162 research outputs found

    The optimal synthesis of scanned linear antenna arrays

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    In this paper, symmetric scanned linear antenna arrays are synthesized, in order to minimize the side lobe level of the radiation pattern. The feeding current amplitudes are considered as the optimization parameters. Newly proposed optimization algorithms are presented to achieve our target; Antlion Optimization (ALO) and a new hybrid algorithm. Three different examples are illustrated in this paper; 20, 26 and 30 elements scanned linear antenna array. The obtained results prove the effectiveness and the ability of the proposed algorithms to outperform and compete other algorithms like Symbiotic Organisms Search (SOS) and Firefly Algorithm (FA)

    Optimization of Invasive Weed for Optimal Dimensions of Concrete Gravity Dams

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    Dam construction projects among the most extensive and most expensive projects are considered. It is always appropriate and optimal for such concrete structures to reduce the volume of concrete and consequently reduce construction costs is essential. In this study, invasive weed optimization software GNU octave, dimensions of concrete gravity dam Koyna located in India optimized stability constraints. For this purpose, a cross-section with a length unit consists of eight geometric parameters as input variables, and other geometric parameters were defined using these variables. The result showed that invasive weeds are well-optimized dimensions of the dam as the volume of concrete in the construction of the dam at the current level measures 3633 cubic meters that optimal dropped 3353 cubic meters, which is a mean of 7.7% of the value of the objective function (the volume of concrete in dams) is reduced. This amount of concrete decreased the construction of the dam, saving the cost and is more economical

    A review of optimization approaches for controlling water-cooled central cooling systems

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    Buildings consume a large amount of energy across all sectors of society, and a large proportion of building energy is used by HVAC systems to provide a comfortable and healthy indoor environment. In medium and large-size buildings, the central cooling system accounts for a major share of the energy consumption of the HVAC system. Improving the cooling system efficiency has gained much attention as the reduction of cooling system energy use can effectively contribute to environmental sustainability. The control and operation play an important role in central cooling system energy efficiency under dynamic working conditions. It has been proven that optimization of the control of the central cooling system can notably reduce the energy consumption of the system and mitigate greenhouse gas emissions. In recent years, numerous studies focus on this topic to improve the performance of optimal control in different aspects (e.g., energy efficiency, stability, robustness, and computation efficiency). This paper provides an up-to-date overview of the research and development of optimization approaches for controlling water-cooled central cooling systems, helping readers to understand the new significant trends and achievements in this area. The optimization approaches have been classified as system-model-based and data-based. In this paper, the optimization methodology is introduced first by summarizing the key decision variables, objective function, constraints, and optimization algorithms. The principle and performance of various optimization approaches are then summarized and compared according to their classification. Finally, the challenges and development trends for optimal control of water-cooled central cooling systems are discussed

    НовыС ΠΏΠΎΠ΄Ρ…ΠΎΠ΄Ρ‹ ΠΊ ΡƒΠΏΡ€Π°Π²Π»Π΅Π½ΠΈΡŽ Ρ†Π΅Π½Π°ΠΌΠΈ Π½Π° транспортныС услуги

