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In our research, we are focusing on the development of iterative algorithms for solving contact problems emerging in the analysis of steel structures bolt connections. Despite the project is focusing mainly on the development of efficient numerical algorithms, several supplementary problems have to be solved. In our research, we decided to utilize widely-used popular commercial software Ansys to provide the data for our algorithms implemented in Matlab as well as for the comparison of results. This paper presents experiences with our newly developed data interface. [ABSTRACT FROM AUTHOR]

In Handwritten Character Recognition (HCR), interest in feature extraction has been on the increase with the abundance of algorithms derived to increase the accuracy of classification. In this paper, a metaheuristic approach for feature extraction technique in HCR based on Crow Search Algorithm (CSA) was proposed. Freeman Chain Code (FCC) was used as data representation. The main problem in representing a character using FCC is that the results of the extractions depend on the starting points that affected the route length of chain code. To solve this problem, the metaheuristic approach via CSA was proposed to find the shortest route length and minimum computational time for HCR. The performance measurements of the proposed CS-FCC extraction algorithm are the route lengths and computation times. The experiments on the algorithms are performed based on the chain code representation derived from established previous works of Center of Excellence for Document Analysis and Recognition (CEDAR) dataset which consists of 126 upper-case letter characters. Based on the result, the proposed CS-FCC obtained 1880.28 in term of route length and only needs 1.10 second to solve the whole character images. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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 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]

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]

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]

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]

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]

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]