Finding Optimal Solutions Research Articles

ПОРІВНЯЛЬНИЙ АНАЛІЗ ЕФЕКТИВНОСТІ МОНОЛІТНОГО ТА ЦИКЛІЧНОГО ЗАВАДОСТІЙКИХ КОДІВ

Comparative analysis of the effectiveness of monolithic and cyclic noise protective codes built on "Ideal Ring Bundles" (IRBs) as the common theoretical basis for synthesis, researches and application of the codes for improving technical indexes of coding systems with respect to performance, reliability, transformation speed, and security has been realized. IRBs are cyclic sequences of positive integers, which form perfect partitions of a finite interval of integers. Sums of connected IRB elements enumerate the natural integers set exactly R-times. The IRB-codes both monolithic and cyclic ones forming on the underlying combinatorial constructions can be used for finding optimal solutions for configure of an applicable coding systems based on the common mathematical platform. The mathematical model of noise-protective data coding systems presents remarkable properties of harmonious developing real space. These properties allow configure codes with useful possibilities. First of them belong to the self-correcting codes due to monolithic arranged both symbols "1" and of course "0" of each allowed codeword. This allows you to automatically detect and correct errors by the monolithic structure of the encoded words. IRB codes of the second type provide improving noise protection of the codes by choosing the optimal ratio of information parameters. As a result of comparative analysis of cyclic IRB-codes based with optimized parameters and monolithic IRB-codes, it was found that optimized cyclic IRB codes have an advantage over monolithic in relation to a clearly fixed number of detected and corrected codes, while monolithic codes favorably differ in the speed of message decoding due to their inherent properties of self-correction and encryption. Monolithic code characterized by packing of the same name characters in the form of solid blocks. The latter are capable of encoding data on several levels at the same time, which expands the ability to encrypt and protect encoded data from unauthorized access. Evaluation of the effectiveness of coding optimization methods by speed of formation of coding systems, method power, and error correcting has been made. The model based on the combinatorial configurations contemporary theory, which can find a wide scientific field for the development of fundamental and applied researches into information technolodies, including application multidimensional models, as well as algorithms for synthesis of the underlying models.

Management of COVID-19 Crisis: Bangladesh Perspectives

Bangladesh has one of the world's most extensive networks of community health workers and a robust tradition of public-private collaboration during crises, underpinned by resilient communities. These strengths highlight Bangladesh's capacity to navigate the current COVID-19 crisis effectively, finding optimal solutions to ensure survival amidst the catastrophe. Globally, the COVID-19 pandemic has ravaged lives and caused widespread societal disruption, prompting closures of schools and workplaces aimed at curbing transmission and delaying the epidemic's peak. Bangladesh's strategy to combat COVID-19 cannot rely solely on governmental efforts; it necessitates unprecedented collaboration across public and private sectors, both locally and internationally. Initiatives such as the COVID-19 Action Platform by the World Economic Forum, in conjunction with the World Health Organization, have been pivotal in responding to the pandemic. Bangladesh can leverage these platforms to bolster its private sector, share its experiences in managing COVID-19, and enhance economic resilience. Data from the Institute of Epidemiology, Disease Control, and Research highlight that the majority (62%) of COVID-19 cases in Bangladesh have occurred among individuals aged 21-50. Notably, those aged 31-40 account for the largest segment (23%) of infections, followed by those aged 21-30 (20%), and 41-50 (19%). Patients aged 51-60 constitute 15% of cases, while those over 60 represent 13%. Adolescents (ages 11-20) and children (under 10) comprise 7% and 3% of cases, respectively. This demographic profile aligns with trends observed in neighboring India but differs from harder-hit countries like the USA, China, and Spain.