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    Development of new approaches to formation of analytics mechanisms for the purpose of pricing management of services is an important aspect of increasing the efficiency of transport management processes.Research aimed at improving the tools for determining the optimal parameters of the ratio of quality and price of service for formation of a competitive and efficient tariff policy continues to remain relevant and in demand in modern market conditions. The objective of the study, presented in the article, is to analyse and evaluate the prospects for implementation of the areas to improve the apparatus for assessing the price elasticity of demand for railway passenger transport services as the transition to the use of non-linear parameters in terms of customer behaviour modelling functions, as well as introduction of the most effective algorithms from the set of modern global mathematical optimisation tools.The research conclusions are based on the use of system analysis mechanisms, methods of economic and mathematical modelling and optimisation, as well as of non-parametric statistics tools.The results based on the use of an array of data on the demand of passengers of branded trains include: a comparative assessment of quality of modelling the price elasticity of demand using 15 functions that are nonlinear in terms of parameters; the most promising tools of the search for unknown parameters for non-smooth nonlinear functions for modelling the behaviour of railway customers are identified based on a three-stage procedure for comparative analysis of the performance of more than 60 optimisation algorithms (including the calculation of minima and medians for the sums of squares of modelling errors, bootstrap analysis, Kruskal– Wallace and Mann–Whitney tests, as well as the calculation of a metric specially developed by the authors for assessing the degree of superiority of one algorithm over another within the framework of non-parametric analysis).The findings seem able to be successfully used in relation to other modes of transport in solving similar problems of developing an effective toolkit for managing the prices of transport services.Π’Π°ΠΆΠ½Ρ‹ΠΌ аспСктом ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½ΠΈΡ эффСктивности процСссов управлСния Π½Π° транспортС являСтся Ρ€Π°Π·Π²ΠΈΡ‚ΠΈΠ΅ Π½ΠΎΠ²Ρ‹Ρ… ΠΏΠΎΠ΄Ρ…ΠΎΠ΄ΠΎΠ² ΠΊ Ρ„ΠΎΡ€ΠΌΠΈΡ€ΠΎΠ²Π°Π½ΠΈΡŽ ΠΌΠ΅Ρ…Π°Π½ΠΈΠ·ΠΌΠΎΠ² Π°Π½Π°Π»ΠΈΡ‚ΠΈΠΊΠΈ для Ρ†Π΅Π»Π΅ΠΉ управлСния Ρ†Π΅Π½Π°ΠΌΠΈ услуг.Π’ соврСмСнных Ρ€Ρ‹Π½ΠΎΡ‡Π½Ρ‹Ρ… условиях ΠΏΡ€ΠΎΠ΄ΠΎΠ»ΠΆΠ°ΡŽΡ‚ ΠΎΡΡ‚Π°Π²Π°Ρ‚ΡŒΡΡ Π°ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½Ρ‹ΠΌΠΈ ΠΈ вострСбованными исслСдования, Π½Π°ΠΏΡ€Π°Π²Π»Π΅Π½Π½Ρ‹Π΅ Π½Π° ΡΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Π½ΠΈΠ΅ инструмСнтария опрСдСлСния ΠΎΠΏΡ‚ΠΈΠΌΠ°Π»ΡŒΠ½Ρ‹Ρ… ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² ΡΠΎΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΡ качСства ΠΈ стоимости обслуТивания для формирования конкурСнтоспособной ΠΈ эффСктивной Ρ‚Π°Ρ€ΠΈΡ„Π½ΠΎΠΉ ΠΏΠΎΠ»ΠΈΡ‚ΠΈΠΊΠΈ.