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Embedded real-time multi-task systems must often not only comply with timing constraints but also need to meet energy requirements. However, optimizing energy consumption might lead to higher Worst-Case Execution Time (WCET), leading to an un-schedulable system, as frequently executed code can easily differ from timing-critical code. To handle such an impasse in this paper, we formulate a Metaheuristic Algorithm-based Multi-objective Optimization (MAMO) for multi-task real-time systems. But, performing multiple WCET, energy, and schedulability analyses to solve a MAMO poses a bottleneck concerning compilation times. Therefore, we propose two novel approaches - Path-based Constraint Approach (PCA) and Impact-based Constraint Approach (ICA) - to reduce the solution search space size and to cope with this problem. Evaluations showed that PCA and ICA reduced compilation times by 85.31% and 77.31%, on average, over MAMO. For all the task sets, out of all solutions found by ICA-FPA, on average, 88.89% were on the final Pareto front.

Most real-time embedded systems are required to fulfill timing constraints while adhering to a limited energy budget. Small ScratchPad Memory (SPM) poses a common hardware constraint on embedded systems. Static SPM allocation techniques are limited by the SPM's stringent size constraint, which is why this paper proposes a Dynamic SPM Allocation (DSA) model at the compiler level for the dynamic allocation of a program to SPM during runtime. To minimize Worst-Case Execution Time (WCET) and energy objectives, we propose a multi-objective DSA-based optimization. Static SPM allocations might inherently use SPM sub-optimally, while all proposed DSA optimizations are only single-objective. Therefore, this paper is the first step towards a DSA that trades WCET and energy objectives simultaneously. Even with extra DSA overheads, our approach provides better quality solutions than the state-of-the-art multi-objective static SPM allocation and ILP-based single-objective DSA approach.

Electromagnetic compatibility has become an integral part of the development process of any electronic or electrical product; however, maintaining a balance between radiation and susceptibility is not a simple task. When solving problems with EMC, financial and time costs arise, i.e., undesirable phenomena that EMC Risk-based management tries to prevent. This article focuses on a specific type of 3D shielding that can protect and envelop a product. Creating a shielding enclosure can be complex, especially considering atypical user requirements, such as material transparency and roundness. These requirements lead to unique shapes of electromagnetic enclosures, for which no specific procedures exist. Therefore, an idea was born based on simple cubes that could be assembled into arbitrary shapes with the help of metaheuristic algorithms and thereby help developers in their efforts. The principle is based on simply joining cubes of predefined form into the desired structure. To make the process more efficient, the application of artificial intelligence is expected, which will be able to propose a solution for a user-defined design based on experience and habits. © 2023 IEEE.

Multi-level image thresholding is a common approach to image segmentation where an image is divided into several regions based on its histogram. Otsu's method is the most popular method for this purpose, and is based on seeking for threshold values that maximise the between-class variance. This requires an exhaustive search to find the optimal set of threshold values, making image thresholding a time-consuming process. This is especially the case with increasing numbers of thresholds since, due to the curse of dimensionality, the search space enlarges exponentially with the number of thresholds. Population-based metaheuristic algorithms are efficient and effective problem-independent methods to tackle hard optimisation problems. Over the years, a variety of such algorithms, often based on bio-inspired paradigms, have been proposed. In this paper, we formulate multi-level image thresholding as an optimisation problem and perform an extensive evaluation of 23 population-based metaheuristics, including both state-of-the-art and recently introduced algorithms, for this purpose. We benchmark the algorithms on a set of commonly used images and based on various measures, including objective function value, peak signal-to-noise ratio, feature similarity index, and structural similarity index. In addition, we carry out a stability analysis as well as a statistical analysis to judge if there are significant differences between algorithms. Our experimental results indicate that recently introduced algorithms do not necessarily achieve acceptable performance in multi-level image thresholding, while some established algorithms are demonstrated to work better.

Introduction: Currently, software quality is a fundamental feature to ensure a space in the global market that day by day is more competitive and demanding. Software testing allows software developers to find and fix bugs and, thereby, raise the quality of their products. The cost of testing is now estimated at 50% of the total cost of software development, so it is necessary to find less expensive ways to ensure elevated levels of fault detection. In this scenario, black box tests play a key role; within these tests, combinatorial approaches are one of the best options. In combinatorial tests, test users must have a tool that provides them with the least number of test cases with the greatest coverage (failure detection) according to the parameters of the method (procedure, function, or other) that they want to test, and this is the main objective of this work. Objective: This paper presents an algorithm that supports the generation of test cases in black box tests based on creating Mixed Covering Arrays (MCA). These fixes allow you to generate the smallest number of test cases required to test a unit of code with the highest required coverage. The proposed algorithm builds an initial solution based on a greedy algorithm and then improves this solution through an iterative process oriented by simulated annealing (metaheuristic algorithm). Methodology: The research was conducted following the Iterative Research Pattern proposed by Pratt. First, the main problems reported in the state of the art for the construction of CSF were identified, then a review of the proposed solutions to these problems was performed. Then a first algorithm was created and then iteratively modified this algorithm, including, and removing components according to the experimental results of its operation. When the desired version was obtained, a process of refinement of parameters was performed and compared with the best results presented in the literature, results obtained by different algor, Introducción: Actualmente, la calidad del software en una característica fundamental para asegurarse un espacio en el mercado global que día a día es más competitivo y exigente. Las pruebas de software permiten a las empresas desarrolladoras de software encontrar y corregir fallos y con ello elevar la calidad de sus productos. El costo de las pruebas hoy se estima en un 50% del costo total del desarrollo de software, por esto se hace necesario encontrar formas menos costosas que garanticen altos niveles de detección de fallos. En este escenario, las pruebas de caja negra tienen un papel fundamental y dentro de estas pruebas los enfoques combinatoriales son una de las mejores opciones. En las pruebas combinatorias se hace necesario que los usuarios probadores (testers) cuenten con una herramienta que les proporcione el menor número de casos de prueba con la mayor cobertura (detección de fallos) de acuerdo con los parámetros del método (procedimiento, función u otro) que desea probar, y este es el objetivo principal de este trabajo. Objetivo: En este artículo se presenta un algoritmo que soporta la generación de casos de prueba en pruebas de caja negra basado en la creación de Arreglos de Cobertura Mixta (MCA). Estos arreglos permiten generar el menor número de casos de prueba requeridos para probar una unidad de código con la mayor cobertura requerida (mayor posibilidad de detectar fallos). El algoritmo propuesto construye una solución inicial basado en un algoritmo codicioso (greedy) y luego mejora esta solución a través de un proceso iterativo orientado por recocido simulado (algoritmo metaheurístico) y tres formas de definir soluciones vecinas. Metodología: La investigación se realizó siguiendo el Patrón de Investigación Iterativa propuesto por Pratt. Primero se identificaron los principales problemas reportados en el estado del arte para la construcción de MCA, luego se realizó una revisión de las propuestas de solución a estos problemas. Después se creó un prime

Accurate streamflow prediction is significant when developing water resource management and planning, forecasting floods, and mitigating flood damage. This research developed a novel methodology that involves data pre-processing and an artificial neural network (ANN) optimised with the coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA-ANN) to forecast the monthly water streamflow. The monthly streamflow data of the Tigris River at Amarah City, Iraq, from 2010 to 2020, were used to build and evaluate the suggested methodology. The performance of CPSOCGSA was compared with the slim mold algorithm (SMA) and marine predator algorithm (MPA). The principal findings of this research are that data pre-processing effectively improves the data quality and determines the optimum predictor scenario. The hybrid CPSOCGSA-ANN outperformed both the SMA-ANN and MPA-ANN algorithms. The suggested methodology offered accurate results with a coefficient of determination of 0.91, and 100% of the data were scattered between the agreement limits of the Bland–Altman diagram. The research results represent a further step toward developing hybrid models in hydrology applications., Validerad;2022;Nivå 2;2022-10-04 (hanlid)