Peculiarities of making decisions on the choice of the composition of the main hardware equipment of aviation multifunctional simulators

The current stage of training aircraft crews (AC) is characterized by the predominance of training tasks solved with the help of aviation multifunctional simulators (AMT). AMT covers a wide range of aviation simulators designed to solve problems of planning and managing the use of hardware and software resources in solving various training tasks in the interests of training aircraft crews. Based on this, AMT are characterized by a variety of hardware that ensure the implementation of technological operations and processes for analyzing and processing information when performing educational tasks, which determined the relevance of the task of choosing their optimal composition. Purpose of the work is to develop a model for choosing the composition of hardware equipment (HSS) in the AMT structure for solving training tasks in the interests of training aircraft crews. The solution to the optimization problem of substantiating the CAO is based on the analysis of the features of the application and development of AMT based on the modernization of obsolete and new hardware equipment. The complexity of the task is due to significant computational difficulties arising from the need to provide various technological operations and cycles of information flows in the form of a certain set of elementary operations and the presence of “individual” quality criteria for each educational task. To solve it, the technological process of solving educational AMT is represented by an oriented graph, the vertices of which are the tasks to be solved, and the arcs connecting them are elementary technological operations implemented by hardware. The fulfillment of each educational task is based on the fulfillment of a certain set of elementary streams based on the implementation of the compared CAO options in the AMT structure. The search for a solution is carried out on the graph using a three-level model based on finding optimal solutions to cover the educational tasks to be solved with the available means of obsolete hardware equipment, justifying the required number of new (developed) tools and choosing their optimal composition in the AMT structure as a whole, taking into account the preferences of the persons taking decision (decision maker). The proposed methodological approach and the model for choosing the CAO that implements it provide a sequential and level-bylevel solution of optimization problems with extreme variables for a reasonable choice of the decision maker of the optimal composition of hardware in the AMT structure in the interests of solving a variety of diverse training tasks for training aircraft crews.

A Comprehensive Review of Metaheuristic Methods for the Reconfiguration of Electric Power Distribution Systems and Comparison With a Novel Approach Based on Efficient Genetic Algorithm

The distribution system reconfiguration (DSR) is a complex large-scale optimization problem, which is usually formulated with one or more objective functions and should satisfy multiple sets of linear and non-linear constraints. As the exploration of feasible solutions in large and nonconvex search space of DSR is typically hard, it is important to develop efficient algorithms and methods for finding optimal solutions for DSR problem in reasonably short computational times. In traditional DSR, the configuration of distribution network can be changed by opening and closing sectional and tie switches, where active power losses are minimized, while radial network configuration and supply to all connected loads are both preserved. Accordingly, this paper provides a comprehensive review of a number of existing metaheuristic reconfiguration methods and introduces a novel efficient genetic algorithm (efficient GA) for DSR with loss minimization. In order to demonstrate benefits and effectiveness of the proposed efficient GA for DSR, the paper also provides a detailed comparison of results with an improved genetic algorithm (improved GA) for several test systems and real distribution networks. The obtained simulation results clearly show higher accuracy and improved convergence performance of the proposed efficient GA method, compared to the improved GA and other considered reconfiguration methods.

Exact Optimization and Decomposition Approaches for 2D Shelf Space Allocation

Shelf space is one of the scarcest resources, and its effective management to maximize profits has become essential to gain a competitive advantage for retailers. We consider the two-dimensional shelf space allocation problem (2DSSAP) with additional features motivated by literature and our interactions with a local bookstore. Two dimensions represent the width and height of rectangular arrangement space of a product. We determine optimal number of facings of all products in both dimensions and allocate them as contiguous rectangles to maximize profit. We first develop a mixed-integer linear mathematical programming model (MIP) for our problem and propose a solution method based on logic-based Benders decomposition (LBBD). Next, we construct an exact 2-stage algorithm (IP1/IP2), inspired by LBBD, which can handle larger and real-world size instances. To compare performances of our methods, we generate 100 test instances inspired by real-world applications and benchmarks from the literature. We observe that IP1/IP2 finds optimal solutions for real-world instances efficiently and can increase the local bookstore's profit up to 16.56\%. IP1/IP2 can provide optimal solutions for instances with 100 products in minutes and optimally solve up to 250 products (assigned to 8 rows x 160 columns) within a time limit of 1800 seconds. This exact 2-stage IP1/IP2 solution approach can be effective in solving similar problems such as display problem of webpage design, allocation of product families in grocery stores, and flyer advertising.

