Wyniki wyszukiwania - BazTech

1

MOPOA: A New Multi-Objective Pufferfish Optimization Algorithm

Nasrabadi Mohaddethe, Khazaiepoor Mahdi, Kherad Mahdi

Foundations of Computing and Decision Sciences

|

2026

|

Vol. 51, No. 1

139--170

EN

Multi-objective optimization problems (MOPs) pose significant challenges due to the presence of multiple conflicting objectives. This paper introduces MOPOA, a novel Multi-Objective Pufferfish Optimization Algorithm inspired by the defensive behaviors of pufferfish in nature. MOPOA extends the original single-objective POA by incorporating Pareto dominance, an external archive for preserving non-dominated solutions, and a crowding distance mechanism to maintain solution diversity. The algorithm balances exploration and exploitation through biologically inspired phases simulating predator-prey interactions. To evaluate MOPOA's performance, it was benchmarked against several state-of-the-art algorithms, including NSGA-III, MOPSO, MODA, and MOFDO, on two well-known test suites: the ZDT and CEC-2019 multi-objective functions. Results indicate that MOPOA not only achieves superior convergence to the Pareto front but also maintains high diversity and robustness across diverse optimization scenarios. These findings position MOPOA as a powerful and adaptive tool for solving complex real-world multi-objective problems.

2

From static metrics to multi-objective parametric optimization: enhancing the performance of semi-arid residential buildings through the Hourly Thermal Comfort Index

Nedjahi Sara, Khelil Sara, Khadraoui Mohamed Amine

Budownictwo i Architektura

|

2026

|

Vol. 25, no. 2

art. no 26034

EN

Energy consumption and thermal comfort remain pivotal global challenges that directly impact the quality of residential life and habitat sustainability. This research develops a numerical model to predict the thermal behavior of residential buildings through a comprehensive parametric analysis, focusing on windows as critical elements in energy exchange. A wide range of variables was evaluated, including insulation deficiencies, material properties, glazing types, window-to-wall ratio (WWR), solar shading devices, floor levels, and operational factors such as family size and lighting loads. The study was conducted on a residential facade in Batna, Algeria – a warm, semi-arid climate. Thermal comfort was assessed by monitoring ambient temperatures and correlating them with the Hourly Thermal Comfort Index (HTCI) in accordance with ASHRAE 55 standards. To ensure objective accuracy, the study focused on a field-based sample of five residents living in southwest-oriented units. The findings, which maximize thermal performance using the "Galapagos" evolutionary algorithm, show that single glazing significantly degrades comfort levels. In contrast, advanced configurations – combining double or triple low-E glazing with horizontal shading – increased the comfort index from a baseline of 55.54% to over 77% for the southwest orientation. By providing a precise hourly analysis (HTCI) that captures instantaneous thermal fluctuations, this study addresses the limitations of traditional static assessments. These findings establish a framework for future research that integrates in situ measurements with occupant surveys, effectively bridging the gap between objective performance and subjective experience to achieve a holistic understanding of thermal challenges in real-world residential environments.

3

Multi-objective optimization for dynamic performance of overhead conductor rail anchor segments based on non-dominated sorting genetic algorithm II and response surface method

Zhao Xia, Zhang Zhulin, Wang Ying, Mu Xiuqing, Ge Leĳiao, Teng Ming

Archives of Electrical Engineering

|

2026

|

Vol. 75, nr 1

135--156

EN

The overlapping part between the overhead conductor rail (OCR) anchor segments is called the anchor joint, which is a key component that constrains the dynamic performance of the OCR. When the train passes through the anchor joint, the contact force fluctuates significantly, degrading the current collection quality. This paper carries out the multiobjective optimization of the dynamic performance at the OCR anchor segments by combining the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with the Response Surface Method (RSM), in order to alleviate the severe fluctuations in contact force, thus making a trade-off between the two inconsistent objectives of contact force standard deviation (𝐹𝛿) and range (𝐹𝑟). Firstly, the Box–Behnken experimental design method was employed, with the elevation of the first suspension point, cantilever span, anchor joint, and standard span as design variables, and 𝐹𝛿 and 𝐹𝑟 as objective functions, to conduct numerical simulation studies on them. Secondly, to enhance the dynamic performance of OCR anchor segments, the NSGA-II was used to optimize the objective functions 𝐹𝛿 and 𝐹𝑟 Finally, simulations using the geometric parameters corresponding to the Pareto optimal solutions obtained by the NSGA-II showed that, compared to the original design, 𝐹𝛿 was increased by 11.18%, and 𝐹𝑟 was raised by 35.04%.