Modelling drought is vital to water resources management, particularly in arid areas, to reduce its effects. Drought severity and frequency are significantly influenced by climate change. In this study, a novel hybrid methodology was built, data preprocessing and artificial neural network (ANN) combined with the constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA), to forecast standard precipitation index (SPI) based on climatic factors. Additionally, the marine predators algorithm (MPA) and the slime mould algorithm (SMA) were used to validate the performance of the CPSOCGSA algorithm. Climatic factors data from 1990 to 2020 were employed to create and evaluate the SPI 1, SPI 3, and SPI 6 models for Al-Kut City, Iraq. The results indicated that data preprocessing methods improve data quality and find the best predictors scenario. The performance of CPSOCGSA-ANN is better than MPA-ANN and SMA-ANN algorithms based on various statistical criteria (i.e., R2, MAE, and RMSE). The proposed methodology yield R2 = 0.93, 0.93, and 0.88 for SPI 1, SPI 3, and SPI 6, respectively., Validerad;2022;Nivå 2;2022-09-06 (johcin)

Water quality has a significant influence on human health. As a result, water quality parameter modelling is one of the most challenging problems in the water sector. Therefore, the major factor in choosing an appropriate prediction model is accuracy. This research aims to analyse hybrid techniques and pre-processing data methods in freshwater quality modelling and forecasting. Hybrid approaches have generally been seen as a potential way of improving the accuracy of water quality modelling and forecasting compared with individual models. Consequently, recent studies have focused on using hybrid models to enhance forecasting accuracy. The modelling of dissolved oxygen is receiving more attention. From a review of relevant articles, it is clear that hybrid techniques are viable and precise methods for water quality prediction. Additionally, this paper presents future research directions to help researchers predict freshwater quality variables., Validerad;2022;Nivå 2;2022-07-04 (sofila)

Modelling drought is vital to water resources management, particularly in arid areas, to reduce its effects. Drought severity and frequency are significantly influenced by climate change. In this study, a novel hybrid methodology was built, data preprocessing and artificial neural network (ANN) combined with the constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA), to forecast standard precipitation index (SPI) based on climatic factors. Additionally, the marine predators algorithm (MPA) and the slime mould algorithm (SMA) were used to validate the performance of the CPSOCGSA algorithm. Climatic factors data from 1990 to 2020 were employed to create and evaluate the SPI 1, SPI 3, and SPI 6 models for Al-Kut City, Iraq. The results indicated that data preprocessing methods improve data quality and find the best predictors scenario. The performance of CPSOCGSA-ANN is better than MPA-ANN and SMA-ANN algorithms based on various statistical criteria (i.e., R2, MAE, and RMSE). The proposed methodology yield R2 = 0.93, 0.93, and 0.88 for SPI 1, SPI 3, and SPI 6, respectively., Validerad;2022;Nivå 2;2022-09-06 (johcin)

Accurate streamflow prediction is significant when developing water resource management and planning, forecasting floods, and mitigating flood damage. This research developed a novel methodology that involves data pre-processing and an artificial neural network (ANN) optimised with the coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA-ANN) to forecast the monthly water streamflow. The monthly streamflow data of the Tigris River at Amarah City, Iraq, from 2010 to 2020, were used to build and evaluate the suggested methodology. The performance of CPSOCGSA was compared with the slim mold algorithm (SMA) and marine predator algorithm (MPA). The principal findings of this research are that data pre-processing effectively improves the data quality and determines the optimum predictor scenario. The hybrid CPSOCGSA-ANN outperformed both the SMA-ANN and MPA-ANN algorithms. The suggested methodology offered accurate results with a coefficient of determination of 0.91, and 100% of the data were scattered between the agreement limits of the Bland–Altman diagram. The research results represent a further step toward developing hybrid models in hydrology applications., Validerad;2022;Nivå 2;2022-10-04 (hanlid)

Water quality has a significant influence on human health. As a result, water quality parameter modelling is one of the most challenging problems in the water sector. Therefore, the major factor in choosing an appropriate prediction model is accuracy. This research aims to analyse hybrid techniques and pre-processing data methods in freshwater quality modelling and forecasting. Hybrid approaches have generally been seen as a potential way of improving the accuracy of water quality modelling and forecasting compared with individual models. Consequently, recent studies have focused on using hybrid models to enhance forecasting accuracy. The modelling of dissolved oxygen is receiving more attention. From a review of relevant articles, it is clear that hybrid techniques are viable and precise methods for water quality prediction. Additionally, this paper presents future research directions to help researchers predict freshwater quality variables., Validerad;2022;Nivå 2;2022-07-04 (sofila)

This work is dedicated to parameter optimization for a self-biased amplifier to be used in preamplifiers for the diagnosis of seizures in neuro-diseases such as epilepsy. For the sake of maximum compactness, which is obligatory for all implantable devices, power is to be supplied by a piezoelectric nanogenerator (PENG). Several meta-heuristic optimization algorithms and an ANN (artificial neural network)-assisted goal attainment method were applied to the circuit, aiming to provide us with the set of optimal design parameters which ensure the minimal overall area of the preamplifier. These parameters are the slew rate, load capacitor, gain–bandwidth product, maximal input voltage, minimal input voltage, input voltage, reference voltage, and dissipation power. The results are re-evaluated and compared in the Cadence 180 nm SCL environment. It has been observed that, among the metaheuristic algorithms, the whale optimization technique reached the best values at low computational cost, decreased complexity, and the highest convergence speed. However, all metaheuristic algorithms were outperformed by the ANN-assisted goal attainment method, which produced a roughly 50% smaller overall area of the preamplifier. All the techniques described here are applicable to the design and optimization of wearable or implantable circuits.

Worst-Case Execution Time (WCET) is the most important design criterion in the domain of hard real-time systems. Most embedded systems also need to satisfy additional design criteria like, e.g., energy consumption. Performing WCET and energy analyses statically at compile-time can be time-consuming. Consequently, minimizing WCET and energy consumption of the code at the compiler level using multi-objective optimization can be a time-consuming process. In this paper, we propose an approximation model to quickly approximate the WCET and energy consumption of the code at compile-time. Instead of using traditional WCET and energy analyses, we exploit this approximation model to perform ScratchPad Memory (SPM) allocation-based multi-objective optimization. Furthermore, we solve the multi-objective optimization problem using metaheuristic algorithms and explore the trade-offs between WCET and energy consumption. Using the proposed approximation model, we achieved, on average, a 94.12% reduction in compilation time and maintained the quality of the Pareto optimal solutions while performing the multi-objective optimization. Furthermore, the approximation error while using the proposed approximation model was in an acceptable range of 2% - 4% on average., European Union

Spatial decision making is an everyday activity, common to individuals and organizations. However, recently there is an increasing interest in the importance of spatial decision-making systems, as more decision-makers with concerns about sustainability, social, economic, environmental, land use planning, and transportation issues discover the benefits of geographical information. Many spatial decision problems are regarded as optimization problems, which involve a large set of feasible alternatives, multiple conflicting objectives that are difficult and complex to solve. Hence, Multi-Objective Optimization methods (MOO)—metaheuristic algorithms integrated with Geographical Information Systems (GIS) are appealing to be powerful tools in these regards, yet their implementation in spatial context is still challenging. In this thesis, various metaheuristic algorithms are adopted and improved to solve complex spatial problems. Disaster management and urban planning are used as case studies of this thesis.These case studies are explored in the four papers that are part of this thesis. In paper I, four metaheuristic algorithms have been implemented on the same spatial multi-objective problem—evacuation planning, to investigate their performance and potential. The findings show that all tested algorithms were effective in solving the problem, although in general, some had higher performance, while others showed the potential of being flexible to be modified to fit better to the problem. In the same context, paper II identified the effectiveness of the Multi-objective Artificial Bee Colony (MOABC) algorithm when improved to solve the evacuation problem. In paper III, we proposed a multi-objective optimization approach for urban evacuation planning that considered three spatial objectives which were optimized using an improved Multi-Objective Cuckoo Search algorithm (MOCS). Both improved algorithms (MOABC and MOCS) proved to be efficient in solving evacuation planning when co