LEXICOGRAPHIC PROBLEMS OF CONVEX OPTIMIZATION: SOLVABILITY AND OPTIMALITY CONDITIONS, CUTTING PLANE METHOD

The lexicographic approach for solving multicriteria problems consists in the strict ordering of criteria concerning relative importance and allows to obtain optimization of more important criterion due to any losses of all another, to the criteria of less importance. Hence, a lot of problems including the ones of com­plex system optimization, of stochastic programming under risk, of dynamic character, etc. may be presented in the form of lexicographic problems of opti­mization. We have revealed conditions of existence and optimality of solutions of multicriteria problems of lexicographic optimization with an unbounded convex set of feasible solutions on the basis of applying properties of a recession cone of a convex feasible set, the cone which puts in order lexicographically a feasible set with respect to optimization criteria and local tent built at the boundary points of the feasible set. The properties of lexicographic optimal solutions are described. Received conditions and properties may be successfully used while developing algorithms for finding optimal solutions of mentioned problems of lexicographic optimization. A method of finding lexicographic of optimal solutions of convex lexicographic problems is built and grounded on the basis of ideas of method of linearization and Kelley cutting-plane method.

Information Technologies of Accounting and Analysis in Modern Companies

This article addresses the issue of finding optimal solutions using the information technologies of accounting and analysis in modern companies. The aim of the study is to reveal available information technologies for the needs of small, medium and large businesses operating in modern conditions. This goal is achieved by using systematization, comparison, and analysis of information, obtained under the survey and open management statistics. For the first time, the paper systematizes up-to-date information of 2021 about the most popular programs, online services, platforms and cloud services that are used to improve accounting and analytical processes in enterprises of various sizes. The main global trends in software development in terms of COVID-19 pandemic have been identified. In particular, the study defines the countries that occupy the leading positions in the informatization of business processes. An attempt was made to classify information technologies by their use by various volume of businesses. The analysis of research results of the Internet search query frequency regarding the use of information technologies enabled to determine the most popular software products worldwide. The peculiarities of information technologies, their advantages and disadvantages were examined and the common and distinctive features were compared. It was determined that for the new enterprises to implement information technologies, it is necessary to conduct a step-by-step study of all available software products. The software evaluation algorithm was described to help select the optimal software for the specific business processes. The paper also describes the way to solve the problem of using accounting and analysis software for the businesses of a specific kind of activity.

A Guided Evolutionary Search Approach for Real-Time Stencil Printing Optimization

This study aims to identify and maintain optimal stencil printer settings in dynamic surface mount technology (SMT) assembly lines to control the solder paste volume transfer efficiency (TE) and increase the yield rates of surface mount assembly (SMA) lines. Stencil printing process (SPP) is a crucial procedure that determines the first pass yields in SMA lines. Stencil printing speed (PS) and printing pressure (PP) are two critical SPP parameters in the manufacturing process; identifying the optimal printer settings in complex fabrication environment is cost efficient but challenging. To search for the global optimal PS and PP values in real time with minimal computational efforts, a dynamic optimization SPP optimization system is proposed based on a novel guided evolutionary search (GES) strategy. The GES algorithm is flexible to adjust the feasible solution region based on real-time optimization results to maximize the probability of finding optimal solutions and minimize the computational burden. As a major extension on the classical evolutionary search, the GES minimizes the possibility to fall in the local optimal due to insufficiencies in the prediction accuracy and guarantees global optimality within the dynamically varying printing environment.

