Wyniki wyszukiwania - BazTech

1

Minimalizacja asymetrii w sieci niskiego napięcia zawierającej mikroinstalacje fotowoltaiczne oraz magazyny energii

Pijarski Paweł, Przepiórka Dominik, Binkowski Tomasz

Rynek Energii

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2025

|

Nr 5

3--9

PL

W artykule przedstawiono analizy dotyczące asymetrii napięć i prądów w sieci niskiego napięcia, wynikające z nierównomiernego rozmieszczenia odbiorników jednofazowych oraz mikroinstalacji fotowoltaicznych. Zastosowanie pięciu różnych, metaheurystycznych algorytmów optymalizacyjnych pozwoliło na minimalizację asymetrii poprzez optymalne sterowanie mocą czynną i bierną w poszczególnych fazach. Wyniki symulacji, przeprowadzonych na zmodyfikowanej sieci testowej IEEE ELVTF, potwierdziły efektywność algorytmów optymalizacyjnych oraz znaczenie asymetrycznej regulacji. Zaproponowane podejście może przyczynić się do poprawy jakości zasilania w warunkach rosnącej liczby odnawialnych źródeł energii.

EN

The paper presents an analysis of voltage and current asymmetry in low-voltage networks, caused by the uneven distribution of single-phase loads and photovoltaic micro-installations. The application of ﬁve different metaheuristic optimization algorithms enabled the minimization of asymmetry through optimal control of active and reactive power in individual phases. Simulation results, carried out using a modified IEEE ELVTF test network, conﬁrmed the effectiveness of the optimization algorithms and the importance of asymmetric regulation. The proposed approach may contribute to improving power quality under the growing presence of renewable energy sources.

2

Optimisation of low voltage network operation - selected power quality indicators vs. metaheuristic optimisation algorithms

Pijarski Paweł, Przepiórka Dominik, Malska Wiesława

Przegląd Elektrotechniczny

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2025

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R. 101, nr 6

63--68

EN

The article analyses selected power quality indicators in a low-voltage network containing RES sources and energy storage. Their optimal values were determined using ten different metaheuristic optimisation methods. The proposed analyses will enable the selection of the most beneficial indicator from the point of view of managing the operation of the distribution network through optimal control of the charging power of energy storage and the reactive power of prosumer photovoltaic installations. This will also reduce power losses and equalise voltage profiles.

PL

W artykule przeanalizowano wybrane wskaźniki jakości zasilania w sieci niskiego napięcia zawierającej źródła OZE oraz magazyny energii. Wyznaczono ich optymalne wartości przy wykorzystaniu dziesięciu różnych metaheurystycznych metod optymalizacji. Proponowane analizy umożliwią wybór najbardziej korzystnego wskaźnika z punktu widzenia zarządzania pracą sieci dystrybucyjnej poprzez optymalne sterowanie mocą ładowania magazynów energii oraz mocą bierną prosumenckich instalacji fotowoltaicznych. Pozwoli to również ograniczyć straty mocy i wyrównać profile napięcia.

3

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

Podolski Michał

Przegląd Budowlany

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2025

|

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.

4

The use of artificial intelligence in building information modelling: document-based bibliographic analysis

Zima Krzysztof

Budownictwo o Zoptymalizowanym Potencjale Energetycznym

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2025

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Vol. 14

86--94

EN

A strong connection to the architectural, engineering and construction network, with access to technologies such as BIM, leads to increased industrial productivity. The use of BIM can be implemented by using many programs and systems that provide knowledge and quick solutions. Combining BIM with artificial intelligence (AI) methods can provide even greater software benefits. This article examines the use of AI methods coupled with BIM in scientific research. The main objective of the research is to learn about trends in the use of artificial intelligence in BIM research in the context of construction management. This was achieved through bibliometric and scientifically accessible searches and mapping with text mining. Data on artificial intelligence in BIM research has been collected by reviewing and using articles from the Scopus database. The paper will contribute to the access of literature and trends in AI and BIM research.

