Results in Control and Optimization最新文献

{"title":"Frequency regulation of two-area thermal and photovoltaic power system via flood algorithm","authors":"Serdar Ekinci ,&nbsp;Davut Izci ,&nbsp;Cebrail Turkeri ,&nbsp;Aseel Smerat ,&nbsp;Absalom E. Ezugwu ,&nbsp;Laith Abualigah","doi":"10.1016/j.rico.2025.100539","DOIUrl":"10.1016/j.rico.2025.100539","url":null,"abstract":"<div><div>Frequency regulation is critical for maintaining balance between supply and demand in interconnected power systems, ensuring grid stability and preventing disruptions. This becomes increasingly important with the integration of renewable energy sources, such as photovoltaic (PV) units, which introduce variability and complexity into power systems. In this regards, this study presents a novel approach to frequency regulation in a two-area interconnected power system comprising thermal and PV units. A Proportional-Integral (PI) controller is designed, and its parameters are optimally tuned using the flood algorithm (FLA). The innovative use of the FLA ensures robust performance and efficient frequency stabilization under varying operational conditions. The implementation details of the FLA-tuned PI controller are provided, and its performance is rigorously compared with PI controllers tuned using several state-of-the-art optimization techniques. These include sea horse optimization, salp swarm algorithm, whale optimization algorithm, shuffled frog-leaping algorithm, and firefly algorithm. The comparative analysis is based on numerical results of performance metrics, demonstrating the robustness and effectiveness of each tuning method. Performance indices, including maximum overshoot, settling time and steady-state error are used to evaluate the robustness of the designed PI controllers. The frequency variations for the two-area thermal and PV power system are analyzed post-optimization, highlighting the superiority of the FLA-based PI controller in maintaining system stability under various operational conditions. The proposed FLA-based PI controller achieved a reduction in maximum overshoot by 28.3 %, a decrease in settling time by 23.4 %, and an improvement in steady-state error by 15.7 % compared to the next best-performing optimization method. These results demonstrate the significant advantages of the FLA in optimizing frequency regulation.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"18 ","pages":"Article 100539"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A note on “Study on multi-objective linear fractional programming problem involving pentagonal intuitionistic fuzzy number”","authors":"Hassan Hassanpour ,&nbsp;Javad Tayyebi","doi":"10.1016/j.rico.2025.100540","DOIUrl":"10.1016/j.rico.2025.100540","url":null,"abstract":"<div><div>This note presents a critical assessment of the paper authored by Sahoo et al. (2022) titled “<em>Study on multi-objective linear fractional programming problem involving pentagonal intuitionistic fuzzy number</em>” published in Results in Control and Optimization, volume 6, 100091. The note highlights a notable shortcoming in the equivalence between two optimization problems, which leads to two incorrect results, containing two theorems and the proposed method for solving intuitionistic fuzzy multi-objective linear fractional optimization problems.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"18 ","pages":"Article 100540"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An efficient parametric kernel function of IPMs for Linear optimization problems","authors":"Amrane Houas ,&nbsp;Fateh Merahi","doi":"10.1016/j.rico.2025.100537","DOIUrl":"10.1016/j.rico.2025.100537","url":null,"abstract":"<div><div>In this manuscript, we examine linear optimization problems formulated in the standard format. A novel kernel function is employed to devise a new interior-point algorithm for these problems. The proposed method reduces the number of iterations required for the Netlib test problems. The outcomes are subsequently derived using the self-dual embedding technique. The application of the kernel function facilitates the determination of search directions and the quantification of the distance between the current iteration and the <span><math><mi>μ</mi></math></span>-center of the algorithm. Incorporating specific lemmas tailored to this methodology is essential for establishing the optimal limit on iteration complexity. The methodology delineated in the work of K. Roos provides the framework for our investigation. Finally, numerical instances were examined to elucidate the theoretical findings and demonstrate the efficacy of the proposed innovative approach.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"18 ","pages":"Article 100537"},"PeriodicalIF":0.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143478881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective optimization of an open-pit mining system to determine safety buffer using the modified NBI method and the meta-model approach","authors":"Tahereh Khajvandsany ,&nbsp;Hossein Amoozad Khalili ,&nbsp;Ramezan Rezaeyan ,&nbsp;Kourosh Nemati","doi":"10.1016/j.rico.2025.100536","DOIUrl":"10.1016/j.rico.2025.100536","url":null,"abstract":"<div><h3>Background</h3><div>The open-pit mining industry plays a crucial role in the extraction of valuable minerals and resources, contributing significantly to global economies. However, the increasing complexity of mining operations, necessitates the adoption of advanced optimization techniques. A myriad of engineering problems include multiple conflicting objectives, which today are often solved by expensive simulation computations. Methods based on surrogate models are one of the approaches to solving this type of problem.