Wyniki wyszukiwania - BazTech

1

Optymalne planowanie budowlanego, wieloobiektowego przedsięwzięcia drogowego z użyciem algorytmu przeszukiwania genetycznego

Podolski Michał

Przegląd Budowlany

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2025

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R. 96, nr 1

112--116

PL

W artykule przedstawiono zagadnienie harmonogramowania budowlanego, wieloobiektowego przedsięwzięcia drogowego. Podczas wykonywania robót w takich przedsięwzięciach występują możliwości częściowego zazębiania się kolejnych czynności w obiektach. Ze względu na potrzebę maksymalnego skrócenia czasu zajęcia pracami budowlanymi poszczególnych obiektów zakłada się w nich ciągłość wykonywania robót. Założenia te prowadzą do zadania optymalizacyjnego polegającego na poszukiwaniu optymalnej kolejności wykonywania obiektów, która minimalizuje czas trwania przedsięwzięcia. W artykule to zagadnienie z powodzeniem rozwiązano za pomocą algorytmu przeszukiwania genetycznego i zilustrowano przykładem praktycznym.

EN

The article presents the issue of scheduling a multiunit road construction project. During the execution of works in such projects, there is a possibility of partial overlapping of successive activities in the units. Due to the need to maximally shorten the time of occupancy with construction works of the units, continuity of the works is assumed in them. These assumptions lead to the optimization task consisting in finding the optimal order of execution of the units that minimizes the duration of the project. In the article, this issue was successfully solved using a genetic search algorithm and illustrated by a case study.

2

Zastosowanie algorytmu genetycznego do planowania przepływu przedmiotów w złożonych systemach produkcyjnych

Jardzioch Andrzej, Jardzioch Bartosz

Zarządzanie Przedsiębiorstwem

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2024

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Vol. 27, nr 2

16--22

EN

Modern production systems are characterised by a high degree of complexity, resulting from the use of many different technological processes, the parallel production of complex products, the use of advanced numerically controlled (CNC) machine tools and complex transport systems. At the same time, it is necessary to take into account many variables and constraints such as the availability of machines, tools and workers, stock levels in the warehouse, forecasted product demand, material handling capacities and the sequence in which individual tasks must be performed. Effective planning of the flow of items in such systems is key to achieving high productivity, minimising costs and ensuring on-time delivery. Traditional planning methods often prove insufficient in the face of dynamic and unpredictable production conditions. In this context, genetic algorithms (AG) represent a promising tool for optimising production processes. This paper presents an example of the application of a genetic algorithm to optimise the production process in an exemplary robotic production system. As the main optimisation criterion, the total sum of delays to be reckoned with when accepting a defined set of orders for execution. In addition, the total execution time for the set of orders and the machine tool utilisation rates during the entire production process were analysed. In order to be able to apply the genetic algorithm, it was necessary to build a parametric simulation model and integrate this model with the developed genetic algorithm. The simulation model was used to determine the objective function in the optimisation process implemented by the genetic algorithm.

3

Analiza wybranych metod walidacji krzyżowej w programie RSES

Kołpacki Radosław

Studia i Materiały Informatyki Stosowanej

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2024

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T. 16, nr 1

5--8

PL

W artykule przeprowadzono analizę zbioru danych za pomocą dwóch metod walidacji krzyżowej. Wykorzystano program RSES do identyfikacji kluczowych właściwości i relacji w zbiorze. Wyniki wykazują wpływ niektórych parametrów na potencjalną dokładność wyników.

EN

This article presents an analysis of a dataset using two cross-validation methods. The RSES program was employed to identify key properties and relationships within the dataset. The results indicate the impact of certain parameters on the potential accuracy of the outcomes.

4

Analiza wybranych metod walidacji krzyżowej w programie RSES

Bethke Beata

Studia i Materiały Informatyki Stosowanej

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2024

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T. 16, nr 1

11--14

PL

W artykule przeprowadzono analizę zbioru danych za pomocą dwóch metod walidacji krzyżowej. Wykorzystano program RSES do identyfikacji kluczowych właściwości i relacji w zbiorze. Wyniki wykazują wpływ niektórych parametrów na potencjalną dokładność wyników.