The ordering effect of sequential treatment addition is present in many problem areas ranging from online search ranking and job scheduling to election standardization and the design of clinical trials. The straightforward approach to studying this ordering effect on a response is to test each permutation of the treatment components. When the run size becomes too costly, a random sample of permutations can instead be used for estimation and inference. Noting the inefficiencies present in both of these approaches, well-designed order-of-addition experiments have been proposed to reduce the computational complexity of studying the ordering effect without sacrificing statistical power; however, this problem is under-studied in the statistical literature.In this work we make several state-of-the-art advancements to the order-of-addition literature. By leveraging the structure of order-of-addition data, we pose flexible, position-based models that work well in practice. These new models inspire and necessitate the creation of parsimonious designs that guarantee stable parameter estimates. We further adapt these designs for a component screening problem in which the pool of treatment components is larger than the number of available positions in the sequence.We test our methods against several prominent order-of-addition problems, including drug chemotherapy and job scheduling. Our models and model-based optimal designs outperform the accepted alternatives in most cases, and in other situations our methods act as valuable tools for encouraging robust experimentation. To encourage the use of quality order-of-addition designs in wider practice, we implement and compare popular, nature-inspired metaheuristic algorithms for solving difficult design problems without closed-form solutions. We find that certain variants and hybridized algorithms can produce quality designs for obtaining stable parameter estimates in order-of-addition models or for studying black-box deterministi

[ES] Una entidad financiera es cualquier entidad que tiene como objetivo ofrecer servicios de carácter financiero a sus clientes. Esos servicios van desde ejemplos de la vida cotidiana como: captación y remuneración de ahorros, concesión de préstamos y créditos; hasta el asesoramiento en inversión en los diferentes mercados. En este sentido, los asesores financieros elaboran propuestas de inversión a sus clientes con el objetivo de maximizar sus beneficios. Para ejecutar una propuesta de inversión un asesor necesita de una herramienta capaz de calcular el conjunto de operaciones que deben realizarse en el mercado. En el presente trabajo se proponen algoritmos que resuelven el problema anteriormente expuesto. Para ello, en primer lugar, se define formalmente el problema del rebalanceo automático de carteras financieras, teniendo en cuenta los requisitos de las entidades. Debido a la alta explosión combinatoria del problema se decide abordar su solución mediante algoritmos metaheurísticos, específicamente un Recocido Simulado y un algoritmo Genético. Finalmente, se evalúan los resultados de los algoritmos propuestos empleando un conjunto de datos generados en el marco de este trabajo., [EN] A financial entity is any entity that aims to offer financial services to its clients. These services range from examples of everyday life such as: collecting and remunerating savings, granting loans and credits; to investment advice in different markets. In this sense, financial advisors prepare investment proposals to their clients with the aim of maximizing their benefits. To execute an investment proposal, an advisor needs a tool capable of calculating the set of operations that must be carried out in the market. In the present work, algorithms are proposed that solve the previously exposed problem. To do this, first, the problem of automatic rebalancing of financial portfolios is formally defined, taking into account the requirements of the entities. Due to the high combinatorial explosion of the problem, it was decided to approach its solution using metaheuristic algorithms, specifically a Simulated Annealing and a Genetic algorithm. Finally, the results of the proposed algorithms are evaluated using a set of data generated in the framework of this work.

[ES] Una entidad financiera es cualquier entidad que tiene como objetivo ofrecer servicios de carácter financiero a sus clientes. Esos servicios van desde ejemplos de la vida cotidiana como: captación y remuneración de ahorros, concesión de préstamos y créditos; hasta el asesoramiento en inversión en los diferentes mercados. En este sentido, los asesores financieros elaboran propuestas de inversión a sus clientes con el objetivo de maximizar sus beneficios. Para ejecutar una propuesta de inversión un asesor necesita de una herramienta capaz de calcular el conjunto de operaciones que deben realizarse en el mercado. En el presente trabajo se proponen algoritmos que resuelven el problema anteriormente expuesto. Para ello, en primer lugar, se define formalmente el problema del rebalanceo automático de carteras financieras, teniendo en cuenta los requisitos de las entidades. Debido a la alta explosión combinatoria del problema se decide abordar su solución mediante algoritmos metaheurísticos, específicamente un Recocido Simulado y un algoritmo Genético. Finalmente, se evalúan los resultados de los algoritmos propuestos empleando un conjunto de datos generados en el marco de este trabajo., [EN] A financial entity is any entity that aims to offer financial services to its clients. These services range from examples of everyday life such as: collecting and remunerating savings, granting loans and credits; to investment advice in different markets. In this sense, financial advisors prepare investment proposals to their clients with the aim of maximizing their benefits. To execute an investment proposal, an advisor needs a tool capable of calculating the set of operations that must be carried out in the market. In the present work, algorithms are proposed that solve the previously exposed problem. To do this, first, the problem of automatic rebalancing of financial portfolios is formally defined, taking into account the requirements of the entities. Due to the high combinatorial explosion of the problem, it was decided to approach its solution using metaheuristic algorithms, specifically a Simulated Annealing and a Genetic algorithm. Finally, the results of the proposed algorithms are evaluated using a set of data generated in the framework of this work.

Accurate solar radiation (SR) prediction is one of the essential prerequisites of harvesting solar energy. The current study proposed a novel intelligence model through hybridization of Adaptive Neuro-Fuzzy Inference System (ANFIS) with two metaheuristic optimization algorithms, Salp Swarm Algorithm (SSA) and Grasshopper Optimization Algorithm (GOA) (ANFIS-muSG) for global SR prediction at different locations of North Dakota, USA. The performance of the proposed ANFIS-muSG model was compared with classical ANFIS, ANFIS-GOA, ANFIS-SSA, ANFIS-Grey Wolf Optimizer (ANFIS-GWO), ANFIS-Particle Swarm Optimization (ANFIS-PSO), ANFIS-Genetic Algorithm (ANFIS-GA) and ANFISDragonfly Algorithm (ANFIS-DA). Consistent maximum, mean and minimum air temperature data for nine years (2010–2018) were used to build the models. ANFIS-muSG showed 25.7%–54.8% higher performance accuracy in terms of root mean square error compared to other models at different locations of the study areas. The model developed in this study can be employed for SR prediction from temperature only. The results indicate the potential of hybridization of ANFIS with the metaheuristic optimization algorithms for improvement of prediction ccuracy., Validerad;2020;Nivå 2;2020-12-02 (alebob)

Accurate solar radiation (SR) prediction is one of the essential prerequisites of harvesting solar energy. The current study proposed a novel intelligence model through hybridization of Adaptive Neuro-Fuzzy Inference System (ANFIS) with two metaheuristic optimization algorithms, Salp Swarm Algorithm (SSA) and Grasshopper Optimization Algorithm (GOA) (ANFIS-muSG) for global SR prediction at different locations of North Dakota, USA. The performance of the proposed ANFIS-muSG model was compared with classical ANFIS, ANFIS-GOA, ANFIS-SSA, ANFIS-Grey Wolf Optimizer (ANFIS-GWO), ANFIS-Particle Swarm Optimization (ANFIS-PSO), ANFIS-Genetic Algorithm (ANFIS-GA) and ANFISDragonfly Algorithm (ANFIS-DA). Consistent maximum, mean and minimum air temperature data for nine years (2010–2018) were used to build the models. ANFIS-muSG showed 25.7%–54.8% higher performance accuracy in terms of root mean square error compared to other models at different locations of the study areas. The model developed in this study can be employed for SR prediction from temperature only. The results indicate the potential of hybridization of ANFIS with the metaheuristic optimization algorithms for improvement of prediction ccuracy., Validerad;2020;Nivå 2;2020-12-02 (alebob)

This work presents the newly formulated Dynastic Optimization Algorithm, DOA as applied to the wind turbine micrositing problem. The data is acquired by the use of the standard MATLAB software at a wind speed of 12 m/s. The values of the efficiency of the algorithm, cost per installation of per unit turbine, and total dissipated power at each number of turbines installed are discussed. This algorithm is applied to two test functions and the results are described therein. It has been welldemonstrated that the proposed DOA exhibits superior performance over GA and DEA for test functions by hitting the minima very often and with higher precision. On the other hand DOA performance on WTM problem is also encouraging.

This work presents the newly formulated Dynastic Optimization Algorithm, DOA as applied to the wind turbine micrositing problem. The data is acquired by the use of the standard MATLAB software at a wind speed of 12 m/s. The values of the efficiency of the algorithm, cost per installation of per unit turbine, and total dissipated power at each number of turbines installed are discussed. This algorithm is applied to two test functions and the results are described therein. It has been welldemonstrated that the proposed DOA exhibits superior performance over GA and DEA for test functions by hitting the minima very often and with higher precision. On the other hand DOA performance on WTM problem is also encouraging.