ЦСль исслСдования, прСдставлСнного Π² ΡΡ‚Π°Ρ‚ΡŒΠ΅, – Π°Π½Π°Π»ΠΈΠ· ΠΈ ΠΎΡ†Π΅Π½ΠΊΠ° пСрспСктив Ρ€Π΅Π°Π»ΠΈΠ·Π°Ρ†ΠΈΠΈ Ρ‚Π°ΠΊΠΈΡ… Π½Π°ΠΏΡ€Π°Π²Π»Π΅Π½ΠΈΠΉ ΠΏΠΎ ΡΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Π½ΠΈΡŽ Π°ΠΏΠΏΠ°Ρ€Π°Ρ‚Π° ΠΎΡ†Π΅Π½ΠΊΠΈ Ρ†Π΅Π½ΠΎΠ²ΠΎΠΉ эластичности спроса Π½Π° услуги ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡ€ΠΎΠΆΠ½ΠΎΠ³ΠΎ пассаТирского транспорта, ΠΊΠ°ΠΊ ΠΏΠ΅Ρ€Π΅Ρ…ΠΎΠ΄ ΠΊ использованию Π½Π΅Π»ΠΈΠ½Π΅ΠΉΠ½Ρ‹Ρ… ΠΏΠΎ ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Π°ΠΌ Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΉ модСлирования повСдСния ΠΊΠ»ΠΈΠ΅Π½Ρ‚ΠΎΠ², Π° Ρ‚Π°ΠΊΠΆΠ΅ Π²Π½Π΅Π΄Ρ€Π΅Π½ΠΈΠ΅ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ эффСктивных Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΠΎΠ² ΠΈΠ· арсСнала соврСмСнного инструмСнтария глобальной матСматичСской ΠΎΠΏΡ‚ΠΈΠΌΠΈΠ·Π°Ρ†ΠΈΠΈ.Π€ΠΎΡ€ΠΌΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅ Π²Ρ‹Π²ΠΎΠ΄ΠΎΠ² исслСдования основываСтся Π½Π° ΠΏΡ€ΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠΈ ΠΌΠ΅Ρ…Π°Π½ΠΈΠ·ΠΌΠΎΠ² систСмного Π°Π½Π°Π»ΠΈΠ·Π°, ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² экономико-матСматичСского модСлирования ΠΈ ΠΎΠΏΡ‚ΠΈΠΌΠΈΠ·Π°Ρ†ΠΈΠΈ, Π° Ρ‚Π°ΠΊΠΆΠ΅ инструмСнтария нСпарамСтричСской статистики.Π’ ΠΈΡ‚ΠΎΠ³Π΅, Π½Π° основС использования массива Π΄Π°Π½Π½Ρ‹Ρ… ΠΎ спросС пассаТиров Ρ„ΠΈΡ€ΠΌΠ΅Π½Π½Ρ‹Ρ… ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² ΠΏΡ€ΠΎΠ²Π΅Π΄Π΅Π½Π° ΡΡ€Π°Π²Π½ΠΈΡ‚Π΅Π»ΡŒΠ½Π°Ρ ΠΎΡ†Π΅Π½ΠΊΠ° качСства модСлирования Ρ†Π΅Π½ΠΎΠ²ΠΎΠΉ эластичности спроса ΠΏΡ€ΠΈ использовании 15 Π½Π΅Π»ΠΈΠ½Π΅ΠΉΠ½Ρ‹Ρ… ΠΏΠΎ ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Π°ΠΌ Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΉ, Π° Ρ‚Π°ΠΊΠΆΠ΅, Π² Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Π΅ осущСствлСния трёхэтапной ΠΏΡ€ΠΎΡ†Π΅Π΄ΡƒΡ€Ρ‹ ΡΡ€Π°Π²Π½ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° эффСктивности Ρ€Π°Π±ΠΎΡ‚Ρ‹ Π±ΠΎΠ»Π΅Π΅ Ρ‡Π΅ΠΌ 60 Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΠΎΠ² ΠΎΠΏΡ‚ΠΈΠΌΠΈΠ·Π°Ρ†ΠΈΠΈ (Π²ΠΊΠ»ΡŽΡ‡Π°ΡŽΡ‰Π΅ΠΉ, Π² Ρ‚ΠΎΠΌ числС, расчёт ΠΌΠΈΠ½ΠΈΠΌΡƒΠΌΠΎΠ² ΠΈ ΠΌΠ΅Π΄ΠΈΠ°Π½ для сумм ΠΊΠ²Π°Π΄Ρ€Π°Ρ‚ΠΎΠ² ошибок модСлирования, бутстрСп-Π°Π½Π°Π»ΠΈΠ·, тСсты ΠšΡ€Π°ΡΠΊΠ΅Π»Π°β€“Π£ΠΎΠ»Π»Π΅ΡΠ° ΠΈ ΠœΠ°Π½Π½Π°β€“Π£ΠΈΡ‚Π½ΠΈ, Π° Ρ‚Π°ΠΊΠΆΠ΅ расчёт ΡΠΏΠ΅Ρ†ΠΈΠ°Π»ΡŒΠ½ΠΎ Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½Π½ΠΎΠΉ Π°Π²Ρ‚ΠΎΡ€Π°ΠΌΠΈ ΠΌΠ΅Ρ‚Ρ€ΠΈΠΊΠΈ ΠΎΡ†Π΅Π½ΠΊΠΈ стСпСни прСвосходства ΠΎΠ΄Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΠ° Π½Π°Π΄ Π΄Ρ€ΡƒΠ³ΠΈΠΌ Π² Ρ€Π°ΠΌΠΊΠ°Ρ… нСпарамСтричСского Π°Π½Π°Π»ΠΈΠ·Π°) ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½Ρ‹ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ пСрспСктивныС ΠΌΠ΅Ρ…Π°Π½ΠΈΠ·ΠΌΡ‹ поиска нСизвСстных ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² для Π½Π΅Π³Π»Π°Π΄ΠΊΠΈΡ… Π½Π΅Π»ΠΈΠ½Π΅ΠΉΠ½Ρ‹Ρ… Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΉ модСлирования повСдСния ΠΊΠ»ΠΈΠ΅Π½Ρ‚ΠΎΠ² ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡ€ΠΎΠΆΠ½ΠΎΠ³ΠΎ транспорта.ΠŸΡ€Π΅Π΄ΡΡ‚Π°Π²Π»ΡΠ΅Ρ‚ΡΡ, Ρ‡Ρ‚ΠΎ ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π½Ρ‹Π΅ Π²Ρ‹Π²ΠΎΠ΄Ρ‹ ΠΌΠΎΠ³ΡƒΡ‚ Π±Ρ‹Ρ‚ΡŒ ΡƒΡΠΏΠ΅ΡˆΠ½ΠΎ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Π½Ρ‹ ΠΈ ΠΏΡ€ΠΈΠΌΠ΅Π½ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎ ΠΊ Π΄Ρ€ΡƒΠ³ΠΈΠΌ Π²ΠΈΠ΄Π°ΠΌ транспорта ΠΏΡ€ΠΈ Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΈ ΠΈΠΌΠΈ Π°Π½Π°Π»ΠΎΠ³ΠΈΡ‡Π½Ρ‹Ρ… Π·Π°Π΄Π°Ρ‡ формирования эффСктивного инструмСнтария управлСния Ρ†Π΅Π½Π°ΠΌΠΈ транспортных услуг