5

Smart Routes: Hybrid Metaheuristics for Efficient Vehicle Routing Problem

Kovalenko Yehor, Pivavarau Andrei, Ochelska-Mierzejewska Joanna

Annals of Computer Science and Information Systems

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2025

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Vol. 45

103--110

EN

This article presents a hybrid algorithm developed to solve the Vehicle Routing Problem with Time Windows (VRPTW), which involves finding optimal routes for a fleet of vehicles serving a set of geographically dispersed customers within specified time intervals. The proposed solution combines Ant Colony Optimization (ACO) as the primary method for global solution construction, with the 2-opt local search technique used for route refinement, and a Tabu Search strategy to escape local optima and further improve solution quality. The algorithm dynamically adapts pheromone levels to favor both spatial and temporal proximity between customers, enhancing decision making during route construction. Experimental results demonstrate that the hybrid approach yields high-quality solutions, significantly improving known results by up to 30\% in some cases, while maintaining reasonable computation times. This makes the algorithm well-suited for real-time logistics scenarios where time efficiency and solution accuracy are both critical.

6

Effectiveness of metaheuristics applied to Human Resource Allocation Problem in Short-Term Employment Sector – a case study

Myszkowski Paweł B., Przewozniczek Michał, Kopocinski Lukasz

Annals of Computer Science and Information Systems

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2025

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Vol. 45

137--143

EN

This work identifies and defines the real-world Human Resource Allocation Problem in Short-Term Employment Sector (HRAP-STE). HRAP is a subclass of the classic Human Resource Allocation Problem, adopted to the short-term employment sector, where the main everyday objective is assigning employees to the customer factories. This process has three types of actors: customers, employees, and the company from the short-term employment sector, which provides a platform for cooperation. Usually, customers require significantly more employees than their available number. Since employee assignment is usually a subject of long-term cooperation, all customers should be satisfied (at least partially) even if they do not bring the highest profit. Thus, for a company in the short-term sector, HRAP refers to three objectives: profit from projects, priority of projects, and balance in the project portfolio to satisfy all clients. In this work, we define a specific HRAP-STE problem, consider its crucial elements, and define a benchmark set of real and artificial instances. To investigate the HRAP-STE as a real case study, we apply and compare well-known (meta)heuristics (shown effective in solving real-world problems) dedicated to solving discrete problems. The computational results show the advantages of (meta)heuristics in solving instances of a larger size.

7

New ideas in Lagrangian relaxation for a scheduling problem with the weighted tardiness criterion

Rudy Jarosław, Idzikowski Radosław, Smutnicki Czesław, Banaszak Zbigniew, Bocewicz Grzegorz

International Journal of Applied Mathematics and Computer Science

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2024

|

Vol. 34, no. 2

235--251

EN

We consider an extension of Lagrangian relaxation methods for solving the total weighted tardiness scheduling problem on a single machine. First, we investigate a straightforward relaxation method and decompose it into upper and lower subproblems. For the upper subproblem we propose an alternative solving method in the form of a local search metaheuristic. We also introduce a scaling technique by arbitrary numbers to reduce the complexity of the problem and confront it with greatest common divisor scaling. Next, we propose a novel alternative relaxation approach based on aggregating constraints. We discuss the properties and implementation of this new approach and a technique to further reduce its computational complexity. We perform a number of computer experiments on instances based on the OR-Library generation scheme to illustrate and ascertain the numerical properties of the proposed methods. The results indicate that for larger instances the proposed alternative relaxation and scaling approaches have a much better convergence rate with little to no decrease in solution quality. The results also show that the proposed local-search metaheuristic is a viable alternative to the existing solving methods.