EN

This article presents an analysis of a dataset using two cross-validation methods. The RSES program was employed to identify key properties and relationships within the dataset. The results indicate the impact of certain parameters on the potential accuracy of the outcomes.

5

Solving the economic dispatch by new hybrid algorithm

Naama Bakhta, Dahmani Kaouther, Abrouche Amel, Bouzeboudja Hamid

Przegląd Elektrotechniczny

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2024

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R. 100, nr 5

164--169

EN

The problem of economic dispatch is the minimization of the total cost of production by satisfying the demand of the load. The resolution of this problem is a way of managing an electricity production system taking into account the constraints of equalities and inequalities, in other words it is to find the optimal production for a given combination of units in operation. The appearance of meta-heuristic methods which are part of artificial intelligence, has effectively contributed to solving this problem. Bee colony optimization is a very recent family of meta-heuristics. Its principle is based on the behavior of real bees in life. Bees have properties that are quite different from those of other insect species. They live in colonies, building their nests in tree trunks or other similar enclosed spaces. In this paper, we will apply the optimization by colony of bees in test systems of different sizes with the aim of minimizing the cost of production of electrical energy by taking into account the effect of the valve points of the power plants. In order to see the effectiveness of the proposed algorithm, it has been compared with other algorithms in the literature.

PL

Problem ekonomicznej wysyłki polega na minimalizacji całkowitego kosztu produkcji poprzez zaspokojenie zapotrzebowania na ładunek. Rozwiązanie tego problemu to sposób zarządzania systemem wytwarzania energii elektrycznej z uwzględnieniem ograniczeń równości i nierówności, czyli znalezienie optymalnej produkcji dla danej kombinacji pracujących jednostek. Pojawienie się metod metaheurystycznych wchodzących w skład sztucznej inteligencji skutecznie przyczyniło się do rozwiązania tego problemu. Optymalizacja kolonii pszczół to bardzo nowa rodzina metaheurystyk. Jego zasada opiera się na zachowaniu prawdziwych pszczół w życiu. Pszczoły mają właściwości zupełnie odmienne od właściwości innych gatunków owadów. Żyją w koloniach, budując gniazda w pniach drzew lub innych podobnych zamkniętych przestrzeniach. W tym artykule zastosujemy optymalizację przez rodzinę pszczół w układach testowych różnej wielkości w celu minimalizacji kosztów produkcji energii elektrycznej poprzez uwzględnienie wpływu punktów zaworowych elektrowni. Aby sprawdzić skuteczność zaproponowanego algorytmu, porównano go z innymi algorytmami dostępnymi w literaturze.

6

Optimizing SiGe-on-annealed DPSi heterostructures using raman spectroscopy and genetic algorithm for enhanced material characterization and performance

Gouder Soraya, Yousfi Laatra, Guiza Dhaouadi, Mahamdi Ramdane, Berbezier Isabelle

Przegląd Elektrotechniczny

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2024

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R. 100, nr 4

165--170

EN

In our preceding investigation, we delved into the intricacies of SiGe alloys on double porous silicon (DPSi) through Raman spectroscopy, uncovering previously unknown connections between Raman peak shifts, stresses, and the concentration of Ge in the SiGe alloys in porous materials.A standout feature of this study lies in its distinct approach — a comparison of results employing a genetic algorithm. This method offers a comprehensive analysis of the data, enhancing our understanding of the intricate relationships at play. Validated through the frequency method, our results yield valuable insights into epitaxial growth on DPSi, presenting a nuanced perspective on the intricate interplay between Raman spectroscopy, stress, and alloy composition. These findings not only contribute to the evolving understanding of SiGe alloys but also pave the way for further advancements in the field of epitaxial growth on innovative substrates like DPSi.