Магістерська дисертація: 91 с., 23 рис., 7 табл., 37 джерел, 1 додаток. Актуальність. Розробка якісного програмного продукту - це складний процес, який вимагає високого рівня підготовки команди розробників. Задля підвищення якості та роботоспроможності програмних продуктів потрібно велику увагу приділяти тестуванню. Ручне тестування в деяких випадках займає більше часу для перевірки системи, в той час як автоматизоване тестування дозволяє значно заощадити витрати компанії-клієнта, зекономити ресурси та час на тестування та підтримку високоякісної продукції та зменшити ризик випуску неякісних продуктів або продуктів, які не відповідаюсь потребам замовника. Ось чому технологія автоматизації тестування дуже популярна серед компаній, що займаються інформаційними технологіями, робота яких пов’язана з розробкою програмного забезпечення. Зв'язок роботи з науковими програмами, планами, темами. Робота виконувалась на кафедрі автоматизованих систем обробки інформації та управління Національного технічного університету України «Київський політехнічний інститут ім. Ігоря Сікорського» в рамках теми «Ефективні методи розв'язання задач теорії розкладів» (№ ДР 0117U000919). Мета дослідження - підвищення якості та надійності програмного продукту за рахунок автоматизації процесу тестуванння, що дозволяє скоротити час та витрати на виконання процесів тестування. Для досягнення мети необхідно виконати наступні завдання: проаналізувати існуючі методи автоматизації тестування програмного забезпечення; провести аналіз відомих робіт з розв’язання поставленої в рамках роботи задачі; розробити методи побудови плану тестування в залежності від обраної стратегії; розробити метаевристичні алгоритми покриття множини для вирішення задачі побудови плану тестування; виконати програмну реалізацію розроблених алгоритмів; налагодити інтеграцію з TeamCity; проаналізувати дані обчислювальних експериментів для визначення параметрів алгоритмів; провести порівняльний аналіз розроблених алгоритмів обраної с, Topicality. Developing a quality software product is a complex process that requires a high level of training from the development team. In order to improve the quality and performance of software products, great attention should be paid to testing. Manual testing in some cases takes more time to test the system, while automated testing can significantly reduce the cost of customer companies, save resources and time used to test and maintain high product quality, and reduce the risk of marketing a substandard product. or a product that does not meet the needs of users. That is why testing automation technologies are quite popular in information technology companies, whose work is related to software development. Relationship of work with scientific programs, plans, themes. The work was performed at the Department of Computer-Aided Management And Data Processing Systems of the National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute» within the topic «Effective methods for solving problems of schedule theory» (state registration number 0117U000919) The purpose of the study is to improve the quality and reliability of the software product by automating the testing process, which reduces the time and cost of testing. To achieve this goal you need to perform the following tasks: to analyze existing methods for software testing automation; to analyze the known work to solve the problem set in the work; to develop methods for building a testing plan depending on the chosen strategy; to develop metaheuristic algorithms for covering the set to solve the problem of constructing a test plan; to perform software implementation of the developed algorithms; to establish integration with TeamCity; to analyze the data of computational experiments to determine the parameters of algorithms; to carry out the comparative analysis of the developed algorithms of the chosen strategy. Object of research is the process of software testing. Subject of research is m

Актуальність. Для холдингу Fozzy дуже важливою компонентою є ефективна робота працівників, особливо тих, які працюють поза межами офісу. Департаменту дуже важливо розуміти, як працює ця категорія людей, які поставлені перед ними задачі, і як вони з ними справляються. Тому, використання інформаційних технологій з метою організації часу мобільної групи є актуальною задачею на сьогоднішній день, тому що це не тільки допоможе працівникам у роботі, а і надасть можливість керівництву чітко усвідомлювати ті задачі, які поставлені перед працівником і відслідковувати їх виконання та завантаженість кожного із них. Саме тому і набули широкого розповсюдження персоналізовані електронні органайзери (Microsoft Outlook), у функціонал яких покладено задачу організації робочого часу. Математична модель може дещо змінюватись у зв’язку зі зміною умов досліджуваної області. Математичною моделлю цієї роботи є задача Організації робочого часу Куратора СБ з використанням часових вікон (Security Leader Problem with Time Windows, SLPTW). Мета роботи і задачі дослідження. Метою є підвищення ефективності організації процесу відвідування філій та вирішення задач працівником мобільної групи. Для досягнення поставленої мети необхідно вирішити такі завдання:  провести аналітику відомих методів вирішення задачі SLPTW;  удосконалити існуючий метод розв’язання задачі за допомогою технологій паралельного програмування;  програмно реалізувати алгоритм SLPTW;  вирішити задачу алгоритмічної реалізації алгоритму;  провести дослідження на ефективність реалізованого алгоритму.[] Об’єкт дослідження – є процес прокладання маршрутів керівнику мобільної групи з питань охорони та безпеки торгівельної мережі. Предмет дослідження – задача підвищення ефективності організації робочого часу працівника мобільної групи. Методи дослідження, використані в роботі, відносять до класу алгоритмів метаевристики. Наукова новизна отриманих результатів базується на вдосконаленні алгоритму ILS (повторюваного локального пошук, For the Fozzy group holding, a very important component is the efficient work of employees, especially those who work outside the office. It is very important for the department to understand how this category of people who are tasked with it and how they handle it. Therefore, the use of information technology to organize the time of the mobile group is an urgent task today, because it will not only help workers in the work, but also allow management to clearly understand the tasks that are set before the employee and monitor their performance, and the workload of each of them. In this regard, personalized electronic organizers (Microsoft Outlook), which include the functionality of working time, have become widespread. When solving it, the mathematical model may differ depending on which domain conditions are taken into account. In this paper, the mathematical model is the task of organizing the Security Leader Problem with Time Windows (SLPTW). Since response time for software is an important feature, developing an efficient algorithm for the task at hand is an up-to-date task. Therefore, this work is dedicated to the research and refinement of SLPTW. Purpose and tasks of the study. The goal is to maximize the aggregate value of visiting affiliates and solving problems with a mobile group employee. To achieve this goal it is necessary to solve the following tasks:  analyze known results of solving the SLPTW task;  to develop a method (modification of an existing method) of solving a problem using parallel programming technologies;  develop algorithmic support for the SLPTW task;  to develop software implementation of algorithm (s); Сonduct research on the effectiveness of the developed algorithmic support. The object of study is the process of drawing routes to a mobile group employee. The subject of the study - the task of improving the organization of working hours of a mobile group employee. The research methods used in the work are based on metaheuristic a

When a model-based approach is appropriate, an optimal design can guide how tocollect data judiciously for making reliable inference at minimal cost. However, findingoptimal designs for a statistical model with several possibly interacting factors can beboth theoretically and computationally challenging, and this issue is rarely discussed inthe literature. We propose nature-inspired metaheuristic algorithms, like particle swarmoptimization (PSO) and its variants, to solve such optimization problems. Wedemonstrate that such techniques, which are easy to implement, can find differenttypes of optimal designs for models with several factors efficiently. To facilitate use ofsuch algorithms, we provide computer codes to generate tailor made optimal designsand evaluate efficiencies of competing designs. As applications, we apply PSO and findBayesian optimal designs for Exponential models useful in HIV studies and re-design acar-refuelling study for a Logistic model with ten factors and some interacting factors.

The presented paper describes a use of metaheuristic algorithm for medical image segmentation. First section is dedicated to a brief introduction to the principles of this kind of segmentation and second section is used for description of used algorithms and used approaches to segmentation. Next sections are used for presentations of achieved results.

The presented paper describes a use of metaheuristic algorithm for medical image segmentation. First section is dedicated to a brief introduction to the principles of this kind of segmentation and second section is used for description of used algorithms and used approaches to segmentation. Next sections are used for presentations of achieved results.

