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1

Technological considerations in wire electrical discharge machining of Hadfield’s casted steel

Poroś Dariusz

Zeszyty Naukowe Politechniki Morskiej w Szczecinie

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2025

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nr 83 (155)

122--132

EN

This study addresses the machining challenges of Hadfield steel by optimizing wire electrical discharge machining (WEDM) parameters through a robust, multimethod approach. The research niche lies in applying hybrid modelling and optimization strategies ‒ specifically combining statistical and soft computing techniques ‒ to enhance machinability of high-manganese steels. The main objective is to improve both material removal rate (MRR) and surface roughness (SR) through systematic parameter tuning. The methodology integrates Taguchi L27 orthogonal array, analysis of variance (ANOVA), and genetic algorithms (GAs) to analyze and optimize five key process parameters: tₒₙ (pulse-on time), tₒff (pulse-off time), gap voltage (Vg), wire feed rate (Wf), and dielectric pressure (Dp). Results revealed that tₒₙ, tₒff, and Vg significantly influenced MRR and SR, while Wf and Dp had negligible effects. The maximum MRR of 36.25 mm²/min (+249.57% from baseline) is achieved under optimal conditions (tₒₙ = 120 μs, tₒff = 30 μs, and Vg = 80 V). The lowest SR of 0.95 μm (46% improvement) is achieved at tₒₙ = 100 μs, tₒff = 40 μs, and Vg = 60 V. Multiobjective optimization using MATLAB’s fmincon solver and GA-based regression modeling yielded a balanced result (MRR = 14.92 mm²/min and SR = 1.76 μm). The SEM imaging and 3D surface topography analysis confirmed that higher SR correlated with craters, microcracks, and cavities due to thermal loading. This work highlights MATLAB’s fmincon optimization combined with genetic algorithm-based modeling as a powerful framework for process optimization, especially for complex geometries such as grooves and splines in Hadfield steel, and underscores both the potential and limitations of WEDM in machining hard-to-cut materials.

2

Optimizing DVA placement using evolutionary algorithms for dynamic beam loading

Podwórna Monika, Grosel Jacek

Studia Geotechnica et Mechanica

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2025

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Vol. 47, nr 2

167--178

EN

Danamic vibration absorbers (DVAs) are used to suppress the excessive structural response due to dynamic loading. To maximize their effectiveness, the placement and characteristics of DVAs need to be carefully chosen. To address this challenge, a methodology that enables this task to be accomplished by means of an evolutionary algorithm is presented in this paper. A beam subjected to a sequence of forces with random amplitudes, which move at random time instances with a constant velocity, is considered. The beam has either one or a set of two arbitrarily located DVAs. The loading is modeled using a filtered Poisson process, while the DVAs are modeled as single-degree-of-freedom (SDOF) systems. It is shown that the proposed algorithm can serve as a powerful tool when selecting an arrangement of DVAs, in turn effectively mitigating any undesired structural response. Moreover, the optimization of DVAs leads to the asymmetry of the absorber’s position along the length of a bridge’s beam. The obtained results can be used to evaluate the correctness of calculations conducted for the purpose of assessing structural damping requirements.

3

Zastosowanie metody GAES do wyznaczania wartości parametrów wielogałęziowych obwodów Cauera

Kurzawa Milena, Wojciechowski Rafał M., Idziak Paweł

Przegląd Elektrotechniczny

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2025

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R. 101, nr 3

276--280

PL

W artykule przedstawiono metodę łączącą elementy strategii ewolucji z algorytmem genetycznym (GAES), tj. metodę ukierunkowaną na poszukiwanie ekstremum nie tylko funkcji wielomodalnej, ale także funkcji wielu zmiennych. Opisano jej implementację w poszukiwaniu parametrów wielogałęziowych obwodów Cauera. Sprawdzono słuszność zaproponowanej metody oraz porównano uzyskane wyniki na podstawie metody GAES z wynikami uzyskanymi na podstawie modelu polowego.

