary as it can improve efficiency and reduce computational cost. In optimization problem only best solution has to select out of all the possible solutions for problem that cannot be solved in polynomial time. So optimization is NP complete problem. Traditional approach was not found competent to solve complex problems thus nature inspired algorithms has been recognized for their competency. Nature inspired algorithm are inspired by natural phenomena. These algorithms are applied to optimize problem and provide acceptable and feasiblesolution in reasonable time. In the present paper an attempt has been made to discuss various nature inspired algorithms for test suite optimization. We have mainly discussed biology inspired algorithm and try to touch social phenomena inspired algorithm. It is concluded that GA is most widely used algorithm but now researchers are moving towards newly added nature inspired techniques and exploring hybrid techniques. [ABSTRACT FROM AUTHOR]

The paper considers the algorithm for solving axisymmetric contact problems of thermoelasticity, taking into account the processes of creep. To take into account the contact interaction of bodies, the mortar method was used, the modified symmetric successive upper relaxation method (MSSOR) was used to solve the obtained system of linear equations. For the numerical solution of the problem modeling the creep process, time discretization based on the implicit Euler method was applied, the Newton method was used to linearize the resulting system of equations. An algorithm with automatic step selection based on obtaining an estimate of local error of the method is proposed. The results of the application of the proposed a lgorithm to the demo problem, which simulate thermomechanical processes in the section of the fuel element, which includes from 1 to 10 fuel pellets are presented. [ABSTRACT FROM AUTHOR]

In this paper, we consider the study of the extreme problem of the fictitious domain method based on the use of the Lagrange functional, with a multiplier defined on the actual boundary and associated with genuine boundary conditions. The theorem of the existence of a generalized solution of the auxiliary FDM problem is proved. The convergence of the solution of the auxiliary FDM problem is shown. For mathematical research, the theory of functional spaces and the technique of obtaining a priori estimates in functional spaces are used. To verify the effectiveness of the developed method, numerical analysis and comparison with other methods of solving the problem were carried out. [ABSTRACT FROM AUTHOR]

In this paper, we study an algorithmic model for calculating estimates and prove the existence of an optimal algorithm in this model. For problems with disjoint classes, a much simpler in description and much more efficient computational algorithm is constructed. Methods are being developed that make it possible to code operator B more economically, which will reduce the required memory and more efficiently use the constructed correct algorithms for solving applied problems. [ABSTRACT FROM AUTHOR]

The paper considers algorithms that allow to effectively solve the problem of synthesis of invariant control systems for dynamic objects in the case of external influences that are not accessible to direct measurement. Regularization methods are applied, while the problem of regularization and stable estimation is solved. The estimates of unmeasurable impacts obtained in this case have the properties of asymptotic stability. [ABSTRACT FROM AUTHOR]

Currently, the quality management of production processes is not only an integral part of the enterprise management system, but also largely determines the ability to meet the needs of consumers with minimal production costs. In this regard, the paper considers one of the modern methods of quality management, based on the probabilistic assessment of the qualitative completion of the stages of the process under study. This approach takes into account the sequential dependence of the production stages, as well as the joint impact of measures aimed at effective process management. At the same time, the costs of the activities carried out must be minimized. The subject of this paper is a mathematical problem of operational management of multi-stage production processes with consistently interrelated stages based on a probabilistic approach to quality assessment. An algorithm for solving the problem and its software implementation in the form of an application is presented. The software product is acceptable for practical use and allows you to optimally choose the measures of influence on consistently interrelated stages of the process. [ABSTRACT FROM AUTHOR]

OCR post-processing is an important step to improve OCR text accuracy. It includes two main tasks, error detection and error correction. Hill climbing algorithm is a heuristic search method used for solving optimization problems. In this paper, we present a novel OCR error correction approach using an adapted version of the Hill climbing algorithm. Correction candidates of OCR errors are explored by random character edits and evolved with the Hill climbing. The character edit patterns are obtained from the training data. The proposed model is evaluated on the benchmark dataset in the OCR post-correction competition of the International Conference on Document Analysis and Recognition 2017. It is shown that our model outperforms various baseline approaches in the competition. In addition, the randomness of the proposed algorithm is analyzed to verify its stability under parameter configurations. [ABSTRACT FROM AUTHOR]