    An application of improved salp swarm algorithm for optimal power flow solution considering stochastic solar power generation

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    This paper describes the use of an improved version of the Salp Swarm Algorithm, known as iSSA, to address Optimal Power Flow (OPF) issues in power system management. The iSSA is applied to OPF problems involving stochastic solar power generation, with the goal of optimizing control variables such as real power generation, voltage magnitude at generation buses, transformer tap settings, and reactive power compensation. The optimization aims to achieve three objectives: minimizing power loss, minimizing cost, and minimizing combined cost and emissions from power generation. The iSSA's performance was tested on a modified IEEE 30-bus system and compared to other recent algorithms, including SSA. The simulation results show that the iSSA outperformed all compared algorithms for all objective functions that have been derived in this study

    A Survey on Natural Inspired Computing (NIC): Algorithms and Challenges

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    Nature employs interactive images to incorporate end users2019; awareness and implication aptitude form inspirations into statistical/algorithmic information investigation procedures. Nature-inspired Computing (NIC) is an energetic research exploration field that has appliances in various areas, like as optimization, computational intelligence, evolutionary computation, multi-objective optimization, data mining, resource management, robotics, transportation and vehicle routing. The promising playing field of NIC focal point on managing substantial, assorted and self-motivated dimensions of information all the way through the incorporation of individual opinion by means of inspiration as well as communication methods in the study practices. In addition, it is the permutation of correlated study parts together with Bio-inspired computing, Artificial Intelligence and Machine learning that revolves efficient diagnostics interested in a competent pasture of study. This article intend at given that a summary of Nature-inspired Computing, its capacity and concepts and particulars the most significant scientific study algorithms in the field

    Synthesizing multi-layer perceptron network with ant lion biogeography-based dragonfly algorithm evolutionary strategy invasive weed and league champion optimization hybrid algorithms in predicting heating load in residential buildings