PL

W naszym poprzednim badaniu zagłębiliśmy się w zawiłości stopów SiGe na podwójnie porowatym krzemie (DPSi) za pomocą spektroskopii Ramana, odkrywając nieznane wcześniej powiązania między przesunięciami pików Ramana, naprężeniami i stężeniem Ge w stopach SiGe w materiałach porowatych. Cechą tego badania jest odrębność podejścia — porównanie wyników z wykorzystaniem algorytmu genetycznego. Metoda ta umożliwia wszechstronną analizę danych, co pozwala lepiej zrozumieć złożone zależności. Nasze wyniki, potwierdzone metodą częstotliwości, dostarczają cennych informacji na temat wzrostu epitaksjalnego na DPSi, prezentując zniuansowaną perspektywę na skomplikowane wzajemne oddziaływanie między spektroskopią Ramana, naprężeniem i składem stopu. Odkrycia te nie tylko przyczyniają się do lepszego zrozumienia stopów SiGe, ale także torują drogę do dalszych postępów w dziedzinie wzrostu epitaksjalnego na innowacyjnych podłożach, takich jak DPSi.

7

Optimal distribution of reactive power by hybrid metaheuristic methods applied to the west Algerian network

Cherki Imene, Khalfallah Naima, Chaker Abdelkader

Przegląd Elektrotechniczny

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2024

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R. 100, nr 8

170--174

EN

This research focuses on the utilization of artificial intelligence through the sequential and integrated crossover of two population metaheuristic methods: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). These methods are applied to solve the Optimal Reactive Power Flow (ORPF) in the West Algerian network, comprising 102 nodes. The objective of this combination is to demonstrate its impact compared to non-hybrid metaheuristic methods in reducing energy losses while effectively improving various aspects such as voltage levels, the flow of active and reactive energy in the lines, transformation ratios of transformers, and the execution time of the process. Following this application, a comparative study of the results from different methods was conducted.

PL

Niniejsze badania koncentrują się na wykorzystaniu sztucznej inteligencji poprzez sekwencyjne i zintegrowane krzyżowanie dwóch metod metaheurystycznych populacji: algorytmu genetycznego (GA) i optymalizacji roju cząstek (PSO). Metody te są stosowane do rozwiązania optymalnego przepływu mocy biernej (ORPF) w sieci zachodnioalgierskiej, obejmującej 102 węzły. Celem tej kombinacji jest wykazanie jej wpływu w porównaniu z niehybrydowymi metodami metaheurystycznymi na redukcję strat energii przy jednoczesnej skutecznej poprawie różnych aspektów, takich jak poziomy napięcia, przepływ energii czynnej i biernej w liniach, współczynniki transformacji transformatorów i czas realizacji procesu. Po tej aplikacji przeprowadzono badanie porównawcze wyników różnych metod.

8

Dynamic scheduling strategy and algorithm for mixed batch scheduling in vacuum freeze-dried fruit processes

Huang JinDian, Hong YingHan, Lin KeYe

Production Engineering Archives

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2024

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Vol. 30, Iss. 4

477--490

EN

Vacuum freeze-dried fruit processes consisting of heating and holding are modelled as a mixed batch scheduling with the objective of minimizing the makespan. The jobs differ from each other in job family, size, weight and ready time. The batch processing time is determined by the longest job and the total weight of the jobs in the batch. A mixedinteger linear programming model is developed and tested with small-scale examples. Typical batch scheduling strategies are analysed and a machine based dynamic programming strategy is proposed. The machine-based dynamic scheduling strategy is applied to design improved genetic and particle swarm optimization algorithms, which demonstrate the effectiveness of this strategy. The worst-case ratio of the algorithms using machine dynamic programming strategy are proved. Numerical experiments show that the heuristic algorithm, genetic algorithm, and particle swarm optimization algorithm based on machine dynamic scheduling strategy outperform related algorithms using greedy and job-based dynamic scheduling strategies.