One of the central algorithmic graph theory problems is the minimum dominating set (MDS) problem. The MDS has various implementations particularly in graph mining. Various fields such as engineering, physics, social and biological sciences are widely used graph theory.Within the most powerful naturally algorithms are inspired for optimization. The firefly algorithm (FFA) FFA had been successfully used for solving many problems in several fields. This paper introduces FFA for solving the MDS problem. FFA is a general purpose algorithm; however, the problem has to be presented in an appropriate manner to convenient the presented algorithm steps. Hence, binary presentation in this study has been implemented for the MDS problem. Overall, the effectiveness of the presented algorithm experimental results demonstrated over the MDS problem. [ABSTRACT FROM AUTHOR]

By the author of the work, part of the requirements for the schedule of training sessions in higher educational institutions are formalized and written in the form of equations and inequalities. All these relations are linear or quadratic expressions with integer (more precisely, Boolean) arguments. In [1], a possible formalization of the problem of finding the optimal schedule is presented, which also turned out to be an integer quadratic Boolean programming problem without additional constraints. There are methods for solving this problem [2], but they have a relatively low rate of convergence, which, taking into account the dimension of the real problem of forming the SS, does not allow using these methods to solve the problem of finding the optimal and acceptable SS. Therefore, it is necessary to develop a method for solving the obtained formalized problem, which would make it possible to obtain a solution to the problem under consideration in an acceptable time, taking into account the capabilities of modern computer technology. In this paper, we propose an algorithm for solving the problem of composing SS based on the adaptation of the classical gradient search method. In relation to the task of compiling SS, there is a large list of works, including classical monographs [3], [4]. [ABSTRACT FROM AUTHOR]

In this paper, we propose an optimal diagonal algorithm under a generalized interval arithmetic for solving the problem of complete interval integer transport without converting the crisp equivalent problem. The proposed algorithm is simple to implement and it offers a numerical illustration to demonstrate the efficacy of the proposed process. [ABSTRACT FROM AUTHOR]

The paper aims to develop an algorithm to solve the inverse problem of identifying the volatility with given point measurements in a regime-switching economy. This value is of a particular importance since it is one of the main ingredients forming the option premia. After a review on the existing results for the classical Black–Scholes framework (equivalent to one regime), the direct and the inverse problems considering two regimes are defined. The forward problem is solved by applying the Simpson’s rule to an existing exact formula, while the inverse problem is tackled using a root-finding algorithm, namely Levenberg–Marquardt. What is more, a closed-form approximation formula for computing the implied volatility is proposed when at-the-money case is assumed. The numerical performance of the algorithms is studied and they are compared with a FDS approach. The paper is closed with a discussion and final remarks outlining the usefulness and applicability of the results. [ABSTRACT FROM AUTHOR]

Two problems of an object description based on certain incomplete information received by several agents are under consideration. An object in the problems is presented as a set of its elements and is characterized by properties of these elements and relations between them. The description of an object is an elementary conjunction of constant atomic predicate formulas, which are true for this object. It is supposed that all agents in the first problem have the same name for every object element. In the second problem, every agent does not know the true names of the object elements and arbitrarily gives them names. Algorithms solving the set problems are described and the upper bounds of these algorithms run steps are proved. Model examples of an algorithm implementation are given for the problems. [ABSTRACT FROM AUTHOR]

A technique has been developed for experimental verification of software that implements the algorithm for graphematic analysis and preprocessing of text resources. This software is used to solve the problem of automated mining analysis of poorly structured data. It is designed to extract semantically significant constructs from poorly structured resources, which is achieved by transforming text using a number of auxiliary algorithms and classifying the data obtained using machine learning algorithms in Python. At the first stage of the developed algorithm, abbreviations and acronyms are searched in a text file. After searching for abbreviations and acronyms using templates, a search is performed for graphematic descriptors, namely name, email and URL, phone number and date. At the final stage of the developed algorithm, the boundaries of sentences and direct speech are distinguished. [ABSTRACT FROM AUTHOR]

The paper presents a computational algorithm for solving the problem of pricing an American put-option described by the Black-Sholes equation. Firstly, two changes of variables are presented. First change is used to eliminate a discontinuity of solution derivatives. The second one transforms the equation on the domain with free boundary to the equation with an unknown coeffcient on a domain with fixed boundary. Secondly, the finite-difference approximation of the obtained equation is fulfilled including boundary condition at infinity. Finally, the computational algorithm is presented for solving discrete analogue of parabolic equation with iteration of the secant method at each time layer to found an unknown coeffcient with the help of the additional boundary condition. [ABSTRACT FROM AUTHOR]