EN

The article presents a method combining elements of evolution strategy with a genetic algorithm (GAES) for searching for the extremum not only of a multimodal function but also of a multivariable function. Its implementation in the search for parameters of multibranch Cauer circuits is described. The validity of the proposed method was verified, and the obtained results based on the GAES method were compared with the results obtained based on the field model.

4

Optymalizacja generatora do małej przydomowej elektrowni wiatrowej

Wróbel Krzysztof, Śliwiński Artur

Przegląd Elektrotechniczny

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2025

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R. 101, nr 2

120--124

PL

W artykule podjęto próbę optymalizacji konstrukcji małego generatora opartego na przełączalnej maszynie reluktancyjnej, wykorzystanego w małej przydomowej elektrowni wiatrowej. Cała konstrukcja nie przekracza swoimi gabarytami trzech metrów, co pozwala według obecnych przepisów na instalację takiej elektrowni wiatrowej bezpośrednio na budynku, bez konieczności uzyskania pozwolenia ani zgłoszenia budowy. W badaniach porównano wyniki uzyskane w czasie optymalizacji konstrukcji generatora i sposobu jego sterowania algorytmem genetycznym w wyniku zastosowania trzech różnych funkcji celu.

EN

The article attempts to optimize the design of a small generator based on a switched reluctance machine, used in a small home wind power plant. The entire structure does not exceed three meters in size, which allows, according to current regulations, the installation of such a wind power plant directly on a building, without the need to report the construction. The research compared the results obtained during the optimization of the generator design and the method of its control with a genetic algorithm as a result of using three different objective functions.

5

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.

6

Auto-tuning of a modified L1-adaptive controller with genetic algorithms for dynamic positioning of a remotely operated vehicle under marine currents

Sainz Jose Joaquin, Becerra Victor, Revestido-Herrero Elías, Llata Jose Ramon, Alonso-Rentería Luciano

Polish Maritime Research

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2025

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nr 2

115--123

EN

In this article, a modified L1-adaptive controller with auto-tuning using a genetic algorithm is presented for dynamic positioning of remotely operated vehicles (ROVs) under marine currents, based on a six-degree-of-freedom Nonlinear model of an ROV. To enable tuning of some of the parameters of the controller, a cost function related to the error of the steady state positions of the system is minimised with the use of the genetic algorithm. A series of simulations are conducted to ascertain the performance of the system with the implemented controller, taking into consideration the vehicle position, orientation, and control signals sent as commands to the thrusters. The simulations are carried out with noise levels representative of those encountered by the standard underwater instrumentation on an ROV, as well as with underwater current velocities. In addition, the results are compared with those of a classical controller to verify the improvements offered by the controller proposed in this paper.

7

Enhancing hyperparameters of LSTM network models through Genetic Algorithm for Virtual Learning Environment prediction

Ismanto Edi, Ghani Hadhrami Ab, Saleh Nurul Izrin Md

International Journal of Electronics and Telecommunications

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2025

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Vol. 71, No. 4

21

EN

In today's technology-driven era, innovative methods for predicting behaviors and patterns are crucial. Virtual Learning Environments (VLEs) represent a rich domain for exploration due to their abundant data and potential for enhancing learning experiences. Long Short-Term Memory (LSTM) models, while proficient with sequential data, face challenges such as overfitting and gradient issues. This study investigates the optimization of LSTM parameters and hyperparameters for VLE prediction. Adaptive gradient-based algorithms, including ADAM, NADAM, ADADELTA, ADAGRAD, and ADAMAX, exhibited superior performance. The LSTM model with ADADELTA achieved 91% accuracy for BBB course data, while ADAGRAD LSTM models attained average accuracies of 80% and 85% for DDD and FFF courses, respectively. Genetic algorithms for hyperparameter optimization significantly contributed, with the GA + LSTM + ADAGRAD model achieving 88% and 87% accuracy in the 7th and 9th models for BBB course data. The GA + LSTM + ADADELTA model produced average accuracy rates of 80% and 84% in DDD and FFF course data, with the highest accuracy rates of 86% and 93%, as well. These findings highlight the effectiveness of adaptive and genetic algorithms in enhancing LSTM model performance for VLE prediction, offering valuable insights for educational technology advancement.