8

A novel chaotic binary butterfly optimization algorithm based feature selection model for classification of autism spectrum disorder

Ramakrishnan Anandkumar, Ramalingam Rajakumar, Ramalingam Padmanaban, Ravi Vinayakumar, Alahmadi Tahani Jaser, Maidin Siti Sarah

International Journal of Applied Mathematics and Computer Science

|

2024

|

Vol. 34, no. 4

647--660

EN

Autism spectrum disorder (ASD) issues formidable challenges in early diagnosis and intervention, requiring efficient methods for identification and treatment. By utilizing machine learning, the risk of ASD can be accurately and promptly evaluated, thereby optimizing the analysis and expediting treatment access. However, accessing high dimensional data degrades the classifier performance. In this regard, feature selection is considered an important process that enhances the classifier results. In this paper, a chaotic binary butterfly optimization algorithm based feature selection and data classification (CBBOAFS-DC) technique is proposed. It involves, preprocessing and feature selection along with data classification. Besides, a binary variant of the chaotic BOA (CBOA) is presented to choose an optimal set of a features. In addition, the CBBOAFS-DC technique employs bacterial colony optimization with a stacked sparse auto-encoder (BCO-SSAE) model for data classification. This model makes use of the BCO algorithm to optimally adjust the ‘weight’ and ‘bias’ parameters of the SSAE model to improve classification accuracy. Experiments show that the proposed scheme offers better results than benchmarked methods.

9

Improving -parameterized differential transform method with Dandelion optimizer for solving ordinary differential equations

Najeeb Mustafa Raed, Qasim Omar Saber

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2024

|

T. 14, nr 4

104--108

EN

In this manuscript, we aim to address Ordinary Differential Equations (ODEs) by α-Parameterized Differential Transform Method (α-PDTM). Additionally, we seek to enhance the effectiveness of α-PDTM by incorporating the Dandelion Optimizer (DO). The DO plays a crucial role in optimizing the parameter α, ensuring its adjustment and modification to secure the most favorable value. This refinement results in a more accurate approximation compared to conventional methods. The proposed approach, referred to as (αDO-PDTM), demonstrates a solution distinguished by its reliability and efficiency, as determined through the computation of Maximum Absolute Error (MAE) and the Mean Square Errors (MSE).

PL

Celem niniejszego manuskryptu jest rozwiązanie równań różniczkowych zwyczajnych (ODE) metodą α-parametryzowanej transformacji różniczkowej (α-PDTM). Ponadto staramy się zwiększyć skuteczność α-PDTM poprzez włączenie optymalizatora Dandelion (DO). DO odgrywa kluczową rolę w optymalizacji parametru α, zapewniając jego dostosowanie i modyfikację w celu zabezpieczenia najbardziej korzystnej wartości. To udoskonalenie skutkuje dokładniejszym przybliżeniem w porównaniu z metodami konwencjonalnymi. Proponowane podejście, określane jako (αDO-PDTM), demonstruje rozwiązanie wyróżniające się niezawodnością i wydajnością, co zostało określone poprzez obliczenie maksymalnego błędu bezwzględnego (MAE) i średnich błędów kwadratowych (MSE).

10

Reference Tracking with Responsive Minimum-Separation Threat Areas Avoidance Method using PSO Based MPC Control

Pawłowski Piotr

Elektronika : konstrukcje, technologie, zastosowania

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2024

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Vol. 65, Nr 8

44--48

EN

In this paper, we propose a solution for motion control of the object (agent) within a certain region of interest considering distance to actual reference trajectory and the threat posed by occurring hazardous regions of denial. In this application, a PSO (Particle Swarm Optimization) based MPC (Model Predictive Controller) will be designed, with the aim to achieve flexibility and responsiveness to changing environment conditions (such as appearing threats), that will allow for real short-time path adjustments. Presented approach allows for defining required effective separation between the agent and encountered and identified threats, preserving sensitivity for reference tracking errors.