9

Emergency response capability evaluation of power transmission and transformation projects based on projection pursuit method

Shuo Li, Xin Fu, Yuanyuan He

Journal of Power Technologies

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2024

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Vol. 104, nr 2

116--129

EN

To adapt to the rapid development of power transmission and transformation projects, improve their emergency response capability level, and reduce the losses caused by accidents, the projection pursuit method was introduced into the emergency response capability evaluation of power transmission and transformation projects. The emergency response capability evaluation system of power transmission and transformation projects has been established mostly from each composition and structure of power transmission and transformation engineering systems, and highly subjective evaluation methods have been adopted to assess the models established. In this study, a total of 19 concrete indexes were selected from 4 aspects-monitoring and early warning capability, emergency control capability, emergency rescue and disposal capability, and emergency support capability-to establish an emergency response capability evaluation index system of power transmission and transformation projects. Then, an emergency response capability evaluation model for power transmission and transformation projects was constructed based on the projection pursuit model, followed by optimization using real code accelerated genetic algorithm (RAGA); for high-dimensional data, this model could directly find the structure and features of data itself, avoiding the limitations of subjective judgment and contributing to more truthful and reliable evaluation results; finally, this model was used to evaluate and analyze the emergency response capability of six power transmission and transformation projects: GZXS S00kV, JXXYS00kV, QHXN 750kV, YNZT S00kV, JSNJ S00kV, and SXXA 750kV. The results show that the six power transmission and transformation projects are different in the emergency response capability level; the emergency response capability level of power transmission and transformation projects is greatly affected by the early warning personnel deployment capability, daily emergency drill capability, emergency technology implementation capability, emergency training and education capability, and risk response capability.

10

Genetic algorithm-based decision tree optimization for detection of dementia through MRI analysis

Anuradha Govada, Davu Harini, Karri Muthyalanaidu

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2024

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T. 14, nr 1

83--89

EN

Dementia is a devastating neurological disorder that affects millions of people globally, causing progressive decline in cognitive function and daily living activities. Early and precise detection of dementia is critical for optimal dementia therapy and management however, the diagnosis of dementia is often challenging due to the complexity of the disease and the wide range of symptoms that patients may exhibit. Machine learning approaches are becoming progressively more prevalent in the realm of image processing, particularly for disease prediction. These algorithms can learn to recognize distinctive characteristics and patterns that are suggestive of specific diseases by analyzing images from multiple medical imaging modalities. This paper aims to develop and optimize a decision tree algorithm for dementia detection using the OASIS dataset, which comprises a large collection of MRI images and associated clinical data. This approach involves using a genetic algorithm to optimize the decision tree model for maximum accuracy and effectiveness. The ultimate goal of the paper is to develop an effective, non-invasive diagnostic tool for early and accurate detection of dementia. The GA-based decision tree, as proposed, exhibits strong performance compared to alternative models, boasting an impressive accuracy rate of 96.67% according to experimental results.

PL

Demencja jest wyniszczającym zaburzeniem neurologicznym, które dotyka miliony ludzi na całym świecie, powodując postępujący spadek funkcji poznawczych i codziennych czynności życiowych. Wczesne i precyzyjne wykrywanie demencji ma kluczowe znaczenie dla optymalnej terapii i zarządzania demencją, jednak diagnoza demencji jest często trudna ze względu na złożoność choroby i szeroki zakres objawów, które mogą wykazywać pacjenci. Podejścia oparte na uczeniu maszynowym stają się coraz bardziej powszechne w dziedzinie przetwarzania obrazu, szczególnie w zakresie przewidywania chorób. Algorytmy te mogą nauczyć się rozpoznawać charakterystyczne cechy i wzorce, które sugerują określone choroby, analizując obrazy z wielu modalności obrazowania medycznego. Niniejszy artykuł ma na celu opracowanie i optymalizację algorytmu drzewa decyzyjnego do wykrywania demencji przy użyciu zbioru danych OASIS, który obejmuje duży zbiór obrazów MRI i powiązanych danych klinicznych. Podejście to obejmuje wykorzystanie algorytmu genetycznego do optymalizacji modelu drzewa decyzyjnego w celu uzyskania maksymalnej dokładności i skuteczności. Ostatecznym celem artykułu jest opracowanie skutecznego, nieinwazyjnego narzędzia diagnostycznego do wczesnego i dokładnego wykrywania demencji. Zaproponowane drzewo decyzyjne oparte na GA wykazuje wysoką wydajność w porównaniu z alternatywnymi modelami, szczycąc się imponującym współczynnikiem dokładności wynoszącym 96,67% zgodnie z wynikami eksperymentalnymi.