4

Multi-objective optimization of the slide burnishing process for 36CrNiMo4 steel using the topsis approach

Dzierwa Andrzej, Slota Jan, Markopoulos Angelos

Advances in Science and Technology. Research Journal

|

2026

|

Vol. 20, no. 3

387--395

EN

The research presents a multi-objective optimization of the slide burnishing process applied to 36CrNiMo4 steel, aiming to enhance surface quality and mechanical properties. The study focuses on key process parameters, including burnishing force, feed rate, and burnishing speed, and their effects on surface roughness, microhardness, and residual stress. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach is employed to identify optimal parameter settings that balance multiple objectives simultaneously. The conducted research demonstrates significant improvements in the treated steel components and the process parameters: P = 120 N, f = 0.04 mm/rev, n = 900 rev/min were found to be the most advantageous. The proposed optimization framework provides an effective decision-making tool for process engineers to achieve superior surface integrity in slide burnishing applications.

5

Multi-Objective optimization of corrosion current density and polarization resistance of surface-ground AA7075 thin plate in 3.5 wt.% NaCl solution

Boadu Michael Kwabena, Agyei-Agyemang Anthony, Andoh Prince Yaw

Advances in Mechanical and Materials Engineering

|

2026

|

Vol. 43, nr 1

109--125

EN

AA7075 thin plates are extensively used in the marine industry, particularly for the manufacturing of hydrofoil skin panels. Surface grinding is a critical finishing process for these plates, yet the optimal grinding parameters that minimize corrosion current density (Icorr) and maximize polarization resistance (Rp) are not well established. This study was conducted to determine optimal grinding settings for controlling Icorr and Rp in 3.5 wt.% NaCl solution (simulated seawater). AA7075 thin plates were ground following a design of experiments (DoE) schedule, and Icorr and Rp were measured using a CorrTest electrochemical workstation. Results showed that Icorr increased markedly with higher table speed (50 spm), feed rate (5.0 mm/min), and grinding depth (1.0 mm), while Rp decreased under the same conditions. Standardized effects analysis identified feed rate and grinding depth as the most influential factors, each with an effect of 10.94, whereas table speed had a moderate effect, and interaction terms played secondary but significant roles. Regression models demonstrated strong predictive capability, with R² and predicted R² values of 99.28% and 97.12% for Icorr, and 98.43% and 93.71% for Rp. The optimal settings were found at low table speed (2 spm), low feed rate (1.0 mm/min), and low grinding depth (0.2 mm).

PL

Cienkie płyty ze stopu aluminium AA7075 są szeroko powszechnie w przemyśle morskim, szczególnie do wytwarzania paneli poszycia hydropłatów. Szlifowanie powierzchniowe stanowi kluczowy proces wykończeniowy tych płyt, jednak optymalne parametry szlifowania minimalizujące gęstość prądu korozyjnego (Icorr) oraz maksymalizujące opór polaryzacyjny (Rp) nie zostały dotąd jednoznacznie określone. Celem badań było wyznaczenie optymalnych parametrów szlifowania umożliwiających kontrolę parametrów Icorr i Rp w roztworze 3,5% mas. NaCl (symulowana woda morska). Cienkie płyty ze stopu AA7075 szlifowano zgodnie z planem eksperymentalnym, a wartości Icorr i Rp mierzono przy użyciu elektrochemicznej stacji pomiarowej CorrTest. Wyniki wykazały, że wartość parametruIcorr zwiększała się przy większej prędkości stołu (50 spm), większym posuwie (5,0 mm/min) oraz większej głębokości szlifowania (1,0 mm), natomiast wartość parameteru Rp zmniejszała się w tych samych warunkach. Analiza efektów standaryzowanych wykazała, że posuw i głębokość szlifowania były czynnikami o największym wpływie (każdy z efektem równym 10,94), podczas gdy prędkość stołu miała wpływ umiarkowany, a wyrazy interakcyjne odgrywały rolę drugorzędną, lecz istotną statystycznie. Modele regresyjne wykazały wysoką zdolność predykcyjną, z wartościami R² i przewidywanego R² równymi odpowiednio 99,28% i 97,12% dla parametru Icorr oraz 98,43% i 93,71% dla parametru Rp. Optymalne parametry uzyskano przy niskiej prędkości stołu (2 spm), niskim posuwie (1,0 mm/min) oraz małej głębokości szlifowania (0,2 mm).