PL

W poniższym artykule, przedstawiamy propozycję rozwiązania umożliwiającego sterowanie ruchem obiektu (agenta) w zadanym obszarze zainteresowania, uwzględniając napotkane obszary zagrożenia oraz zmiany odległości od zadanej trajektorii ruchu. W tym celu zastosowany został kontroler predykcyjny MPC (Model Predictive Controller), wyposażony w optymalizator oparty na metodzie optymalizacji za pomocą roju cząstek PSO (Particle Swarm Optimization). Waściwości takiego kontrolera pozwalają na reagowanie na zmianę warunków otoczenia (takich jak pojawiające się zagrożenia), elastycznie i responsywnie dostosowując i korygując w czasie rzeczywistym krótkoterminową trajektorię. Przedstawiona strategia daje możliwość zdefiniowania efektywnej wymaganej separacji pomiędzy agentem a napotkanymi, zidentyfikowanymi zagrożeniami, jednocześnie zachowując podatność na błędy śledzenia trajektorii.

11

Optimization of fractional order PI controller using metaheuristics algorithms applied to multilevel inverter for grid connected PV

Boucheriette Wafa, Mechgoug Raihane, Benguesmia Hani

Diagnostyka

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2023

|

Vol. 24, No. 3

art. no. 2023311

EN

Due to its multiple advantages in industrial and grid-connected applications, Multi-Level Inverters (MLIs) have increased in popularity in recent years. To improve the efficiency of a grid-connected PV system's integrated multi-level inverter fractional order PI (FOPI) controllers are used to describe the control process. The control system is made up of three control loops based on FOPI controllers: one for controlling the intermediate circuit voltage (Vdc) and the other two for controlling the direct and quadratic currents (Id, Iq) supplied by the multilevel inverter. The proposed controller parameters (Kp, KI, λ) must be selected in order to increase the efficiency of the multi-level inverter while decreasing the total harmonic distortion (THD) of the output current of the inverter as well as voltage. For this we used three meta-heuristic algorithms (PSO, ABC, GWO). The performance of the three controllers PSO-FOPI, ABC-FOPI and GWO-FOPI controller is compared. The findings showed that GWO-FOPI performs better than the other PSO-FOPI and ABC-FOPI in accuracy and total harmonic distortion THD term. The simulation will be conducted using Matlab/Simulink.

12

Application of the path-repairing technique and virus optimization algorithm for the dimensional synthesis of four-bar mechanisms

Grabski Jakub Krzysztof, Sopa Martyna, Mrozek Agata

Archives of Civil and Mechanical Engineering

|

2023

|

Vol. 23, no. 2

art. no. e134, 2023

EN

This paper considers the synthesis of the four-bar mechanism. It is treated here as an optimization problem, in which an objective function is defined. To solve this problem, a metaheuristic called the virus optimization algorithm is employed. Furthermore, a new path-repairing technique recently published by Sleesongsom and Bureerat is applied instead of the very common technique related to the application of a penalty function. This makes the search by means of the metaheuristic more efficient. Furthermore, the obtained results are very accurate.

13

Comparative Analysis of Exact, Heuristic and Metaheuristic Algorithms for Flexible Assembly Scheduling

Maghiar Octavian, Selea Teodora, Copie Adrian, Micota Flavia, Marin Mircea

Annals of Computer Science and Information Systems

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2023

|

Vol. 35

615--625

EN

Real-world manufacturing scenarios usually lead to difficult assembly scheduling problems. Besides strict precedence constraints between jobs or operations, such problems incorporate constraints related to maintenance activities on working stations (machines) and specific setup times when different operations are executed on the same machine. This paper analyzes the performance of several approaches, based on mathematical programming and on (meta)heuristics, to solve flexible assembly scheduling problems characterized by an arbitrary tree-like structure of the operation network. In this context, a specific encoding of candidate solutions and some specific perturbation operators are proposed. The encoding and the operators allows the distribution of sub(batches) of operations on several machines which leads, for some assembly scheduling problems, to a significant decrease of the makespan.