Implementation of discrete particle swarm optimization algorithm in the capacitated vehicle routing problem

Capacitated Vehicle Routing Problem (CVRP) is known as an NP-hard problem. It is because CVRP problems are very hard for finding optimal solutions, especially in large instances. In general, the NP-hard problem is difficult to solve in the exact method, so the metaheuristic approach is implemented in the CVRP problem to find a near-optimal solution in reasonable computational time. This research uses the DPSO algorithm for solving CVRP with ten instances of benchmark datasets. DPSO implementation uses tuning parameters with the One Factor at Time (OFAT) method to select the best DPSO parameters. The outcome objective function will be compared with several PSO models proposed in previous studies. Statistical test using One Way Reputed Measure ANOVA is needed to compare algorithm performance. First, ANOVA uses for comparing’s results. Then, ANOVA is also used to test DPSO’s performance compared with DPSO-SA, SR-1, and SR-2 algorithm. The computational result shows that the basic DPSO algorithm not competitive enough with other methods for solving CVRP.

Optimality conditions for set-valued optimization problems via scalarization function

One of the most important and popular topics in optimization problems is to find its optimal solutions, especially Pareto optimal points, a well-known solution introduced in multi-objective optimization. This topic is one of the oldest challenges in many issues related to science, engineering and other fields. Many important practical-problems in science and engineering can be expressed in terms of multi-objective/ set-valued optimization problems in order to achieve the proper results/ properties. To find the Pareto solutions, a corresponding scalarization problem has been established and studied. The relationships between the primal problem and its scalarization one should be investigated for finding optimal solutions. It can be shown that, under some suitable conditions, the solutions of the corresponding scalarization problem have uniform spread and have a close relationship to Pareto optimal solutions for the primal one. Scalarization has played an essential role in studying not only numerical methods but also duality theory. It can be usefully applied to get relationships/ important results between other fields, for example optimization, convex analysis and functional analysis. In scalarization, we ussually use a kind of scalarized-functions. One of the first and the most popular scalarized-functions used in scalarization method is the Gerstewitz function. In the paper, we mention some problems in set-valued optimization. Then, we propose an application of the Gerstewitz function to these problems. In detail, we establish some optimality conditions for Pareto/ weak solutions of unconstrained/ constrained set-valued optimization problems by using the Gerstewitz function. The study includes the consideration of problems in theoretical approach. Some examples are given to illustrate the obtained results.

Optimal sharing energy of a complex of houses through energy trading in the Internet of energy

To increase utilization of distributed renewable energy and decrease dependence on conventional electrical grids, the Internet of energy integrates smart grids with battery energy storage systems and the Internet of things to utilize redundant energy, so that energy can be shared among users. However, few works considered sharing energy in the Internet of energy from a residential community scale. Therefore, this paper constructs a mixed-integer programming model for the optimal sharing energy of a complex of houses through the Internet of energy with an energy trading platform, by which houses with renewable energy facilities and battery energy storage systems can trade and share energy so that the total profit of the whole complex of houses is maximized. This paper further solves this problem by a hybrid algorithm of harmony search and variable neighborhood search, which are good at finding optimal solutions through population search and individual search, respectively. Additionally, a solution repairing scheme is proposed to guarantee solution feasibility during the algorithm. Through simulation, this algorithm can save energy consumption of 748.17 kW for one day, and shift the peak load. On the average, each house can make a profit above 70¢ for one day.

ABOUT THE ALIGNMENT DESIGN OF HEAT SUPPLY SYSTEMS ON THE BASIS OF SYSTEM ANALYSIS

Statement of the problem. The aim of the article is to develop procedures for translating practical questions about the choice of optimal routes of the heat supply system into mathematical problems of multi-criteria optimization and the study of these problems taking into account a large number of different parameters of the system and their connections.Results and conclusions. A mathematical description is compiled of the route choosing problem for the heat supply system based on the methods of system analysis. As optimized criteria, the integrated parameters are discussed describing the main characteristics of the systems under consideration. Unification of the problem where some of the criteria must be minimized and the other one maximized is carried out by replacing the minimized criteria with inverse values. Using the example of the modeling problems the procedures are shown for finding optimal solutions. The visibility of such procedures is provided by the use of a quadratic (Euclidean) norm instead of the traditional summation of weight coefficients. The importance of correct scaling and choice of weight factors is shown in the study of the discussed multi-criteria optimization problem.