Магістерська дисертація: 110 с., 20 рис., 22 табл., 1 додаток, 84 джерела. Актуальність. Всесвітня туристська організація (World Tourism Organization, UNWTO) визначає впровадження нововведень в туризмі однією з основних функцій туристичного маркетингу. Тому, використання інформаційних технологій з метою розвитку туризму, є актуальною задачею на сьогоднішній день. В зв’язку з цим, широкого розповсюдження набули персоналізовані електронні туристичні путівники (Personalized Electronic Tourist guides, PETs), до функціональності яких відноситься задача побудови туристичних маршрутів. При її розв’язанні математична модель може відрізнятись з огляду на те, які умови предметної області враховуються. В даній роботі математичною моделлю виступає задача Командного спортивного орієнтування з часовими вікнами (Team Orienteering Problem with Time Windows, TOPTW). Оскільки час реагування для програмного забезпечення є важливою ознакою, розробка ефективного алгоритму поставленої задачі на сьогоднішній день є актуальною задачею. Тому, дана робота присвячена дослідженню та удосконаленню розв’язування TOPTW. Мета роботи і задачі дослідження. Метою є максимізація сумарної корисності побудованих туристичних маршрутів заданої тривалості з врахуванням часових періодів відвідування туристичних місць. Для досягнення поставленої мети необхідно вирішити такі завдання: − провести аналіз відомих результатів розв’язування задачі TOPTW; − розробити метод (модифікацію існуючого методу) розв’язання задачі з використанням технологій паралельного програмування; − розробити алгоритмічне забезпечення задачі TOPTW; − розробити програмну реалізацію алгоритму(ів); − провести дослідження ефективності розробленого алгоритмічного забезпечення. Об’єкт дослідження – процес складання туристичних маршрутів. 4 Предмет дослідження – задача командного спортивного орієнтування з часовими вікнами. Методи дослідження, застосовані в роботі, базуються на метаевристичних алгоритмах. Наукова новизна одержаних результатів, Master dissertation:110 p., 20 fig., 22 tab., 1 appendix, 84 sources. Relevance. The World Tourism Organization (UNWTO) identifies the introduction of tourism innovations as one of the main functions of tourism marketing. Therefore, the use of information technology for the development of tourism is an actual task to date. Because of that, personalized Electronic Tourist Guides (PETs), which encapsulate tourist trip design problem (TTDP), have become widespread. When TTDP is solved, the mathematical model may differ in terms of what the terms of the subject area are taken into account. In this paper, the problem of Team Orienteering Problem with Time Windows (TOPTW) is the mathematical model. As the response time for the software is an important feature, developing an effective algorithm for the task to date is an actual task. Therefore, this work is devoted to the research and improvement of the solution methods of TOPTW. Purpose and objectives of the study. The purpose is to maximize the total usefulness of the built tourist routes of a given duration, taking into account the time periods of visiting tourist places. To achieve this goal it is necessary to solve the following tasks: − to analyze known results of solving TOPTW; − to develop a method (modification of the existing method) for solving a problem using parallel programming technologies; − to develop algorithms for TOPTW; − to develop software implementation of the algorithms; − to study the effectiveness of the developed algorithms. The object of study – the process of designing tourist routes. Purpose of the study –team orienteering problem with time windows. The scientific novelty of the obtained results is in modifying the Iterated Local Search algorithm, comparing it with the algorithm of Simulated annealing, using parallel programming technologies for modifying the Iterated Local Search algorithms and the Simulated annealing algorithm for solving TOPTW. 6 Relationship of work with scientific program

Актуальність теми. В наш час важко уявити життя без телекомунікаційних мереж. Мережа інтернет, яка є телекомунікаційною мережею, розширюється з кожним днем долучаючи тисячі нових користувачів. Україна є однією з країн, в якій інтернет розвивається швидкими темпами. На 2017 рік у світі нараховувалось близько 4 мільярдів користувачів всесвітньої мережі або 50% населення Землі. Значно зростає кількість користувачів соцмереж і частка мобільних пристроїв у мережі. Незважаючи на те, що майже всі куточки планети покриті мережею, у багатьох місцях все ще наявна незначна пропускна спроможність мережі. В цьому випадку ефективне розв’язання задачі пошуку найкоротших шляхів між заданими вузлами є надзвичайно важливою задачею. Об’єктом дослідження є процес комбінаторної оптимізації при розв’язанні задачі пошуку найкоротших шляхів в телекомунікаційній мережі. Предметом дослідження є евристичні методи розв’язання задачі пошуку найкоротших шляхів в телекомунікаційній мережі. Метою роботи є розробка нового методу розв’язання задачі пошуку найкоротших шляхів в телекомунікаційній мережі, що характеризується вищою швидкодією та якістю розв’язків ніж відомі методи. Методи дослідження. В роботі використовуються методи евристичних алгоритмів, методи дискретної математики, методи комбінаторної оптимізації., Actuality of theme. In our time, it's hard to imagine a life without telecommunication networks. The Internet, which is a telecommunication network, is expanding every day by engaging thousands of new users. Ukraine is one of the countries in which the Internet is developing rapidly. By 2017, the world has about 4 billion users worldwide or 50% of the Earth's population. The number of social network users and the share of mobile devices in the network is growing significantly. Even though almost all the corners of the planet are covered by the network, in many places there is still a small bandwidth of the network. In this case, an effective solution to the problem of finding the shortest paths between the given nodes is an extremely important task. The object of the study is the process of combinatorial optimization in solving the problem of finding the shortest paths in the telecommunication network. The subject of the research is the heuristic methods of solution of shortest path problem in the telecommunication network. The aim of the work is to develop a new method for solving the problem of finding the shortest paths in a telecommunication network characterized by higher speed and quality of solutions than known methods. Research methods. Methods of heuristic algorithms, methods of discrete mathematics, methods of combinatorial optimization are used in this work., Актуальность темы. В наше время трудно представить жизнь без телекоммуникационных сетей. Сеть интернет, которая является телекоммуникационной сетью, расширяется с каждым днем привлекая тысячи новых пользователей. Украина является одной из стран, в которой интернет развивается быстрыми темпами. На 2017 год в мире насчитывалось около 4 миллиардов пользователей всемирной сети или 50% населения Земли. Значительно возрастает количество пользователей соцсетей и доля мобильных устройств в сети. Несмотря на то, что почти все уголки планеты покрыты сетью, во многих местах все еще имеется незначительная пропускная способность сети. В этом случае эффективное решение задачи поиска кратчайших путей между заданными узлами является чрезвычайно важной задачей. Объектом исследования является процесс комбинаторной оптимизации при решении задачи поиска кратчайших путей в телекоммуникационной сети. Предметом исследования является эвристические методы решения задачи поиска кратчайших путей в телекоммуникационной сети. Целью работы является разработка нового метода решения задачи поиска кратчайших путей в телекоммуникационной сети, характеризующееся высоким быстродействием и качеством решений чем известные методы. Методы исследования. В работе используются методы эвристических алгоритмов, методы дискретной математики, методы комбинаторной оптимизации.

The presented paper describes a use of metaheuristic algorithm for medical image segmentation. First section is dedicated to a brief introduction to the principles of this kind of segmentation and second section is used for description of used algorithms and used approaches to segmentation. Next sections are used for presentations of achieved results.

The focus of this research is on development of new methods for Building Optimisation Problems (BOPs) and deploying them on realistic case studies to evaluate their performance and utility. First, a new optimisation algorithm based on Ant Colony Optimisation was developed for solving simulation-based optimisation approaches. Secondly, a new surrogate-model optimisation method was developed using active learning approaches to accelerate the optimisation process. Both proposed methods demonstrated better performance than benchmark methods. Finally, a multi-objective scenario-based optimisation was introduced to address uncertainty in BOPs. Results demonstrated the capability of the proposed uncertainty methodology to find a robust design.

We consider the Location-Routing Problem with Heterogeneous Fleet (LRPH) in which the goal is to determine the depots to be opened, the customers to be assigned to each open depot, and the corresponding routes fulfilling the demand of the customers and by considering a heterogeneous fleet. We propose a comparison of granular approaches of Simulated Annealing (GSA), of Variable Neighborhood Search (GVNS) and of a probabilistic Tabu Search (pGTS) for the LRPH. Thus, the proposed approaches consider a subset of the search space in which non-favorable movements are discarded regarding a granularity factor. The proposed algorithms are experimentally compared for the solution of the LRPH, by taking into account the CPU time and the quality of the solutions obtained on the instances adapted from the literature. The computational results show that algorithm GSA is able to obtain high quality solutions within short CPU times, improving the results obtained by the other proposed approaches., Nosotros consideramos el problema de localización y ruteo de vehículos con flota heterogénea (LRPH) en el cual la meta es determinar los depósitos a ser abiertos, los clientes asignados a cada deposito, y las rutas que satisfagan la demanda de los clientes considerando una flota heterogénea. Nosotros proponemos una comparación de algoritmos granulares de Recocido Simulado (GSA), Búsqueda de Vecindario Variable (GVNS) y Tabú Search probabilístico (pGTS) para el LRPH. De esta manera, los algoritmos propuestos consideran un subconjunto del espacio en el cual los movimientos menos favorables son descartados según un factor de granularidad. Los algoritmos propuestos son comparados experimentalmente para la solución del LRPH, considerando el tiempo de CPU y la calidad de la solución obtenida en instancias adaptadas de la literatura. Los resultados computacionales muestran que el algoritmos GSA es capaz de obtener buenas soluciones en tiempos computacionales reducidos, mejorando los resultados obtenidos por los otros algoritmos propuestos.