14

Ant Colony Optimization for Workforce Planning with Hybridization

Fidanova Stefka, Ganzha Maria

Annals of Computer Science and Information Systems

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2023

|

Vol. 35

955--959

EN

Production organization plays a key role in the success of any enterprise. Workforce planning and assignment is an important element of the production organization. Optimizing workforce planning can improve the overall organization of production. The main goal is to minimize the assignment cost of the workers who will perform the planned work. The problem is known to be NP-hard, therefore we will apply methods from the field of artificial intelligence. The problem is to select workers to be assigned to perform the jobs. This is a difficult optimization problem with very strict constraints. For this reason, most of the existing methods hardly find feasible solutions. We propose Ant Colony Optimization Algorithm with hybridization, combination with local search procedures. We compare and analyze their performance.

15

Agricultural system modelling with Ant Colony Optimization

Fidanova Stefka, Dimov Ivan, Angelova Denitsa, Ganzha Maria

Annals of Computer Science and Information Systems

|

2022

|

Vol. 30

329--332

EN

Cereals contribute significantly to humanity's livelihood. They are a source of more food energy worldwide than any other group of crops. Their production contributes considerably to the total global anthropogenic greenhouse gas (GHGs) emissions. In this study we propose a basic bio-economic farm model (BEFM) solved with the help of Ant Colony Optimization (ACO) methodology. We aim to assess farm profits and risks considering various types of policy incentives and adverse weather events. The proposed model can be applied to any annual crop.

16

Studium przypadku skuteczności nowych metod optymalizacji roju w porównaniu do metod znanych

Baumgart Jan, Sangho Belco

Studia i Materiały Informatyki Stosowanej

|

2021

|

T. 13, nr 1

47--50

PL

Porównianie skuteczności nowych metod optymalizacji roju w porównaniu z metodami znanymi w dziedzinie. Inspirowane naturą algorytmy metaheurystyczne stają się coraz bardziej popularne w rozwiązywaniu problemów optymalizacyjnych. Dzięki ich popularności niemal codziennie możemy zobaczyć nowepodejścia i proponowane rozwiązania. W tym artykule przedstawię porównanie, które pokaże kilka najnowszychprac z tej dziedziny w porównaniu z niektórymi algorytmami traktowanymi jako podstawa dziedziny. Głównymcelem było porównanie ostatnio wprowadzonych algorytmów roju i określenie, kiedy nowe rozwiązania są faktycznie szybsze i bardziej precyzyjne. Podsumowując, czy przetestowane nowe podejścia są lepsze niż obecne,dobrze znane i ugruntowane w terenie algorytmy. Algorytmy brane pod uwagę w tej pracy to: Particle SwarmOptimization [5], Artifical Bee Colony [3], Elephant Herding Optimization [7], Whale Optimization [4] i Gras-shopper Optimization [6].Algorytmy uznawane za nowe w tej dziedzinie porównano z dwoma popularnymi idobrze znanymi algorytmami metaheurystycznymi pod względem dokładności znalezionych rozwiązań i szybkości. Zgodnie z wynikami eksperymentów większość porównywanych nowych algorytmów dawała zadowalającewyniki w użytkowaniu.

EN

Comparing the effectiveness of new methods of swarm optimization in comparison with knownmethods. Nature-inspired metaheuristic algorithms are becoming more and more popular in solving optimization problems. Thanks to their popularity, we can see new approaches and proposed solutions almost everyday. In this article, I will present a comparison that will show some of the most recent works in this fieldcompared to some algorithms considered as the basis of the field. The main goal was to compare the recently introduced swarm algorithms and determine when new solutions are actually faster and more precise. Inconclusion, are the new approaches tested better than the current, well-known and field-grounded algorithms?The algorithms considered in this paper are Particle Swarm Optimization, Artifical Bee Colony, Elephant Herding Optimization, Whale Optimization, and Grasshopper Optimization. Algorithms considered new inthis field were compared with two popular and well-known metaheuristic algorithms in terms of accuracy ofsolutions found and speed. According to the experimental results, most of the compared new algorithms gave satisfactory results in use.