6

Optimal design of the Brushed DC motor implemented for car window lifting system using Taguchi method

Knypiński Łukasz, Devarapalli Ramesh, Roszak Sebastian, Kamiński Marcin

Przegląd Elektrotechniczny

|

2025

|

R. 101, nr 4

134--137

EN

The paper presents the results of the multi-objective optimization of the brushed permanent magnet motor for a car window lifting system. The issue was executed by the use of orthogonal Taguchi tables. Application of the Taguchi method leads to simplified optimization. This is because, during the optimization process, the analysis tasks were computed for pre-defined numbers of the experiments. The number of experiments depends on a number and an assigned variability of a design variables. The motor was described by three variables, describing its structure of the magnetic circuit. The optimality criteria were formed by different combinations of functional parameters of the device. The selected functional parameters are taken into account in the multi-objective function. The mathematical model of the brushed DC motor includes (a) the electromagnetic field equations with non-linearity of the ferromagnetic material, (b) equations of the external supply circuit, and (c) equations of mechanical motion. The selected results of optimization were presented and discussed.

PL

W artykule przedstawiono wyniki wielokryterialnej optymalizacji magnetoelektrycznego silnika prądu stałego do napędu szyb w samochodach. Zadanie wykonano przy wykorzystaniu metody tablic Taguchi. Wspomniany algorytm pozwala na skrócenie czasu trwania obliczeń, bowiem przetwarzanie danych dotyczących rozkładu pola elektromagnetycznego oraz parametrów funkcjonalnych, uwzględnianych w wielkokryterialnej funkcji celu, wykonywane są dla zadanej liczby eksperymentów. Obiekt został opisany przy wykorzystaniu trzech zmiennych decyzyjnych. W procesie optymalizacji uwzględniono wybrane parametry funkcjonalne urządzenia. Model matematyczny silnika magnetoelektrycznego zawierał równania pola elektromagnetycznego z uwzględnieniem nieliniowości obwodu magnetycznego, zależności matematyczne zewnęt

7

Multi-objective optimal reactive power planning using particle swarm optimization

Yadav Sandeep, Saini Sanju

Przegląd Elektrotechniczny

|

2025

|

R. 101, nr 4

234--237

EN

The enhanced multi-objective deterministic reactive power planning power system presented in this study takes wind power production and load demand uncertainties into account. Reactive power planning comprises of all the planning steps required to improve electricity networks' stability and voltage profile. This study utilizes a Multi-Objective+Particle Swarm Optimization technique to get the most optimum Renewable Power Production (RPP), while considering the inherent uncertainty related to renewable sources. Attaining goals of preserving the high voltage profile while concurrently decreasing the costs linked to the implementation of VAr results in a mutually advantageous conclusion. The test bus system IEEE 30 is utilised to assess the suggested method's efficacy.

PL

Ulepszony wielocelowy deterministyczny system planowania mocy biernej przedstawiony w tym badaniu uwzględnia niepewność produkcji energii wiatrowej i zapotrzebowania na moc. Planowanie mocy biernej obejmuje wszystkie kroki planowania wymagane do poprawy stabilności sieci elektroenergetycznych i profilu napięcia. W tym badaniu wykorzystano technikę optymalizacji wielocelowej + roju cząstek, aby uzyskać najbardziej optymalną produkcję energii odnawialnej (RPP), biorąc pod uwagę nieodłączną niepewność związaną ze źródłami odnawialnymi. Osiągnięcie celów zachowania profilu wysokiego napięcia przy jednoczesnym zmniejszeniu kosztów związanych z wdrożeniem VAr prowadzi do wzajemnie korzystnego wniosku. System magistrali testowej IEEE 30 jest wykorzystywany do oceny skuteczności sugerowanej metody.