Selection-driven tumor evolution with public goods leads to patterns of clonal expansion consistent with neutral growth.

SummaryCancers are the result of eco-evolutionary processes fueled by heritable phenotypic diversification and driven by environmentally dependent selection. Space represents a key growth-limiting ecological resource, the ability to explore this resource is likely under strong selection. Using agent-based modeling, we explored the interplay between phenotypic strategies centered on gaining access to new space through cell-extrinsic degradation of extracellular matrix barriers and the exploitation of this resource through maximizing cell proliferation. While cell proliferation is a cell-intrinsic property, newly accessed space represents a public good, which can benefit both producers and non-producers. We found that this interplay results in ecological succession, enabling emergence of large, heterogeneous, and highly proliferative populations. Even though in our simulations both remodeling and proliferation strategies were under strong positive selection, their interplay led to sub-clonal architecture that could be interpreted as evidence for neutral evolution, warranting cautious interpretation of inferences from sequencing of cancer genomes.

A conceptual and practical comparison of PSO-style optimization algorithms

Optimization algorithms are widely employed for finding optimal solutions in many applications. Stochastic optimization algorithms including nature-inspired optimization algorithms are simple and easy to implement, and this is the reason why there is a growing interest in this research area. Recently, many nature-inspired optimization algorithms have been proposed for solving many optimization problems. Moreover, with the aim of improving the performance of optimization algorithms, some modifications were applied such as combining different algorithms and employing some sampling techniques for replacing critical parameters in the optimization algorithms. This paper compares five different widely used PSO-style optimization algorithms to investigate if there is a significant difference between them or not. Theoretically, we explain different PSO-style algorithms and discuss the similarities and differences between them. Practically, a number of experiments were conducted to compare these algorithms. Theoretical analysis and practical results prove that there is not any significant difference between the PSO-style algorithms regarding their performance.

Mixed Integer Programming Formulations for Steiner Tree and Quality of Service Multicast Tree Problems

This article presents flow-based mixed integer programming formulations for the Steiner Tree Problem and its variant applied to model and solve the Quality of Service Multicast Tree problem. This is a relevant problem related to nowadays telecommunication networks, particularly Content Delivery Networks, to distribute multimedia over cloud-based Internet systems. To the best of our knowledge, no previous mixed integer programming formulation was proposed for the Quality of Service Multicast Tree problem variant. Experimental evaluation is performed over a set of realistic problem instances from SteinLib, a reference test-set repository, modified accordingly to prove that standard exact solvers are capable of finding solutions to real-world size instances. Exact methods are applied for benchmarking the proposed formulations, finding optimal solutions and low feasible-to-optimal gaps in reasonable execution times.

Multidisciplinary design optimization of distributed energy generation systems: The trade-offs between life cycle environmental and economic impacts

Distributed energy systems (DES) are the focus of increasing attention because they have the potential to enhance the sustainability performance of energy generation. Previous DES researches evaluated various distributed energy technologies and systems from different aspects. However, there is still a research gap to evaluate and compare the multiple technology combinations and sizes for finding optimal energy solutions under various scenarios. This study aims to determine the best combination of technologies and their corresponding sizes for DES for various building types and climate zones in terms of life cycle environmental and economic impact. We developed parametric models (which considers dynamic hour by hour energy demand) for six commercially available distributed energy technologies and simulated the performance of them under various conditions. Then, we used a novel approach – multidisciplinary design optimization (MDO) to examine the billions of options (e.g., technologies, sizes, climate zone, Etc.) and identified the Pareto front with the optimal environmental and economic impact. According to MDO simulations, the microturbine-solar PVs-lithium ion battery and solid oxide fuel cells-solar PVs-lithium ion battery are two optimal combinations of technologies for three commercial building types for five climate zones. The DES can primarily reduce the environmental impact compared to conventional centralized energy production (CCEP) by 16–61% in all scenarios. However, the life cycle cost of DES is higher than CCEP, especially for SOFC-based DES. The microturbine-based DES is more cost-competitive and economical (about 65%, 32%, and 64% lower than SOFC-based DES for the small, medium, and large office, respectively).