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    The significance of accurate heating load (HL) approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are formulated through synthesizing a multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), the dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis

    Distribution network reconfiguration considering DGs using a hybrid CS-GWO algorithm for power loss minimization and voltage profile enhancement

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    This paper presents an implementation of the hybrid Cuckoo search and Grey wolf (CS-GWO) optimization algorithm for solving the problem of distribution network reconfiguration (DNR) and optimal location and sizing of distributed generations (DGs) simultaneously in radial distribution systems (RDSs). This algorithm is being used significantly to minimize the system power loss, voltage deviation at load buses and improve the voltage profile. When solving the high-dimensional datasets optimization problem using the GWO algorithm, it simply falls into an optimum local region. To enhance and strengthen the GWO algorithm searchability, CS algorithm is integrated to update the best three candidate solutions. This hybrid CS-GWO algorithm has a more substantial search capability to simultaneously find optimal candidate solutions for problem. Furthermore, to validate the effectiveness and performances of the proposed hybrid CS-GWO algorithm is being tested and evaluated for standard IEEE 33-bus and 69-bus RDSs by considering different scenarios

    Optimal Design of Photovoltaic, Biomass, Fuel Cell, Hydrogen Tank Units and Electrolyzer Hybrid System for a Remote Area in Egypt

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    In this paper, a new isolated hybrid system is simulated and analyzed to obtain the optimal sizing and meet the electricity demand with cost improvement for servicing a small remote area with a peak load of 420 kW. The major configuration of this hybrid system is Photovoltaic (PV) modules, Biomass gasifier (BG), Electrolyzer units, Hydrogen Tank units (HT), and Fuel Cell (FC) system. A recent optimization algorithm, namely Mayfly Optimization Algorithm (MOA) is utilized to ensure that all load demand is met at the lowest energy cost (EC) and minimize the greenhouse gas (GHG) emissions of the proposed system. The MOA is selected as it collects the main merits of swarm intelligence and evolutionary algorithms; hence it has good convergence characteristics. To ensure the superiority of the selected MOA, the obtained results are compared with other well-known optimization algorithms, namely Sooty Tern Optimization Algorithm (STOA), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA). The results reveal that the suggested MOA achieves the best system design, achieving a stable convergence characteristic after 44 iterations. MOA yielded the best EC with 0.2106533 /kWh,thenetpresentcost(NPC)with6,170,134/kWh, the net present cost (NPC) with 6,170,134 , the loss of power supply probability (LPSP) with 0.05993%, and GHG with 792.534 t/y

    Optimal Design of Photovoltaic, Biomass, Fuel Cell, Hydrogen Tank units and Electrolyzer hybrid system for a remote area in Egypt

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    In this paper, a new isolated hybrid system is simulated and analyzed to obtain the optimal sizing and meet the electricity demand with cost improvement for servicing a small remote area with a peak load of 420 kW. The major configuration of this hybrid system is Photovoltaic (PV) modules, Biomass gasifier (BG), Electrolyzer units, Hydrogen Tank units (HT), and Fuel Cell (FC) system. A recent optimization algorithm, namely Mayfly Optimization Algorithm (MOA) is utilized to ensure that all load demand is met at the lowest energy cost (EC) and minimize the greenhouse gas (GHG) emissions of the proposed system. The MOA is selected as it collects the main merits of swarm intelligence and evolutionary algorithms; hence it has good convergence characteristics. To ensure the superiority of the selected MOA, the obtained results are compared with other well-known optimization algorithms, namely Sooty Tern Optimization Algorithm (STOA), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA). The results reveal that the suggested MOA achieves the best system design, achieving a stable convergence characteristic after 44 iterations. MOA yielded the best EC with 0.2106533 /kWh,thenetpresentcost(NPC)with6,170,134/kWh, the net present cost (NPC) with 6,170,134 , the loss of power supply probability (LPSP) with 0.05993%, and GHG with 792.534 t/y
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