11

The solution of MRSLP with the use of heuristic algorithms

Kochańska Joanna, Burduk Anna, Łapczyńska Dagmara, Musiał Kamil

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2024

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Vol. 72, nr 1

art. no. e146407

EN

Improving production processes includes not only activities concerning manufacturing itself, but also all the activities that are necessary to achieve the main objectives. One such activity is transport, which, although a source of waste in terms of adding value to the product, is essential to the realization of the production process. Over the years, many methods have been developed to help manage supply and transport in such a way as to reduce it to the necessary minimum. In the paper, the problem of delivering components to a production area using trains and appropriately laid-out carriages was described. It is a milk run stop locations problem (MRSLP), whose proposed solution is based on the use of heuristic algorithms. Intelligent solutions are getting more and more popular in the industry because of the possible advantages they offer, especially those that include the possibility of finding an optimum local solution in a relatively short time and the prevention of human errors. In this paper, the applicability of three algorithms – tabu search, genetic algorithm, and simulated annealing – was explored.

12

Improved thermal management of lithium-based batteries employing genetic algorithm optimization

Yüksek Gökhan, Lale Timur

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2024

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Vol. 72, nr 6

art. no. e151378

EN

Lithium-based battery systems (LBS) are used in various applications, from the smallest electronic devices to power generation plants. LBS energy storage technology, which can offer high power and high energy density simultaneously, can respond to continuous energy needs and meet sudden power demands. The lifetime of LBSs, which are seen as a high-cost storage technology, depends on many parameters such as usage habits, temperature and charge rate. Since LBSs store energy electrochemically, they are seriously affected by temperature. High-temperature environments increase the thermal stress exerted on LBS and cause its chemical structure to deteriorate much faster. In addition, the fast charging feature of LBSs, which is generally presented as an advantage, increases the internal temperature of the cell and negatively affects the battery life. The proposed energy management approach ensures that the ambient temperature affects the charging speed of the battery and that the charging speed is adaptively updated continuously. So, the two parameters that harm battery health absorb each other, and the battery has a longer life. A new differential approach has been created for the proposed energy management system. The total amount of energy that can be withdrawn from LBS is increased by 14.18% as compared to the LBS controlled with the standard energy management system using the genetic algorithm optimized parameters. Thus the LBS replacement period is extended, providing both cost benefits and environmentally friendly management by LBSs turning into chemical waste distinctly later.

13

Construction scheduling optimization in the prefabricated buildings through multi-objective GA on the principle of sustainable development

Yang Chuanguang

Archives of Civil Engineering

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2024

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Vol. 70, nr 3

459--472

EN

To save resources and protect the environment to the maximum extent, green buildings came into being. Among them, prefabricated building is the only way for traditional buildings to transform into green buildings. The construction scheduling of traditional buildings is mostly focused on the control of on-site resources, which cannot scientifically and reasonably complete the construction goal of prefabricated building. In response to the above issues, a resource constrained scheduling model based on genetic algorithm is designed by sustainable development, and an improved non dominated sorting genetic algorithm with elite strategy is introduced. It is used to solve the time cost weight balance scheduling model and the low-cost low-carbon scheduling model. The research results indicated that this algorithm had a reverse generation distance value of 0.35 when evaluated 4000 times, and a super volume value of 0.43 when evaluated 10000 times. In the application of a certain affordable housing project, the resource constrained scheduling model based on genetic algorithm can shorten the assembly phase to 8 days, and the low-cost low-carbon scheduling model using proposed algorithm can reduce the transportation cost and carbon emission duration of transportation vehicles to 22501 yuan and 93.75 h, respectively. Resource constrained scheduling models based on genetic algorithms and low-cost low-carbon scheduling models have potential in the field of green buildings, which can achieve significant results in saving time, cost, and reducing carbon emissions. These research results can provide reference for the promotion and practice of green buildings, and guide the formulation and implementation of relevant policies.