8

Optimal Trajectory Planning Method for Handling Robots Based on Multi-objective Particle Swarm Optimization Guided by Evolutionary Information

Liu Qunpo, Sun Rui, Bu Xuhui, Hanajima Naohiko, Ding Weiping

Eksploatacja i Niezawodność

|

2025

|

Vol. 27, no. 4

art. no. 202990

EN

This paper addresses the trajectory optimization and reliability challenges of 6-DOF handling robots by proposing a multi-objective particle swarm optimization method guided by evolutionary information (EIGMOPSO). The method optimizes trajectory planning in terms of time, energy consumption, and smoothness to enhance operational reliability and mechanical durability. To overcome the limitations of traditional MOPSO, a regionally dynamic stratification strategy based on evolutionary capability assessment is proposed, classifying the population into regions by evaluating fitness, diversity, and stability. A layered optimization mechanism dynamically adjusts exploration and exploitation processes, improving global search capability. Additionally, a dynamic two-stage archive maintenance strategy ensures high-quality solutions. Experimental results demonstrate that EIGMOPSO significantly improves operational efficiency, reduces mechanical wear and energy consumption, and enhances system maintainability, making it well-suited for handling robots in industrial environments.

9

Multi-objective optimization of the deck structure of the lightweight microvehicle for improved reliability and desired comfort and stability while driving

Damaziak Krzysztof, Mazurkiewicz Łukasz, Sybilski Kamil, Gulino Michelangelo-Santo, Zonfrillo Giovanni, Vangi Dario

Eksploatacja i Niezawodność

|

2025

|

Vol. 27, no. 4

art. no. 206048

EN

Electric kick scooters represent a viable alternative to reduce emissions associated with the use of cars. However, several obstacles hinder the widespread adoption of e-scooters, primarily stemming from their high mass, short range, and challenges in navigating uphill routes. The LEONARDO project aims to develop an innovative, 10 kg microvehicle with high torque, similar to a monowheel, while maintaining the ease of riding. To achieve this goal, heavy and complex suspension components were eschewed. In order to maintain ride comfort and stability, it was necessary to design a scooter deck with a specific susceptibility, but one that provided a high level of vehicle reliability. The article presents a novel approach to the design of a microvehicle deck. The methodology and the results of measuring operational loads are presented, which were used to develop a design that meets the assumed level of reliability, comfort and stability. The study employs a comparative analysis of two distinct optimization algorithms, each accounting for varying load scenarios and multiple objectives.

10

Multi-objective optimization of a hybrid excitation generator with a paralel magnetic circuit based on the coupling of dual optimization algorithms

Sun Tingjun, Hu Wenjing, Meng Fanxi, Wu Jinke, Liu Yixin, Geng Huihui, Liu Weitao, Wang Wei

Archives of Electrical Engineering

|

2025

|

Vol. 74, nr 1

127--149

EN

The coaxial parallel magnetic circuit dual-rotor hybrid excitation structure generator exhibits several advantages, including high output performance, a wide adjustment range, and excellent stability. This study introduces a topology for a parallel magnetic circuit hybrid excitation generator (PMC-HEG) that utilizes a combination of permanent magnet and electrical excitation. It features salient pole rotors and claw pole rotors, with the latter embedded with permanent magnets, sharing a common stator. The analysis of the rotor magnetic field is conducted using both the equivalent magnetic circuit method and the subdomain method. Through an examination of the generator’s electromagnetic performance, key rotor parameters related to optimization objectives are identified. Finite element simulation analysis is performed on the rotor parameters, employing various optimization algorithms to enhance the salient pole and claw pole rotors, focusing on the amplitude of the induced electromotive force and the distortion rate of the induced electromotive force as optimization targets. The final optimized parameter values are obtained. A prototype is fabricated and tested, with experimental results confirming the reliability of the optimization method. The optimized parallel magnetic circuit hybrid excitation generator demonstrates an increase in the amplitude of the induced electromotive force, an improvement in the fundamental wave of the induced electromotive force, a reduction in harmonic distortion rate, and a significant enhancement in overall output performance.