We consider the Location-Routing Problem with Heterogeneous Fleet (LRPH) in which the goal is to determine the depots to be opened, the customers to be assigned to each open depot, and the corresponding routes fulfilling the demand of the customers and by considering a heterogeneous fleet. We propose a comparison of granular approaches of Simulated Annealing (GSA), of Variable Neighborhood Search (GVNS) and of a probabilistic Tabu Search (pGTS) for the LRPH. Thus, the proposed approaches consider a subset of the search space in which non-favorable movements are discarded regarding a granularity factor. The proposed algorithms are experimentally compared for the solution of the LRPH, by taking into account the CPU time and the quality of the solutions obtained on the instances adapted from the literature. The computational results show that algorithm GSA is able to obtain high qualitysolutions withinshort CPU times, improving the results obtained by the other proposed approaches., Nosotros consideramos el problema de localización y ruteo de vehículos con flota heterogénea (LRPH) en el cual la meta es determinar los depósitos a ser abiertos, los clientes asignados a cada deposito, y las rutas que satisfagan la demanda de los clientes considerando una flota heterogénea. Nosotros proponemos una comparación de algoritmos granulares de Recocido Simulado (GSA), Búsqueda de Vecindario Variable (GVNS) y Tabú Search probabilístico (pGTS) para el LRPH. De esta manera, los algoritmos propuestos consideran un subconjunto del espacio en el cual los movimientos menos favorables son descartados según un factor de granularidad. Los algoritmos propuestos son comparados experimentalmente para la solución del LRPH, considerando el tiempo de CPU y la calidad de la solución obtenida en instancias adaptadas de la literatura. Los resultados computacionales muestran que el algoritmos GSA es capaz de obtener buenas soluciones en tiempos computacionales reducidos, mejorando los resultados obtenidos por los otros algoritmos propuestos.

We consider the Location-Routing Problem with Heterogeneous Fleet (LRPH) in which the goal is to determine the depots to be opened, the customers to be assigned to each open depot, and the corresponding routes fulfilling the demand of the customers and by considering a heterogeneous fleet. We propose a comparison of granular approaches of Simulated Annealing (GSA), of Variable Neighborhood Search (GVNS) and of a probabilistic Tabu Search (pGTS) for the LRPH. Thus, the proposed approaches consider a subset of the search space in which non-favorable movements are discarded regarding a granularity factor. The proposed algorithms are experimentally compared for the solution of the LRPH, by taking into account the CPU time and the quality of the solutions obtained on the instances adapted from the literature. The computational results show that algorithm GSA is able to obtain high qualitysolutions withinshort CPU times, improving the results obtained by the other proposed approaches., Nosotros consideramos el problema de localización y ruteo de vehículos con flota heterogénea (LRPH) en el cual la meta es determinar los depósitos a ser abiertos, los clientes asignados a cada deposito, y las rutas que satisfagan la demanda de los clientes considerando una flota heterogénea. Nosotros proponemos una comparación de algoritmos granulares de Recocido Simulado (GSA), Búsqueda de Vecindario Variable (GVNS) y Tabú Search probabilístico (pGTS) para el LRPH. De esta manera, los algoritmos propuestos consideran un subconjunto del espacio en el cual los movimientos menos favorables son descartados según un factor de granularidad. Los algoritmos propuestos son comparados experimentalmente para la solución del LRPH, considerando el tiempo de CPU y la calidad de la solución obtenida en instancias adaptadas de la literatura. Los resultados computacionales muestran que el algoritmos GSA es capaz de obtener buenas soluciones en tiempos computacionales reducidos, mejorando los resultados obtenidos por los otros algoritmos propuestos.

We consider the Location-Routing Problem with Heterogeneous Fleet (LRPH) in which the goal is to determine the depots to be opened, the customers to be assigned to each open depot, and the corresponding routes fulfilling the demand of the customers and by considering a heterogeneous fleet. We propose a comparison of granular approaches of Simulated Annealing (GSA), of Variable Neighborhood Search (GVNS) and of a probabilistic Tabu Search (pGTS) for the LRPH. Thus, the proposed approaches consider a subset of the search space in which non-favorable movements are discarded regarding a granularity factor. The proposed algorithms are experimentally compared for the solution of the LRPH, by taking into account the CPU time and the quality of the solutions obtained on the instances adapted from the literature. The computational results show that algorithm GSA is able to obtain high quality solutions within short CPU times, improving the results obtained by the other proposed approaches., Nosotros consideramos el problema de localización y ruteo de vehículos con flota heterogénea (LRPH) en el cual la meta es determinar los depósitos a ser abiertos, los clientes asignados a cada deposito, y las rutas que satisfagan la demanda de los clientes considerando una flota heterogénea. Nosotros proponemos una comparación de algoritmos granulares de Recocido Simulado (GSA), Búsqueda de Vecindario Variable (GVNS) y Tabú Search probabilístico (pGTS) para el LRPH. De esta manera, los algoritmos propuestos consideran un subconjunto del espacio en el cual los movimientos menos favorables son descartados según un factor de granularidad. Los algoritmos propuestos son comparados experimentalmente para la solución del LRPH, considerando el tiempo de CPU y la calidad de la solución obtenida en instancias adaptadas de la literatura. Los resultados computacionales muestran que el algoritmos GSA es capaz de obtener buenas soluciones en tiempos computacionales reducidos, mejorando los resultados obtenidos por los otros algoritmos propuestos.

This article presents a strategy for designing optimal planar electromagnetic absorbers of several layers. The electromagnetic absorbers designed can operate in different frequency ranges. They were optimized by using a well-known spiral optimization algorithm. Results for the case of three, five, seven, and nine layers, showed the advantage of using this algorithm, mainly in terms of fewer adjustable parameters and its great capacity for intensification and diversification. Using Spiral Optimization, electromagnetic absorbers with seven and nine layers were designed for the frequency range between 0.8-5.4 GHz. They achieved a minimum attenuation of -26.13 dB and -25.66 dB, with respective thicknesses of 6.26 mm and 8.64 mm. The seven-layered design performed better than the nine-layered one. The results obtained were compared to those reported with other global optimization methods., Este artículo presenta una estrategia de diseño de absorbedores electromagnéticos planares multicapa óptimos con diferentes rangos de frecuencias de operación, mediante el algoritmo de optimización de la espiral. Se obtuvieron resultados para el caso de tres, cinco, siete y nueve capas, que posteriormente se compararon con los reportados utilizando otros métodos. Adicionalmente, se evidenció la fortaleza del algoritmo en este tipo de problemas, principalmente en su sencillez en el número de parámetros que se van a ajustar y su gran capacidad de intensificación y diversificación. Mediante el algoritmo de la espiral se diseñaron absorbedores de siete y nueve capas, para el rango de frecuencias entre 0,8 y 5,4 GHz. Ellos alcanzaron un mínimo de atenuación de -26,13 dB y -25,66 dB con sus correspondientes espesores de 6,26 y 8,64 mm. Los resultados se compararon con los reportados mediante otros métodos de optimización global.

This article presents a strategy for designing optimal planar electromagnetic absorbers of several layers. The electromagnetic absorbers designed can operate in different frequency ranges. They were optimized by using a well-known spiral optimization algorithm. Results for the case of three, five, seven, and nine layers, showed the advantage of using this algorithm, mainly in terms of fewer adjustable parameters and its great capacity for intensification and diversification. Using Spiral Optimization, electromagnetic absorbers with seven and nine layers were designed for the frequency range between 0.8-5.4 GHz. They achieved a minimum attenuation of -26.13 dB and -25.66 dB, with respective thicknesses of 6.26 mm and 8.64 mm. The seven-layered design performed better than the nine-layered one. The results obtained were compared to those reported with other global optimization methods., Este artículo presenta una estrategia de diseño de absorbedores electromagnéticos planares multicapa óptimos con diferentes rangos de frecuencias de operación, mediante el algoritmo de optimización de la espiral. Se obtuvieron resultados para el caso de tres, cinco, siete y nueve capas, que posteriormente se compararon con los reportados utilizando otros métodos. Adicionalmente, se evidenció la fortaleza del algoritmo en este tipo de problemas, principalmente en su sencillez en el número de parámetros que se van a ajustar y su gran capacidad de intensificación y diversificación. Mediante el algoritmo de la espiral se diseñaron absorbedores de siete y nueve capas, para el rango de frecuencias entre 0,8 y 5,4 GHz. Ellos alcanzaron un mínimo de atenuación de -26,13 dB y -25,66 dB con sus correspondientes espesores de 6,26 y 8,64 mm. Los resultados se compararon con los reportados mediante otros métodos de optimización global.