14

Machinability investigation during turning of polyoxymethylene POM-C and optimization of cutting parameters using Pareto analysis, linear regression and genetic algorithm

Hakmi Tallal, Hamdi Amine, Touggui Youssef, Laouissi Aissa, Belhadi Salim, Yallese Mohamed A.

Archive of Mechanical Engineering

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2024

|

LXXI, nr 1

47--71

EN

This paper presents a study on the dry turning of polyoxymethylene copolymer POM-C. The effect of five factors (cutting speed, feed rate, depth of cut, nose radius, and main cutting edge angle) on machinability is evaluated using four output parameters: surface roughness, tangential force, cutting power, and material removal rate. To do so, the study relies on three approaches: (i) Pareto statistical analysis, (ii) multiple linear regression modeling, and (iii) optimization using the genetic algorithm. To conduct the investigation, mathematical models are developed using response surface methodology based on the Taguchi 𝐿16 orthogonal array. The results indicate that feed rate, nose radius, and cutting edge angle significantly influence surface quality, while depth of cut, feed, and speed have a notable impact on other machinability parameters. The developed mathematical models have determination coefficients greater than or very close to 95%, making them very useful for the industry as they allow predicting response values based on the chosen cutting parameters. Finally, the optimization using the genetic algorithm proves to be promising and effective in determining the optimal cutting parameters to maximize productivity while improving surface quality.

15

Testing algorithms for quick rescheduling flow shop problems with FlexSim based simulation and R engine

Janke Piotr

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2023

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z. 168

163--175

EN

Purpose: The aim of this paper is to present a combination of advanced algorithms for finding optimal solutions together with their tests for a permutation flow-shop problem with the possibilities offered by a simulation environment. Four time-constrained algorithms are tested and compared for a specific problem. Design/methodology/approach: Four time-constrained algorithms are tested and compared for a specific problem. The results of the work realisation of the algorithms are transferred to a simulation environment. The entire solution proposed in the work is composed as a parallel environment to the real implementation of the production process. Findings: The genetic algorithm generated the best solution in the same specified short time. By implementing the adopted approach, the correct cooperation of the FlexSim simulation environment with the R language engine was obtained. Research limitations/implications: The genetic algorithm generated the best solution in the same specified short time. By implementing the approach, a correct interaction between the FlexSim simulation environment and the R language engine was achieved. Practical implications: The solution proposed in this paper can be used as an environment to test solutions proposed in production. Simulation methods in the areas of logistics and production have for years attracted the interest of the scientific community and the wider industry. Combining the achievements of science in solving computationally complex problems with increasingly sophisticated algorithms, including artificial intelligence algorithms, with simulation methods that allow a detailed overview of the consequences of changes made seems promising. Originality/value: The original concept of cooperation between the R environment and the FlexSim simulation software for a specific problem was presented.

16

Calculation strength optimum of surgical robot effector for mechanical eigenproblems using FEM and genetic algorithm

Ilewicz Grzegorz

Vibrations in Physical Systems

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2023

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Vol. 34, nr 1

art. no. 2023106

EN

It is essential to check whether the surgical robot end effector is safe to use due to phenomena such as linear buckling and mechanical resonance. The aim of this research is to build an multi criteria optimization model based on such criteria as the first natural frequency, buckling factor and mass, with the assumption of the basic constraint in the form of a safety factor. The calculations are performed for a serial structure of surgical robot end effector with six degrees of freedom ended with a scalpel. The calculation model is obtained using the finite element method. The issue of multi-criteria optimization is solved based on the response surface method, Pareto fronts and the genetic algorithm. The results section illustrates deformations of a surgical robot end effector occurring during the resonance phenomenon and the buckling deformations for subsequent values of the buckling coefficients. The dependencies of the geometrical dimensions on the criteria are illustrated with the continuous functions of the response surface, i.e. metamodels. Pareto fronts are illustrated, based on which the genetic algorithm finds the optimal quantities of the vector function. The conducted analyzes provide a basis for selecting surgical robot end effector drive systems from the point of view of their generated inputs.