11

Design and optimization of an improved hybrid permanent magnets Vernier machine with less-rare-earth and high torque density

Li Hui

Archives of Electrical Engineering

|

2025

|

Vol. 74, nr 1

86--103

EN

This paper proposes a novel improved hybrid permanent magnet Vernier machine (IHPMVM), which is characterized by less-rare-earth (LRE) and high torque-density. The proposed machine features a hybrid magnet arrangement, which adopts both rare earth (RE) and LRE magnets in one magnetic pole simultaneously. The proposed improved design can reduce the consumptions of RE materials by employing low-cost LRE magnets in place of RE magnets. Besides, the hybrid magnet arrangement design has a good magnetic flux-concentrated effect, resulting in high torque density. Particularly, dummy slots are introduced to achieve a flux modulation effect. This unique design effectively reduces the inevitable leakage flux, thereby further improving the utilization of PMs and torque density. Firstly, the machine configuration and its improved design are introduced and investigated. Then, a multi-objective optimization is carried out to obtain the optimal design of the proposed machine considering comprehensive performance. Furthermore, the preliminary electromagnetic characteristics of the proposed machine are compared and analyzed using finite element (FE) methods, which verifies the effectiveness of the optimization. Finally, the demagnetization risk of the LRE magnets is evaluated. This paper is expected to provide a technical reference for designing LRE machines.

12

Optimizing home electricity consumption with user habits analysis

Sung Tien-Wen, Li Jie, Huang Zeming, Fan Qingjun

Archives of Control Sciences

|

2025

|

Vol. 35, No. 3

587--629

EN

With the development of the national economy and the advancement of urbanization, the demand for household electricity consumption is rising sharply and the structure of the complexity of the trend. In order to help users realize intelligent management of household electricity consumption and improve the efficiency of electricity consumption. This paper proposes a smart home electricity consumption optimization method based on user habit analysis. Firstly, the home energy management system framework and related technologies are introduced, and home load dispatch types are categorized and modeled. Then, a circular coordinate fitting method is used to analyze the data and thus derive the users’ electricity consumption habits, as well as an improved K-mean clustering algorithm to mine the users’ personalized demands. Then, a multi-objective intelligent power consumption optimization model is established, and an improved artificial bee colony algorithm is used to solve practical problems. Finally, simulation experiments using real household electricity consumption datasets are conducted to verify the validity and feasibility of the method. The method in this paper can formulate power consumption plans according to the needs of different families, improve the convenience and comfort of users’ power consumption, and realize more reasonable and energy-saving power consumption through users’ participation in adjusting and improving their habitual behaviors. This research has application value and promotion significance and can be widely used in the field of smart home and energy saving and emission reduction.

13

The optimization model of building construction based on BIM and improved NSGA-III algorithm

Wang Meng

Archives of Civil Engineering

|

2025

|

Vol. 71, nr 2

5--18

EN

Currently, there are difficulties in dealing with higher construction requirements and standards in subway construction management. Therefore, a multi-objective optimization model was constructed based on building information management technology, and an improved non-dominated sorting genetic algorithm III was introduced to optimize the model solution. And experimental verification was conducted. These experiments confirmed that the average HV of the improved algorithm was 0.67, which was higher than the original algorithm’s 0.65, indicating that it had higher convergence and reliability. The solution results of the non-dominated sorting genetic algorithm II showed that the optimized cost was 185.1899 million yuan. The cost of optimizing the original non-dominated sorting genetic algorithm III was 184.6469 million yuan. The total cost of optimizing the research algorithm was 184.1165 million yuan. In addition, the research algorithm had the shortest construction period, ideal cost, and significantly higher quality and safety levels than the comparison algorithms. And its time consumption was only 20 seconds, significantly lower than the comparison algorithms. And its cost was between 183 million to 187.5 million yuan, with higher stability and relatively concentrated distribution of solutions. Overall, the subway construction optimization model based on building information management and non-dominated sorting genetic algorithm III has high effectiveness and can be effectively applied in practical construction management.