8

Genetic Algorithm Approach to a Concurrent Real-Time Optimization Problem in the Embedded System Design Process

Górski Adam M., Ogorzałek Maciej

Foundations of Computing and Decision Sciences

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2025

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Vol. 50, No. 3

373--382

EN

In this paper, we present a genetic algorithm for a concurrent real-time optimization problem occurring in the embedded system design process. The problem consists of two concurrent phases, each impacting the other in real time. In the first phase, parameters are selected for optimization, and in the second, the parameters are optimized and their choice is validated in real time. During the implementation of the embedded system, unexpected situations can arise, each of which can be solved in many ways; each way, in turn, may require the execution of different unexpected tasks. However, identifying the optimal path to follow is significantly challenging. Furthermore, some of the proposed solutions to the problem may not yield appropriate results. The proposed algorithm generates a certain number of individuals and evolves them using genetic operators, performing the proper optimization and comparing the results.

9

Optimal coordination for directional overcurrent relays incorporating distribution generators: A comparative study

Abdullah Bassam Hamad, Alkhayyat Mahmood T.

Diagnostyka

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2025

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Vol. 26, No. 1

art. no. 2025111

EN

The integration of renewable energy-based distributed energy resources (DER) into distribution networks has increased due to rising load demand and growing concerns about global warming. The integration of DERs has transformed the operation of distribution networks from a passive to an active nature. As a result, a bidirectional flow of current occurs in the distribution networks. The protection of such systems is generally performed using directional overcurrent relays (DOCRs). However, optimal coordination of the DOCRs is necessary to ensure safe operation. Therefore, this paper aims to develop the optimal coordination of DOCRs using two nature-inspired techniques: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The developed algorithms are tested on IEEE 6-Bus and IEEE 15-Bus test systems in the MATLAB R2022b environment. To validate the effectiveness of the methods, the obtained results are compared with various upto-date algorithms. The comparison shows that the GA outperformed all the algorithms in minimizing the relay operation time for optimum coordination of overcurrent relays.

10

Task offloading in edge computing using integrated particle swarm optimization and genetic algorithm

Palaniappan Shabariram C., Ponnuswamy Priya P.

Advances in Science and Technology. Research Journal

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2025

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Vol. 19, no. 1

371--380

EN

n the ever-evolving landscape of smart city applications and Intelligent Transport Systems, Vehicular Edge Computing emerged as a game-changing technology. Imagine a world where computational resources are no longer restricted to distant cloud servers but are brought nearer to the vehicles and users. Task offloading enables the computation in edge and cloud server. This proximity not only minimizes network latency but also enables a unfold of vehicles to process tasks at the edge, offering a swift and interactive response to the scenarios of applications with delay sensitivity. To deal with this constraint, an integrated methodology is utilized to enhance the offloading process. The proposed system integrates the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The integrated system optimizes task allocation by exploring the solution space effectively and ensuring efficient resource utilization while minimizing latency. In the evaluation, PSO+GA exhibits enhanced adaptability to varying task sizes, facilitating efficient offloading to the edge as needed. Energy efficiency varies between the algorithms, with PSO+GA generally showing minimal energy consumption. When compared to already existing algorithms such as Energy aware offloading, no offloading and random offloading, PSO+GA outperformed these algorithms in system performance and less energy consumption by a factor of 1.18.

11

Optimization of gear pump operating parameters using genetic algorithms and performance analysis

Deptuła Adam, Osiński Piotr

Advances in Science and Technology. Research Journal

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2025

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Vol. 19, no. 3

211--227

EN

This study explores the optimization of operating parameters and the design of innovative gear pumps featuring multi-involute tooth profiles. Using a genetic algorithm, optimal parameters such as the basic radius, tooth height, and angles of involute profiles were determined. Results indicate that the proposed optimization framework significantly improves the volumetric and total efficiency of the pumps. Specifically, a volumetric efficiency increase of 8-12% and a reduction in noise levels by 3-5 dB were observed compared to conventional designs. The experimental validation confirmed the robustness of the proposed model, showcasing its potential for industrial applications. This work highlights the integration of advanced computational techniques with engineering design to achieve enhanced performance metrics.