17

Performances improvement of fractional order systems using the fractional order adaptive PID controller

Asradj Zahir, Bensafia Bouira, Merzouk Imad

Przegląd Elektrotechniczny

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2023

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R. 99, nr 8

257--260

EN

Fractional order systems are widely used in industrial application for its different advantage such us high efficiency, and flexibilities. The applications of fractional order systems in a range of scientific fields have caught the attention of researchers especially in control strategy. The current research work presents the use the fractional adaptive PID controller approach, optimized by genetic algorithm, to improve the performances (rise time, setting time and overshoot) for fractional systems by introducing fractional order integrator and differentiator in the classical feedback adaptive PID controller. To validate the arguments, effectiveness and performances analysis of the proposed approach optimized by genetic algorithm have been studied in comparison to the classical adaptive PID controller. Numerical simulation and analysis are presented to verify the best controller. The Fractional order PID gives the best result in terms of settling time, rise time, overshoot and mean absolute error.

PL

Systemy ułamkowego rzędu są szeroko stosowane w zastosowaniach przemysłowych ze względu na różne zalety, takie jak wysoka wydajność i elastyczność. Zastosowania systemów rzędu ułamkowego w wielu dziedzinach nauki przykuły uwagę badaczy, zwłaszcza w dziedzinie strategii sterowania. Obecna praca badawcza przedstawia wykorzystanie podejścia ułamkowego regulatora adaptacyjnego PID, zoptymalizowanego przez algorytm genetyczny, do poprawy osiągów (czas narastania, czas ustawiania i przeregulowanie) układów ułamkowych poprzez wprowadzenie integratora i układu różniczkowego ułamkowego rzędu do klasycznego regulatora PID z adaptacyjnym sprzężeniem zwrotnym. Aby zweryfikować argumenty, przeprowadzono analizę skuteczności i wydajności proponowanego podejścia zoptymalizowanego za pomocą algorytmu genetycznego w porównaniu z klasycznym adaptacyjnym regulatorem PID. Przedstawiono symulację i analizę numeryczną w celu weryfikacji najlepszego sterownika. PID rzędu ułamkowego daje najlepsze wyniki pod względem czasu ustalania, czasu narastania, przeregulowania i średniego błędu bezwzględnego.

18

Identyfikacja parametrów schematu zastępczego ogniw litowo-jonowych

Kaczmarek Wiktoria, Kasprzyk Leszek

Przegląd Elektrotechniczny

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2023

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R. 99, nr 12

296--299

PL

W artykule opisano zagadnienie identyfikacji parametrów modelu ogniw litowo-jonowych typu LFP. Przedstawiono model obwodowy 2 rzędu, służący do analizy sygnałów elektrycznych na zaciskach ogniwa. Przeprowadzono laboratoryjne badania eksperymentalne, które wykorzystano do identyfikacji. W celu uzyskania dopasowania modelu do przeprowadzonych pomiarów, opracowano autorską aplikację komputerową, która z wykorzystaniem algorytmu genetycznego znajduje optymalne rozwiązania w postaci zbioru parametrów elementów schematu zastępczego w funkcji stanu naładowania ogniwa. Uzyskane wyniki identyfikacji zwizualizowano oraz porównano do wyników uzyskanych podczas pomiarów.

EN

This paper presents the problem of identifying the parameters of the LFP-type lithium-ion cell model. A 2nd order circuit model is presented for the analysis of electrical signals at the terminals of the cell. Laboratory experimental tests were carried out and used for identification. In order to obtain a match between the model and the measurements, a proprietary computer application was developed, which, using a genetic algorithm, finds optimal solutions in the form of a set of parameters of the elements of the equivalent scheme as a function of the cell's state of charge. The identification results obtained were then visualized and compared to the measurements.