14

Optimal allocation of urban land space based on NSGA2

Guo Yi, Bai Chaoqin, Zhao Peiwen

Archives of Civil Engineering

|

2025

|

Vol. 71, nr 1

157--172

EN

Urban land spatial optimization is one of the important issues in urban planning and land resource management. As the speed advancement of urbanization and the continuous increase of population, the rational use of land resources has become the key to sustainable urban development. Based on this, the study adopts the optimization goals of maximizing gross domestic product (GDP), reducing aerosol optical thickness and non-point source pollution (NPSP) load, and reducing land use change costs and incongruity. Three constraints are set simultaneously, including minimum construction land, water body, and cultivated land area. In addition, a fast non dominated sorting genetic algorithm (NSGA2) with elite strategy is used to address it. The outcomes denoted that the iterative distance of the proposed algorithm on the Bin and Cohen functions was only 0.048%, which was 0.522% lower than that of the NSGA2. Meanwhile, the reverse iteration distance value of this algorithm was only 4.14%, which was 22.76% lower than the adaptive weighted genetic algorithm. In addition, the algorithm’s Spacing value was only 4.28%, and the hypervolume index value was as high as 78.66%. This indicated that the research method had a good optimization effect on the optimal allocation (OA) of land space in urban agglomerations, providing scientific decision-making support for sustainable urban development.

15

Multi-objective optimization of construction project management based on an improved genetic algorithm

Dai Xiaoyan, Xia Wanwan, Xu Yingwen

Archives of Civil Engineering

|

2025

|

Vol. 71, nr 3

311--323

EN

In construction project management, it is crucial to consider multiple objectives, such as duration and cost, to develop an optimal plan. This paper established a multi-objective optimization model, taking into account the construction period, cost, safety, and quality of projects. A genetic algorithm (GA) was selected as the solution method, and the non-dominated sorting genetic algorithm-II (NSGA-II) was optimized by cat mapping, adaptive crossover, and mutation operators to obtain an improved algorithm for the model solution. Experiments were conducted to evaluate the performance of the designed algorithm. It was found that the improved NSGA-II exhibited superior convergence and diversity when applied to the test functions ZDT1-ZDT3. The mean construction period obtained from the model solution was 124 days, with a cost of 1,204,782 euros. The quality and safety levels achieved were 0.93 and 0.95, respectively, which were significantly better than those obtained by the NSGA-II. These findings demonstrate the reliability of the improved NSGA-II developed in this paper, suggesting its practical applicability.

16

Designing a connecting pin for a gantry crane by integrating the weighted sum method with the Taguchi method

Duong Truong Giang

Advances in Science and Technology. Research Journal

|

2025

|

Vol. 19, no. 9

129--142

EN

The paper includes studying the method of calculating joint design and then building a multi-objective optimization problem model using the weighted sum and Taguchi methods. The research object is the joint between the gantry crane leg and the beam. Due to the characteristics of the connection bearing large and changing loads, research is necessary to improve safety, longevity, and reliability. The experimental problem model has four design variables and four value levels. The study uses the orthogonal matrix L16 to calculate the response values for each objective function at each stage. To apply the weighted sum method, the study selects the objective function weights for equivalent stress, contact stress, and fatigue strength, transforming the multi-objective problem into a single-objective problem. The test results identified a new set of parameters meeting the goals. The fatigue criterion was reduced by 8.9%, and the fatigue safety factor increased from 1.36 to 1.39. The equivalent stress was decreased by 9%, and the safety factor increased from 2.58 to 2.84. Contact stress was reduced by 37%, and the safety factor increased from 1.29 to 2. Combining these two methods not only solves problems in engineering but can also solve many problems in different fields.