17

Scheduling trucks in a multi-door cross-docking system with time windows

Ozden G., Saricicek I.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2019

|

Vol. 67, nr 2

344--362

EN

Cross-docking is a strategy that distributes products directly from a supplier or manufacturing plant to a customer or retail chain, reducing handling or storage time. This study focuses on the truck scheduling problem, which consists of assigning each truck to a door at the dock and determining the sequences for the trucks at each door considering the time-window aspect. The study presents a mathematical model for door assignment and truck scheduling with time windows at multi-door cross-docking centers. The objective of the model is to minimize the overall earliness and tardiness for outbound trucks. Simulated annealing (SA) and tabu search (TS) algorithms are proposed to solve largesized problems. The results of the mathematical model and of meta-heuristic algorithms are compared by generating test problems for different sizes. A decision support system (DSS) is also designed for the truck scheduling problem for multi-door cross-docking centers. Computational results show that TS and SA algorithms are efficient in solving large-sized problems in a reasonable time.

18

Ant colony optimization algorithm for workforce planning: influence of the evaporation parameter

Fidanova Stefka, Luque Gabriel, Roeva Olympia, Ganzha Maria

Annals of Computer Science and Information Systems

|

2019

|

Vol. 18

177--181

EN

Optimization of the production process is important for every factory or organization. The better organization can be done by optimization of the workforce planing. The main goal is decreasing the assignment cost of the workers with the help of which, the work will be done. The problem is NP-hard, therefore it can be solved with algorithms coming from artificial intelligence. The problem is to select employers and to assign them to the jobs to be performed. The constraints of this problem are very strong and for the algorithms is difficult to find feasible solutions. We apply Ant Colony Optimization Algorithm to solve the problem. We investigate the algorithm performance according evaporation parameter. The aim is to find the best parameter setting.

19

An Analytical Study for the Role of Fuzzy Logic in Improving Metaheuristic Optimization Algorithms

Vij Sonakshi, Jain Amita, Tayal Devendra, Castillo Oscar

Journal of Automation Mobile Robotics and Intelligent Systems

|

2018

|

Vol. 12, No. 4

11--27

EN

The research applications of fuzzy logic have always been multidisciplinary in nature due to its ability in handling vagueness and imprecision. This paper presents an analytical study in the role of fuzzy logic in the area of metaheuristics using Web of Science (WoS) as the data source. In this case, 178 research papers are extracted from it in the time span of 1989-2016. This paper analyzes various aspects of a research publication in a scientometric manner. The top cited research papers, country wise contribution, topmost organizations, top research areas, top source titles, control terms and WoS categories are analyzed. Also, the top 3 fuzzy evolutionary algorithms are extracted and their top research papers are mentioned along with their topmost research domain. Since neuro fuzzy logic poses feasible options for solving numerous research problems, hence a section is also included by the authors to present an analytical study regarding research in it. Overall, this study helps in evaluating the recent research patterns in the field of fuzzy metaheuristics along with envisioning the future trends for the same. While on one hand this helps in providing a new path to the researchers who are beginners in this field as they can start exploring it through the analysis mentioned here, on the other hand it provides an insight to professional researchers too who can dig a little deeper in this field using knowledge from this study.

20

Minimal cycle time determination and golf neighborhood generation for the cyclic flexible job shop problem

Bożejko W., Pempera J., Wodecki M.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2018

|

Vol. 66, nr 3

333--344

EN

In the paper, a problem of scheduling operations in the cyclic flexible job shop system is considered. A new, very fast method of determining the cycle time for any order of tasks on machines is also presented. It is based on the analysis of the paths in the graph representing the examined problem. The theorems concerning specific properties of the graph are proven and used in the construction of the heuristic algorithm searching the solutions space by using the so-called golf neighborhood, which is generated in a way similar to the game of golf, which helps to intensify and diversify calculations. The conducted computational experiments fully confirmed the effectiveness of the proposed method. The proposed methods and properties can be adapted and used in the construction of local search algorithms for solving many other optimization problems.