Simulation is of primal importance in the prediction of the produced power and automatic fault detection in PV grid-connected systems (PVGCS). The accuracy of simulation results depends on the models used for main components of the PV system, especially for the PV module. The present paper compares two PV array models, the five-parameter model (5PM) and the Sandia Array Performance Model (SAPM). Five different algorithms are used for estimating the unknown parameters of both PV models in order to see how they affect the accuracy of simulations in reproducing the outdoor behavior of three PVGCS. The arrays of the PVGCS are of three different PV module technologies: Crystalline silicon (c-Si), amorphous silicon (a-Si:H) and micromorph silicon (a-Si:H/µc-Si:H). The accuracy of PV module models based on the five algorithms is evaluated by means of the Route Mean Square Error (RMSE) and the Normalized Mean Absolute Error (NMAE), calculated for different weather conditions (clear sky, semi-cloudy and cloudy days). For both models considered in this study, the best accuracy is obtained from simulations using the estimated values of unknown parameters delivered by the ABC algorithm. Where, the maximum error values of RMSE and NMAE stay below 6.61% and 2.66% respectively., Peer Reviewed, Postprint (author's final draft)

The cross-ambiguity function (CAF) relates to the correlation processing of signals in radar, sonar, and communication systems in the presence of delays and Doppler shifts. It is a commonly used tool in the analysis of signals in these systems when both delay and Doppler shifts are present. In this chapter, we aim to tackle the CAF synthesization problem such that the synthesized CAF approximates a desired CAF. A CAF synthesization problem is addressed by jointly designing a pair of waveforms using a metaheuristic approach based on the echolocation of bats. Through four examples, it is shown that such an approach can be used as an effective tool in synthesizing different types of CAFs., Series: Studies in Computational Intelligence, Vol. 585

This paper considers the Location-Routing Problem with Heterogeneous Fleet (LRPH), in which the aim is to determine the depots to be opened, the customers to be assigned to each open depot, and the routes to be performed to fulfill the demand of the customers by considering a heterogeneous fleet with different capacities and associated costs. The objective is to minimize the sum of the cost of the open depots, of the used vehicle costs, and of the variable costs related with the distance traveled by the performed routes. In this paper, it is proposed a metaheuristic algorithm based on a granular tabu search to solve the LRPH. Computational experiments on adapted benchmark instances from the literature show that the proposed approach is able to obtain, within short computing times, high quality solutions illustrating its effectiveness., Este artículo considera el problema de localización y ruteo con flota heterogénea (LRPH, por sus siglas en inglés), en el cual se busca determinar los depósitos a ser abiertos, los clientes a ser asignados a cada depósito, y las rutas a ser construidas para satisfacer las demandas de los clientes, considerando una flota de vehículos con capacidad diversa y costos de utilización asociados. El objetivo es minimizar la suma de los costos asociados con la apertura de depósitos, los costos de los vehículos utilizados, y los costos variables directamente relacionados con las distancias recorridas. En este artículo, se propone un algoritmo metaheurístico basado en una búsqueda tabú granular para la resolución del problema. Experimentos computacionales en instancias adaptadas de la literatura, muestran que el algoritmo propuesto es capaz de obtener, en tiempos computacionales razonables, soluciones de alta calidad demostrando su efectividad.

This paper considers the Location-Routing Problem with Heterogeneous Fleet (LRPH), in which the aim is to determine the depots to be opened, the customers to be assigned to each open depot, and the routes to be performed to fulfill the demand of the customers by considering a heterogeneous fleet with different capacities and associated costs. The objective is to minimize the sum of the cost of the open depots, of the used vehicle costs, and of the variable costs related with the distance traveled by the performed routes. In this paper, it is proposed a metaheuristic algorithm based on a granular tabu search to solve the LRPH. Computational experiments on adapted benchmark instances from the literature show that the proposed approach is able to obtain, within short computing times, high quality solutions illustrating its effectiveness., Este artículo considera el problema de localización y ruteo con flota heterogénea (LRPH, por sus siglas en inglés), en el cual se busca determinar los depósitos a ser abiertos, los clientes a ser asignados a cada depósito, y las rutas a ser construidas para satisfacer las demandas de los clientes, considerando una flota de vehículos con capacidad diversa y costos de utilización asociados. El objetivo es minimizar la suma de los costos asociados con la apertura de depósitos, los costos de los vehículos utilizados, y los costos variables directamente relacionados con las distancias recorridas. En este artículo, se propone un algoritmo metaheurístico basado en una búsqueda tabú granular para la resolución del problema. Experimentos computacionales en instancias adaptadas de la literatura, muestran que el algoritmo propuesto es capaz de obtener, en tiempos computacionales razonables, soluciones de alta calidad demostrando su efectividad.

This paper considers the Location-Routing Problem with Heterogeneous Fleet (LRPH), in which the aim is to determine the depots to be opened, the customers to be assigned to each open depot, and the routes to be performed to fulfill the demand of the custo, Este documento considera el problema de ubicación y enrutamiento con la flota heterogénea (LRPH), en el que el objetivo es determinar los depósitos que se abrirán, los clientes que se asignarán a cada depósito abierto y las rutas que se realizarán para sa

El particionado hardware/software es una tarea fundamental en el co-diseño de sistemas embebidos. En ella se decide, teniendo en cuenta las métricas de diseño, qué componentes se ejecutarán en un procesador de propósito general (software) y cuáles en un hardware específico. En los últimos años se han propuesto diversas soluciones al problema del particionado dirigidas por algoritmos metaheurísticos. Sin embargo, debido a la diversidad de modelos y métricas utilizadas, la elección del algoritmo más apropiado sigue siendo un problema abierto. En este trabajo se presenta una comparación de seis algoritmos metaheurísticos: Búsqueda aleatoria (Random search), Búsqueda tabú (Tabu search), Recocido simulado (Simulated annealing), Escalador de colinas estocástico (Stochastic hill climbing), Algoritmo genético (Genetic algorithm) y Estrategia evolutiva (Evolution strategy). El modelo utilizado en la comparación está dirigido a minimizar el área ocupada y el tiempo de ejecución, las restricciones del modelo son consideradas como penalizaciones para incluir en el espacio de búsqueda otras soluciones. Los resultados muestran que los algoritmos Escalador de colinas estocástico y Estrategia evolutiva son los que mejores resultados obtienen en general, seguidos por el Algoritmo genético., Hardware/software partitioning is a key task for embedded system co-design. The main goal of this task is to decide which components of an application will be executed in a general purpose processor (software) and which ones on a specific hardware. In last years, several approaches have been proposed for solving the HSP problem, directed by metaheuristics algorithms. However, due to diversity of models and metrics used, the choice of the best suited algorithm is an open problem yet. This article presents a comparison of six metaheuristics algorithms: Random Search, Tabu Search, Simulated Annealing, Hill Climbing, Genetic Algorithm and Evolution Strategy. The model presented is aimed to minimize the hardware area and execution time, restrictions are considered as penalizations in order to include in the search space other solutions. Results show that Hill climbing and Evolution strategy are the algorithms that obtains best results in general, followed by the Genetic Algorithm.