17

A decision support model for mitigating supply chain risks based on a modified fmea, multi-objective optimization and multi-criteria decision-making approach

Kayouh Nabil, Dkhissi Btissam

Management Systems in Production Engineering

|

2024

|

nr 1 (32)

87--102

EN

Recent years have seen a huge development in the subject of supply chain risk management. In this increasingly uncertain world, the use of practical and effective tools for decision making and risk mitigation has become more necessary than ever. In this research, mitigation strategies for a tier one multinational company operating in the automotive industry and providing an assembly operation to final customer Renault Tanger and Renault SOMACA were prioritized according to their effectiveness, as well as their implementation costs. Based on research in the literature and the opinions of experts in the field. 44 risks and 55 mitigation strategies were identified. FMEA (Failure Modes and Effects Analysis) method was used based on the latest AIAG 2019 edition to filter and identify the risks to be prioritized, we used then a multi-objective optimization approach to identify the mitigation strate-gies that constitute the Pareto front for each of the risks and finally used the EDAS method for the final ranking of the strategies. Our case revealed that strategies like ensuring elaborating a contingency planning and defining the responsibilities, imposing contractual obligations on subcontractors, applying a flexible supply contract were found to be relevant risk mitigation strategies for the company. Managers interested in mitigating risk can deploy this model to prioritize risk mitigation strategies.

18

Using TOPSIS Method for Solving Multi-Objective Optimization of a Two-stage Helical Gearbox with First Stage Double Gear-sets

Dinh Van-Thanh, Tran Huu-Danh, Vu Duc-Binh, Vu Duong, Vu Ngoc-Pi, Luu Tung Anh

Journal of Machine Engineering

|

2024

|

Vol. 24, No. 4

41--56

EN

In order to build a two-stage helical gearbox (THG) with first stage double gear-sets (FSDG), the multi-criteria decision-making (MCDM) method is introduced in this research as a new approach to solving the multi-objective optimization problem (MOOP). The study's objective is to determine the best primary design factors that will increase gearbox efficiency and decrease gearbox mass. To that end, the first stage's gear ratio and the first and second stages' coefficients of wheel face width (CWFW) were chosen as the three main design elements. Further-more, two distinct goals were analyzed: the lowest gearbox mass and the highest gearbox efficiency. Additionally, the MOOP is carried out in two steps: phase 1 solves the single-objective optimization problem to close the gap between variable levels, and phase 2 solves the MOOP to determine the optimal primary design factors. Furthermore, the TOPSIS approach was selected to address the MOOP problem. For the first time, an MCDM technique is used to solve the MOOP of a helical gearbox with FSDG and the power losses during idle motion in order to determine gearbox efficiency was taken into the investigation for the gearbox. When designing the gearbox, the optimal values for three crucial design parameters were ascertained using the study's results.

19

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

Pawłowski Piotr

Elektronika : konstrukcje, technologie, zastosowania

|

2024

|

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.

20

Software Defect Prediction Using Non-Dominated Sorting Genetic Algorithm and k-Nearest Neighbour Classifier

Azzeh Mohammad, Nassif Ali Bou, Talib Manar Abu, Iqba Hajra

e-Informatica Software Engineering Journal

|

2024

|

Vol. 18, nr 1

240103

EN

Background: Software Defect Prediction (SDP) is a vital step in software development. SDP aims to identify the most likely defect-prone modules before starting the testing phase, and it helps assign resources and reduces the cost of testing. Aim: Although many machine learning algorithms have been used to classify software modules based on static code metrics, the k-Nearest Neighbors (kNN) method does not greatly improve defect prediction because it requires careful set-up of multiple configuration parameters before it can be used. To address this issue, we used the Non-dominated Sorting Genetic Algorithm (NSGA-II) to optimize the parameters in the kNN classifier with favor to improve SDP accuracy. We used NSGA-II because the existing accuracy metrics often behave differently, making an opposite judgment in evaluating SDP models. This means that changing one parameter might improve one accuracy measure while it decreases the others. Method: The proposed NSGAII-kNN model was evaluated against the classical kNN model and state-of-the-art machine learning algorithms such as Support Vector Machine (SVM), Naïve Bayes (NB), and Random Forest (RF) classifiers. Results: Results indicate that the GA-optimized kNN model yields a higher Matthews Coefficient Correlation and higher balanced accuracy based on ten datasets. Conclusion: The paper concludes that integrating GA with kNN improved defect prediction when applied to large or small or large datasets.