Towards decomposition based multi-objective workflow scheduling for big data processing in clouds

A workflow is a group of tasks that are processed in a particular order to complete an application. Also, it is a popular paradigm used to model complex big-data applications. Executing complex applications in a distributed system such as cloud or cluster implicates optimization of several conflicting objectives such as monetary cost, energy consumption, total execution time of the application (makespan). Regardless of this trend, most of the workflow scheduling approaches focused on single or bi-objective optimization problem. In this paper, we considered the problem of scheduling workflows in a cloud environment as a multi-objective optimization problem, and hence proposed a multi-objective workflow-scheduling algorithm based on decomposition. The proposed algorithm is capable of finding optimal solutions with a single run. Our evaluation results show that, by a single run, the proposed approach manages to obtain the Pareto Front solutions which are at least as good as schedules produced by running a single-objective scheduling algorithm with constraints for multiple times.

Multicriteria decision making for evergreen problems in food science by sum of ranking differences

Finding optimal solutions usually requires multicriteria optimization. The sum of ranking differences (SRD) algorithm can efficiently solve such problems. Its principles and earlier applications will be discussed here, along with meta-analyses of papers published in various subfields of food science, such as analytics in food chemistry, food engineering, food technology, food microbiology, quality control, and sensory analysis. Carefully selected real case studies give an overview of the wide range of applications for multicriteria optimizations, using a free, easy-to-use and validated method. Results are presented and discussed in a way that helps scientists and practitioners, who are less familiar with multicriteria optimization, to integrate the method into their research projects. The utility of SRD, optionally coupled with other statistical methods such as ANOVA, is demonstrated on altogether twelve case studies, covering diverse method comparison and data evaluation scenarios from various subfields of food science.

Customer order scheduling problem to minimize makespan with sequence-dependent setup times

In this paper, we study a variant of the customer order scheduling problem when sequence-dependent setup times cannot be ignored. The performance measure adopted is the makespan minimization. The existence of sequence-dependent setup times makes this problem to be NP-hard. Furthermore, the solution encoding usually employed for other variants of the customer order scheduling problem does not guarantee finding optimal solutions. For this problem, we present some properties and develop two Mixed Integer Linear Programming (MILP) formulations to analyze the structure of the solutions. Using these properties and models, we propose two matheuristics based on fixing some integer decision variables in the MILP models, denoted as Fixed Variable List Algorithm (FVLA) and Clustering Sequence Algorithm (CSA), respectively. The computational experiments carried out prove the ability of these matheuristics to find high-quality solutions in reasonable CPU time. More specifically, the FVLA matheuristic stands out as the most efficient for the problem.

Machine Learning for Advanced Wireless Sensor Networks: A Review

Wireless sensor networks (WSNs) are typically used with dynamic conditions of task-related environments for sensing(monitoring) and gathering of raw sensor data for subsequent forwarding to a base station. In order to deploy WSNs in real environments, a variety of technical challenges must be addressed. With traditional techniques developed for a specific task, it is hard to react in dynamic situations beyond the scope of the intended task. As a solution to this problem, machine learning (ML) techniques that are able to handle dynamic situations with successful learning process have been applied lately in WSNs. Particularly, deep learning (DL) techniques, a class of ML techniques characterized by the use of deep neural network, are used for WSNs to extract higher level features from raw sensor data. A range of benefits obtained from ML techniques applied to WSNs can be described as reduced computational complexity, increased feasibility in finding optimal solutions, increased energy efficiency, etc. On the other hand, it is found from our survey that large training time and large dataset to get acceptable performance are accompanied with large energy consumption which is not favorable for resource-restrained WSNs. Reviews on the applications of ML techniques in WSNs appeared in the literature. However, few reviews have dealt with the applications of DL techniques in WSNs. In this review, recent developments of ML techniques for WSNs are presented with much emphasis on DL techniques. The DL techniques developed for various applications in WSNs are addressed together with their respective deep neural network architectures.