12

3K method: time-optimal path planning for field robot

Kwaśniewski Konrad K., Gosiewski Zdzisław

Acta Mechanica et Automatica

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2025

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Vol. 19, no. 1

172--176

EN

In this study, a hybrid genetic-geometrical path finding method is presented. Its main feature is the division of the path-finding process into global and local path-finding to achieve a trajectory optimized under the shortest travel time condition in an environment filled with obstacles. To improve the reliability of the algorithm, a safety zone around obstacles is included. In this zone, the maximum velocity allowed for a robot is additionally limited to decrease the probability of collision due to noise in obstacle mapping, distraction from terrain irregularities or malfunction of the steering system. The simulation and real world experiment results are presented in another paper.

13

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.

14

Budynki wielofunkcyjne jako podstawowa jednostka kształtującego się środowiska mieszkaniowego inteligentnych miast przyszłości

Gerigk Mateusz

Środowisko Mieszkaniowe

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2024

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nr 47

55--66

PL

Współczesne podejście do tworzenia funkcji mieszkaniowej skonfrontowane zostało z trendem wzrostu kubatury budynków oraz oczekiwaniami w odniesieniu do przyszłego otoczenia urbanistycznego ukierunkowanego na zrównoważony rozwój. Artykuł przedstawia ujęcie struktury mieszkaniowej w kontekście zdefiniowanych zakresów tematycznych. Mianowicie jest to systemowe podejście do problemu projektowego struktur wielofunkcyjnych kreujących współczesną tkankę mieszkaniową w rozwijających się ośrodkach miejskich. Tworzenie miast inteligentnych z istniejących ośrodków miejskich oraz nowoprojektowanych ośrodków przewiduje kompleksowo dobrane wytyczne projektowe. Kluczowa jest integralność a zarazem interoperacyjność struktury dynamicznej, która może stanowić podstawę do rozwijania nowych oraz ulepszania istniejących systemów. Opracowana modelowa struktura tworzenia i podtrzymania systemu opracowana jest w oparciu o algorytm genetyczny i została przedstawiona w postaci sieci neuronowej, która uwzględnia zastosowanie sztucznej inteligencji (SI). Określona struktura przewidziana jest jako narzędzie wspomagające nadzór i decyzyjność w procesie projektowania i zarządzania współczesnym budynkiem wielofunkcyjnym w jego nowo planowanym otoczeniu.

EN

The contemporary approach to creating the residential function is confronted with the trend of increasing the volume of buildings and expectations regarding the future urban environment focused on sustainable development. This paper presents an overview of the residential structure in the context of defined thematic scopes. Namely, it is a systemic approach to the problem of designing mixed-use buildings which create a modern residential structure in developing urban centres. The creation of smart cities from existing urban areas and newly designed centres involves comprehensively defined design guidelines. The key is the integrity and interoperability of the dynamic structure which can serve as a basis for developing new systems and/or improving the existing ones. The developed model structure for creating and maintaining the system is based on a genetic algorithm and is presented in the form of a neural network that involves the use of artificial intelligence(AI). The specific structure is intended as a tool to support supervision and decision-making in the process of designing and managing contemporary mixed-use buildings in their newly planned surroundings.

15

Scheduling using Convolutional Neural Network in GPU environment

Świtalski Piotr, Siwiak Karolina

Studia Informatica : systems and information technology

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2024

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vol. 1 (30)

81--96

EN

Graphics processing units (GPU) have become the foundation of artificial intelligence. Machine learning was slow, inaccurate, and inadequate for many of today’s applications. The inclusion and utilization of GPUs made a remarkable difference in large neural networks. The numerous core processors on a GPU allow machine learning engineers to train complex models using many files relatively quickly. The ability to rapidly perform multiple computations in parallel is what makes them so effective; with a powerful processor, the model can make statistical predictions about very large amounts of data. GPUs are widely used in machine learning because they offer more power and speed than CPUs. In this paper, we show the use of GPU for solving a scheduling problem. The results show that this idea is useful, especially for large optimization problems.

16

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.

17

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.

18

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.

19

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.

20

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.