In this paper, we discuss a fuzzy transportation problem(FTP) involving trapezoidal fuzzy numbers. We planned another algorithm to find an initial basic fuzzy feasible solution which will be very nearer to the optimal solution. The optimal solution of the given fuzzy transportation problems is verified by using the fuzzy version of Modified Distribution method without transforming into its correspondent classical form. A few numerical problems are solved to illustrate the proposed algorithm and the solution is compared with the solutions obtained by using an existing algorithms. [ABSTRACT FROM AUTHOR]

It is usually difficult for people to solve real-life problems, although nature has had Its own way of looking at and addressing these challenges for millions of years. This is why these days when people fail to strategize in these circumstances, they turn to nature to solve problems. Thus, it is often difficult for algorithms to arrive at the correct solution to a problem through heuristic processes. On the other hand, these strategies are good for getting close to the required time. One of these algorithms is called the Strawberry Algorithm. Here we propose to study the influence of Strawberry Algorithm roots and runners. In this research, we came up with the best solution to find the roots of a linear equation using one of nature's algorithms, and from these algorithms we find the strawberry algorithm through the Visual Studio 2019 platform. It has been observed that the strawberry algorithm succeeded in finding solutions to the linear equation and was very effective in finding any linear equation and he compared the results of the algorithm with the genetic algorithm in terms of measuring efficiency in time and iteration so that the strawberry algorithm was better than the genetic algorithm. [ABSTRACT FROM AUTHOR]

We have reviewed the simulation modeling problems concerning the technological process of transport support. We have proposed the solutions to optimization problems. An optimal path algorithm has been developed in the Anylogic software. Also, the optimization process of the number of vehicles in the specified conditions has been performed. The results of computer experiments are given. [ABSTRACT FROM AUTHOR]

Algorithms for the synthesis of gradient controllers in a closed nonlinear control system have been developed. When constructing a stabilizing controller for the problem of synthesizing such systems, the quadratic form of the Lyapunov function was used. The proposed algorithms for the synthesis of the gradient controller provide a stable equilibrium position of the system. The given computational schemes made it possible to effectively solve the problem of synthesizing nonlinear gradient controllers for a wide class of control objects with nonlinearities that satisfy general sector constraints. [ABSTRACT FROM AUTHOR]

The field of nature inspired computing and optimization techniques have evolved to solve difficult optimization problem in diverse fields of engineering, science and technology. The firefly attraction process is mimicked in the algorithm for solving optimizing proble3ms. In Firefly Algorithm (FA) sorting of fireflies is done by using sorting algorithm. The original FA is proposed with bubble sort for ranking the fireflies. In this paper, the quick sort replaces bubble sort to decrease the time complexity of FA. The dataset used is unconstrained benchmark functions from CEC 2005 [22]. The comparison of FA using bubble sort and FA using quick sort is performed with respect to best, worst, mean, standard deviation, number of comparisons and execution time. The experimental result shows that FA using quick sort requires less number of comparisons but requires more execution time. The increased number of fireflies helps to converge into optimal solution whereas by varying dimension for algorithm performed better at a lower dimension than higher dimension. [ABSTRACT FROM AUTHOR]

Assembly sequence planning (ASP) plays an important role in manufacturing because it determines the investment on the assembly facilities. Recently, various research works have been done to optimize the ASP problem using different optimization algorithms. Besides the algorithm efficiency, another factor that directly contributed to the quality of the solution is how the problem is modelled. According to the existing research, the most accurate approach to represent the ASP is to model base on the part of product. However, this approach resulting in the large number of assembly sequence possibility, and finally make the optimization process become harder. This paper proposed a simpler version of part-based representation using the base part concept. In the proposed model, the base part for the product is defined prior to the establishment of the precedence constraint. This approach has contributed to a smaller search space that resulting in better chance to obtain optimum solution during the optimization. The proposed ASP modelling approach however limited the number of possible solution, since the base part was fixed prior to the optimization. [ABSTRACT FROM AUTHOR]

The present paper is dedicated to numerical solving of three dimensional boundary-value problems in poroelastic formulation. Boundary element method and boundary integral equation method are applied to obtain Laplace domain solution of boundary-value problem. Modified Durbin’s algorithm of numerical inversion of Laplace transform is applied to perform solution in time domain. A problem of the three-dimensional poroelastic prismatic solid clamped at one end, and subjected to uniaxial and uniform impact loading at another is considered. [ABSTRACT FROM AUTHOR]