19

Vehicle routing problem simultaneous deliveries and pickups with split loads and time windows with genetic algorithm (case study in shipping company)

Ferdianti Shea Amanda, Widyadana I Gede Agus

LogForum

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2023

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Vol. 19, no. 4

577--593

EN

Background: This research addresses a Vehicle Routing Problem with Simultaneous Delivery and Pickup, Split Loads, and Time Windows (VRPSDPSLTW). In this research, the VRPSDPSLTW problem is adapted for Company X, a shipping company based in Surabaya. The main goal is to enhance the optimal utilization of vessel capacity in the field of shipping transportation and logistics. Little previous research has been done on VRPSDPSLTW at a shipping company. Methods: The optimization approach employed was the Genetic Algorithm (GA), which serves as a metaheuristic to effectively optimize vessel capacity utilization. The algorithm uses One Point Crossover and Swap Mutation operators and analyzes various mutation parameters to determine the best configuration. The GA was coded in R, and experiments were conducted to obtain the best parameter for the GA. Results: The research yielded several outcomes, including route plans, loaded and unloaded Twenty-Foot Equivalent Units (TEUs), travel times, and trip utility from the point of loading (POL) to the point of delivery (POD). In total, there were 85 port visits, surpassing the initial count of 35 ports. Some ports were visited multiple times, with the exception of Surabaya, which served as the home base for a fleet of 15 vessels. The average trip duration was approximately 35 days. Through experimentation, it was determined that employing 1,000 generations along with a mutation probability of 0.2 produces improved solutions. The Genetic Algorithm solution enhanced the average vessel capacity utilization, increasing it to 80.93%. This represents a significant 21.23% increase compared to the global average of 59.7% observed for similar vessel usage scenarios. Conclusions: Furthermore, through the introduction of novel route opportunities, the contributions of each vessel were effectively enhanced. This achievement resulted in an optimal average vessel capacity utilization that met the demand. The findings strongly advocate for the employment of the Genetic Algorithm, highlighting its potential to substantially improve vessel capacity utilization. Consequently, this approach has played a pivotal role in elevating the efficiency of transportation and logistics operations for Company X.

20

Conditions for increasing the recognition of degradation in thermal-flow diagnostics, taking into account environmental legal aspects

Drosińska-Komor Marta, Głuch Jerzy, Brzezińska-Gołębiewska Katarzyna, Piotrowicz Michał, Ziółkowski Paweł

Journal of Power Technologies

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2023

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Vol. 103, nr 1

33--48

EN

The ever-increasing demand for electricity and the need for conventional sources to cooperate with renewable ones generates the need to increase the efficiency and safety of the generation sources. Therefore, it is necessary to find a way to operate existing facilities more efficiently with full detection of emerging faults. These are the requirements of Polish, European and International law, which demands that energy facilities operate with the highest efficiency and meet a number of restrictive requirements. In order to improve the operation of steam power plants of electric generating stations, thermal-fluid diagnostics have been traditionally used, and in this paper a three-hull steam turbine, having a high-pressure, a medium-pressure and a low-pressure part, has been selected for analysis. The turbine class is of the order of 200 MW electric. Genetic algorithms (GA) were used in the process of creating the diagnostic model. So far, they have been used for diagnostic purposes in gas turbines, and no work has been found in the literature using GA for the diagnostic process of such complex objects as steam turbines located in professional manufacturing facilities. The use of genetic algorithms allowed rapid acquisition of global extremes, that is efficiency and power of the unit. The result of the work undertaken is the possibility to carry out a full diagnostic process, meaning detection, localization and identification of single and double degradations. In this way 100 % of the main faults are found, but there are sometimes additional ones, and these are not perfectly identified especially for single time detection. Thus, the results showed that with a very high success rate the simulated damage to the geometrical elements of the steam turbine under study is found.