Hardware/Software partitioning (HSP) is a key task for embedded system co-design. The main goal of this task is to decide which components of an application are to be executed in a general purpose processor (software) and which ones, on a specific hardware, taking into account a set of restrictions expressed by metrics. In last years, several approaches have been proposed for solving the HSP problem, directed by metaheuristic algorithms. However, due to diversity of models and metrics used, the choice of the best suited algorithm is an open problem yet. This article presents the results of applying a fuzzy approach to the HSP problem. This approach is more flexible than many others due to the fact that it is possible to accept quite good solutions or to reject other ones which do not seem good. In this work we compare six metaheuristic algorithms: Random Search, Tabu Search, Simulated Annealing, Hill Climbing, Genetic Algorithm and Evolutionary Strategy. The presented model is aimed to simultaneously minimize the hardware area and the execution time. The obtained results show that Restart Hill Climbing is the best performing algorithm in most cases., El Particionado Hardware/Software (PHS) es una etapa fundamental en el co-diseño de sistemas embebidos. El objetivo principal de esta etapa es decidir qué componentes de la aplicación serían ejecutados en un procesador de propósito general (software) y cuáles en un hardware específico, teniendo en cuenta las restricciones. En los últimos años, se han propuesto diferentes estrategias para resolver el problema PHS, las cuales utilizan en su mayoría algoritmos metaheurísticos. Sin embargo, debido a la diversidad de modelos y métricas utilizadas, decidir qué algoritmo es mejor que otro es un problema abierto. Este artículo presenta los resultados de aplicar lógica difusa en el problema PHS. Esta estrategia es más flexible que muchas de las otras propuestas, ya que es posible aceptar soluciones bastante buenas o rechazar otras que no parezcan buenas. Además en este trabajo se comparan seis algoritmos metaheurísticos: Búsqueda aleatoria, Búsqueda tabú, Recocido simulado, Escalador de colinas, Algoritmo genético y Estrategia evolutiva. El modelo que se presenta está dirigido a minimizar de forma simultánea el área de hardware y el tiempo de ejecución del sistema. Los resultados muestran que el escalador de colinas es el algoritmo que obtiene mejores resultados en la mayoría de los casos.

El particionado hardware/software es una tarea fundamental en el co-diseño de sistemas embebidos. En ella se decide, teniendo en cuenta las métricas de diseño, qué componentes se ejecutarán en un procesador de propósito general (software) y cuáles en un hardware específico. En los últimos años se han propuesto diversas soluciones al problema del particionado dirigidas por algoritmos metaheurísticos. Sin embargo, debido a la diversidad de modelos y métricas utilizadas, la elección del algoritmo más apropiado sigue siendo un problema abierto. En este trabajo se presenta una comparación de seis algoritmos metaheurísticos: Búsqueda aleatoria (Random search), Búsqueda tabú (Tabu search), Recocido simulado (Simulated annealing), Escalador de colinas estocástico (Stochastic hill climbing), Algoritmo genético (Genetic algorithm) y Estrategia evolutiva (Evolution strategy). El modelo utilizado en la comparación está dirigido a minimizar el área ocupada y el tiempo de ejecución, las restricciones del modelo son consideradas como penalizaciones para incluir en el espacio de búsqueda otras soluciones. Los resultados muestran que los algoritmos Escalador de colinas estocástico y Estrategia evolutiva son los que mejores resultados obtienen en general, seguidos por el Algoritmo genético., Hardware/software partitioning is a key task for embedded system co-design. The main goal of this task is to decide which components of an application will be executed in a general purpose processor (software) and which ones on a specific hardware. In last years, several approaches have been proposed for solving the HSP problem, directed by metaheuristics algorithms. However, due to diversity of models and metrics used, the choice of the best suited algorithm is an open problem yet. This article presents a comparison of six metaheuristics algorithms: Random Search, Tabu Search, Simulated Annealing, Hill Climbing, Genetic Algorithm and Evolution Strategy. The model presented is aimed to minimize the hardware area and execution time, restrictions are considered as penalizations in order to include in the search space other solutions. Results show that Hill climbing and Evolution strategy are the algorithms that obtains best results in general, followed by the Genetic Algorithm.

Hardware/Software partitioning (HSP) is a key task for embedded system co-design. The main goal of this task is to decide which components of an application are to be executed in a general purpose processor (software) and which ones, on a specific hardware, taking into account a set of restrictions expressed by metrics. In last years, several approaches have been proposed for solving the HSP problem, directed by metaheuristic algorithms. However, due to diversity of models and metrics used, the choice of the best suited algorithm is an open problem yet. This article presents the results of applying a fuzzy approach to the HSP problem. This approach is more flexible than many others due to the fact that it is possible to accept quite good solutions or to reject other ones which do not seem good. In this work we compare six metaheuristic algorithms: Random Search, Tabu Search, Simulated Annealing, Hill Climbing, Genetic Algorithm and Evolutionary Strategy. The presented model is aimed to simultaneously minimize the hardware area and the execution time. The obtained results show that Restart Hill Climbing is the best performing algorithm in most cases., El Particionado Hardware/Software (PHS) es una etapa fundamental en el co-diseño de sistemas embebidos. El objetivo principal de esta etapa es decidir qué componentes de la aplicación serían ejecutados en un procesador de propósito general (software) y cuáles en un hardware específico, teniendo en cuenta las restricciones. En los últimos años, se han propuesto diferentes estrategias para resolver el problema PHS, las cuales utilizan en su mayoría algoritmos metaheurísticos. Sin embargo, debido a la diversidad de modelos y métricas utilizadas, decidir qué algoritmo es mejor que otro es un problema abierto. Este artículo presenta los resultados de aplicar lógica difusa en el problema PHS. Esta estrategia es más flexible que muchas de las otras propuestas, ya que es posible aceptar soluciones bastante buenas o rechazar otras que no parezcan buenas. Además en este trabajo se comparan seis algoritmos metaheurísticos: Búsqueda aleatoria, Búsqueda tabú, Recocido simulado, Escalador de colinas, Algoritmo genético y Estrategia evolutiva. El modelo que se presenta está dirigido a minimizar de forma simultánea el área de hardware y el tiempo de ejecución del sistema. Los resultados muestran que el escalador de colinas es el algoritmo que obtiene mejores resultados en la mayoría de los casos.

A review of metaheuristic algorithms (MAs) is presented. Levy flights (LFs) in the context of global optimization, the Levy distribution, a method to generate random numbers within this distribution are discussed. Subsequently, diversification and intensification in global optimization based on LFs are discussed. Then, MAs using LFs as a search mechanism to solve global optimization problems are presented along with highlighting the differences and similarities of different MAs.

In engineering design, optimization is customary, and often indispensable. Typical cases include minimization of drag for vehicles, minimization of weight for structures like buildings and bridges, maximization of power and lift for aircraft and rockets, minimization of fuel consumption for engines, etc. Therefore it comes as no surprise that development of fast and efficient optimization algorithms for engineering design is an actively pursued research area. In recent decades, metaheuristic algorithms have proven to be efficient, robust and versatile methods for numerical optimization. However, they usually need to evaluate a large number of candidate designs to find the optimum. This becomes prohibitive for engineering optimization problems in which each design evaluation may require computationally expensive analysis and, consequently, the optimization process may take much longer time than affordable. Considering that the number of design evaluations is a critical factor in overall optimization time, it is imperative to develop techniques to search for the optimum design using fewest evaluations possible. With this singular goal, this thesis investigates a range of domains in which existing approaches can be improved. Engineering problems are often highly non-linear, discontinuous, and non-differentiable, which rules out (or restricts) the applicability of analytical techniques for solving them. However, they exhibit additional attributes that prove challenging even to the existing metaheuristic techniques, thus making the search difficult and, consequently, creating a necessity for carrying out large numbers of evaluations. These include: (a) Constraints - constraints render a fraction (possibly large fraction) of the search space infeasible, making it hard to find the optimum and at times even a feasible design; (b) Large number of objectives - Pareto-dominance sorting, a commonly used technique in multi-objective optimization algorithms, is inadequate to solv

There are two principal technologies for these large-scale combinatorial optimization problems: 1) exact algorithms and 2) metaheuristic algorithms. This project will integrate concepts from these two technologies to develop generic optimization frameworks to find provably good solutions to large-scale discrete optimization problems often encountered in many real applications. The way that these two sets of methods will be used, and in particular the way in which they will be used together so that each complements the strengths of the other, will be novel and pioneering. In particular, this project will begin by exploring and capitalizing on the links between mixed integer programming decomposition approaches, such as Dantzig Wolfe decomposition and Lagrangian relaxation, and metaheuristics such as the Nested Partitions framework. While the relationship between these seemingly disparate approaches has not been exploited before, there is already evidence to suggest that capitalizing on this relationship to integrate these methods could yield solution frameworks that are more powerful than using either approach on its own.