Filled function method is one of deterministic methods for solving global minimization problems. Filled function algorithm method generally contains of two main phases. First phase is to obtain local minimizer of objective function, second is to obtain minimizer or saddle point of filled function. In the second phase, vector direction plays an important role on finding stationary point of filled function, by assist in escaping from neighborhood of current minimizer of objective function of the first phase. In this paper, we introduce parameter free filled function and some typical vector direction to be applied in filled function algorithm. The algorithm method is implemented into some benchmark test functions. General computational and numerical results are presented to show the performance of each vector direction on filled function method for solving two dimensional unconstrained global optimization problems. [ABSTRACT FROM AUTHOR]

The paper deals with the description of GNSS-INS simulator used for testing algorithms of the airborne gravimetry. At first, some specifications of the simulator are presented. Then the basic principles, models, algorithms used in the GNSS-INS simulator are described. The next part of the article concerns the usage of the simulator for testing algorithms of the airborne vector gravimetry. Some simulation results conclude the paper. [ABSTRACT FROM AUTHOR]

Economic emission dispatch (EED) problems are one of the most crucial problems in power systems. Growing energy demand, limitation of natural resources and global warming make this topic into the center of discussion and research. This paper reviews the use of Quantum Computational Intelligence (QCI) in solving Economic Emission Dispatch problems. QCI techniques like Quantum Genetic Algorithm (QGA) and Quantum Particle Swarm Optimization (QPSO) algorithm are discussed here. This paper will encourage the researcher to use more QCI based algorithm to get better optimal result for solving EED problems. [ABSTRACT FROM AUTHOR]

The paper discusses the development of an algorithm for solving two-dimensional boundary value problems for a nonlinear partial parabolic differential equation with degeneration. The nonlinearity of the equation is dictated by a power law between the heat conductivity coefficient and temperature. The solution algorithm is based on the boundary element method with the application of the dual reciprocity method. The solution accuracy is analyzed for the discussed type of problems, with the application of different systems of radial basis functions. The most suitable system of functions and their parameters are determined. [ABSTRACT FROM AUTHOR]

In this paper we will revisit the modification of the piecewise constant policy timestepping (PCPT) method for solving Hamilton-Jacobi-Bellman (HJB) equations. This modification is called piecewise predicted policy timestepping (PPPT) method and if properly used, it may be significantly faster. We will quickly recapitulate the algorithms of PCPT, PPPT methods and of the classical implicit method and apply them on a passport option pricing problem with non-standard payoff. We will present modifications needed to solve this problem effectively with the PPPT method and compare the performance with the PCPT method and the classical implicit method. [ABSTRACT FROM AUTHOR]

In this paper a new procedure namely Modern Zero Suffix (MOZES) method is proposed to find the IFBS it converges to optimal solution for the fuzzy transportation problem. A new algorithm is generated to find the optimal solution for the fuzzy transportation problem with the aid of above said notion. The supply and demand are represented in terms of unique fuzzy number called Interval Valued Triangular Fuzzy Number (IVTFN). A relevant numerical illustrations are given to justify the above proposed notion. [ABSTRACT FROM AUTHOR]

In this paper we will investigate a family of five-stages high algebraic order embedded explicit six-step finite difference pairs, for the numerical solution of second order initial and/or boundary-value problems with periodical and/or oscillating solutions. Requiring the elimination of the phase-lag of the methods of the family of five-stages embedded explicit six-step algorithms and requiring also the elimination of the phase-lag's derivatives of the methods of the family of five-stages embedded explicit six-step schemes, we obtain a system of of equations. The solution of the previously obtained system of equations, leads to the determination of the free parameters of the new proposed finite difference pairs. Following the building of the new proposed finite difference pairs, we analyze theoretically the resulting schemes as follows: • We will study the local truncation error (LTE) of the resulting schems. • We will study the asymptotic form of the LTE which are produced using as model problem the radial Schrödinger equation. • We will study the comparison of the asymptotic forms of LTEs for several finite difference schemes of the same family. The comparison leads to the conclusions on the effectiveness of each member of the family of five-stages high algebraic order embedded explicit six-step finite difference pairs. • We will study the stability and the interval of periodicity of the resulting algorithms of the family of five-stages high algebraic order embedded explicit six-step finite difference pairs. • We will study the applications of the new builded family of five-stages high algebraic order embedded explicit six-step finite difference pairs to the numerical solution of several second order problems like the radial Schrödinger equation, astronomical problems etc. The previously described applications lead to the conclusions on the effectiveness of the members of the new proposed family of five-stages high algebraic order embedded explicit six-step finite difference pairs. [ABSTRACT FROM AUTHOR]

This paper deals with the preliminary results of a linear two-class Support Vector Machines (SVM) implemented in PERMON toolbox.We present the first insights into training PermonSVM classifier using quadratic programming (QP) algorithms from the PemonQP, i.e. Dostál's SMALBE, which is based on the augmented Lagrangian approach, and MPGP algorithms for box constrained QP. In presented benchmark on the URL dataset, we analyze the abilities of the QP solver with the respect to regularized parameter C and QP solver accuracy eps. In fact, we consider eps as the second parameter of the linear SVM and therefore we got better information about the tested algorithm behaviour. [ABSTRACT FROM AUTHOR]

In this paper, we derive the operator matrix of differentiation (OMD) for the Legender polynomials in the closed interval of the form [a, b], where a, b - R. Then, we use this OMD to solve a system of nonlinear initial value problem for long time span. For that, we propose algorithm that depends on dividing the interval of the solution into a finite subintervals and apply the OMD to each subinterval. This algorithm leads to accurate solution for long time span. Genesio system in chaotic case is considered to demonstrate the efficiency of this algorithm. [ABSTRACT FROM AUTHOR]

Many planning problems for robots are of considerable interest. In this paper, we consider the discrete minimum constraint removal motion planning problem that can be used for a motion planning formulation with explanations for failure. We consider an efficient approach to solve the problem. In particular, we consider an explicit reduction from the decision version of the problem to the satisfiability problem. We present the results of computational experiments for different satisfiability algorithms. [ABSTRACT FROM AUTHOR]

The problem of bringing control system in space Rn to a target set at a given time moment is considered in the paper. System's behavior is described by the system of di_erential equations on which standard conditions of existence, uniqueness and extendability of a solution of the system are implied for a given time period. The 2-stage method is proposed for finding approximate solution. Sets of solvability in a discrete model are constructed on the first stage. On the second stage procedure of constructing the solving control takes place. This procedure utilizes the points of constructed sets as guides. Program complex based on proposed method was implemented in order to provide numerical experiments using computers. The article contains the results of one of these experiments. [ABSTRACT FROM AUTHOR]

Research in mobile grid computing is inspired by the broad benefits of intensive scientific and enterprise applications by the scientific community in grid and mobile computing. The primary aim of grid computing is to combine the power of wide-ranging resources that are very diverse in nature and provide users with essential services to users in the future. Resources are complex and varying in a mobile grid system and the availability is limited for a period of time. With resource mobility in mind, classification is the most challenging and primitive challenge in the mobile grid environment. The previous paradigm outlined the Quantum based approach on Gravitational search Algorithm (QBGSA) for scheduling the task (job) on a mobile computing grid. In any case, it has problem with computational difficulty and execution time. To solve this problem, the proposed system designed an Intuitionistic Fuzzy C-Means (IFCM) clustering and Natural Exponential Inertia Weight with Artificial Bee Colony (NEIWABC) based scheduling approaches. In this proposed research work, grid server has all the information about mobile grid networks. Once it receiving the task request from user, it verifies the available resource providers. The nodes are clustered by using Intuitionistic Fuzzy C-Means (IFCM) clustering algorithm and Cluster Head (CH) selection is done using mobile capacity such as storage capacity, computational power. Then the task request is promoting to the selected CH in the cluster. Then the CH schedules the jobs to the appropriate mobiles to respond user. The task scheduling is performed by using Natural Exponential Inertia Weight with Artificial Bee Colony (NEIWABC) algorithm. The results of this experiment depicts that the proposed system attains good efficiency on comparison with previous system. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm that is able to solve optimal control problems in which the modelling of the system contains variable parameters, with the added complication that, in certain cases, these parameters can lead to control problems governed by quasi-linear equations. Combining the techniques of Pontryagin's Maximum Principle and the shooting method, an algorithm has been developed that is not affected by the values of the parameters, being able to solve conventional problems as well as cases in which the optimal solution is shown to be bang-bang with singular arcs. [ABSTRACT FROM AUTHOR]