</div></div><div><h3>Method</h3><div>This paper presents the multi-objective optimization in the extraction system of a copper mining complex using the normal boundary intersection (NBI) method and a meta-regression model to determine the economic lot-sizing. In this paper, three objective functions were considered including: (i) maximizing the amount of sulfide rock extraction, (ii) minimizing the total cost of the haulage system, and (iii) maximizing the total rocks loaded on trucks. The central composite design (CCD) method was used to develop the design of experiments (DOE) for the design variables.</div></div><div><h3>Results</h3><div>according to obtained findings, the considered design variables were the number of trucks of 120 tons, 240 tons, 35 tons, and 100 tons. The values of objectives considered in each combination of experiments were considered the response surface. A quadratic nonlinear regression model was determined for the objectives of maximizing the amount of sulfide rock extraction and minimizing the total costs of the haulage system and a linear regression model for the objective of maximizing the total rock loaded on trucks. The accuracy of the models was checked using the predicted residual error sum of squares (PRESS) and <span><math><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></math></span>. Moreover, the most common PRESS error was employed to validate the Meta-models. Subsequently, the multi-objective optimization problem was solved using the NBI method. Finally, Pareto solutions were provided using this approach, and they were discussed.</div></div><div><h3>Conclusion</h3><div>Implementing multi-objective optimization in open-pit mining using the modified NBI method and meta-model approach enhances decision-making by balancing safety buffers with operational efficiency. This strategic framework enables managers to minimize risks while maximizing resource extraction, ultimately leading to more sustainable mining practices.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"18 ","pages":"Article 100536"},"PeriodicalIF":0.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COVID-19 detection from optimized features of breathing audio signals using explainable ensemble machine learning","authors":"Shafrin Sultana ,&nbsp;A. B. M. Aowlad Hossain ,&nbsp;Jahangir Alam","doi":"10.1016/j.rico.2025.100538","DOIUrl":"10.1016/j.rico.2025.100538","url":null,"abstract":"<div><div>The automatic detection of COVID-19 using smartphone-recorded breathing signals in a ubiquitous and non-invasive way holds great promise. However, achieving accurate detection is challenging due to breathing signals' noisy and non-stationary nature, lack of distinguishable features, and imbalanced COVID/non-COVID data scenarios. This paper proposes an explainable ensemble learning-based framework for COVID-19 detection that extracts features from breathing signals through multiresolution analysis. First, we extract 165-dimensional features from the decomposed coefficients of a two-level discrete wavelet transformed (DWT) signal. From these, 27 optimized features are selected using the Recursive Feature Elimination with Cross-Validation (RFECV) technique. The level-2 DWT decomposed approximation coefficients retain frequencies in the 0–150 Hz range, aligning with human breathing frequencies. We utilize an ensemble model comprising decision trees, random forests, gradient boost, and XGBoost classifiers with a majority voting strategy for the detection task. A balanced and augmented dataset is prepared using the publicly available Coswara dataset. The results show that the ensemble approach improves accuracy compared to the individual models. Further, we explore the model's interpretability using Shapley additive explanations values, finding that the model places primary importance on features such as the RMS value, higher pitch of short-time Fourier transform, and higher frequency components of the Mel spectrogram, which align well with known COVID-related breathing characteristics. A comparison with related works demonstrates the effectiveness of our proposed feature extraction and ensemble framework, achieving an accuracy of 97.5 % and specificity of 95.24 %. These findings can potentially support smartphone-based COVID-19 detection applications using breathing signals.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"18 ","pages":"Article 100538"},"PeriodicalIF":0.0,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel approach in controlling the spread of a rumor within a crowd","authors":"Imane Dehaj ,&nbsp;Abdessamad Dehaj ,&nbsp;Abdessamad Tridane ,&nbsp;M.A. Aziz-Alaoui ,&nbsp;Mostafa Rachik","doi":"10.1016/j.rico.2025.100534","DOIUrl":"10.1016/j.rico.2025.100534","url":null,"abstract":"<div><div>The spread of rumors within a crowd can lead to harmful consequences, ranging from misinformation and social disturbances to public panic and injuries or fatalities. In this work, we propose a novel approach to an effective strategy for reducing the number of individuals affected by a rumor within a crowd. This strategy relies on developing control functions operating between zero and one, ensuring that the number of individuals affected by the rumor remains below a predetermined threshold at any given time. We analyze this strategy within the frameworks of continuous-time and discrete-time SIR models, which divide the population into Susceptible (S), Infectious (I), and Recovered (R) individuals, considering both practical constraints and theoretical limitations. Our results demonstrate that the proposed control functions ensure a gradual decrease in the number of affected and susceptible individuals over time, effectively limiting the spread of rumors and preventing uncontrollable situations. Numerical simulations illustrate the efficiency of this approach, highlighting its ability to achieve specific objectives in real-world scenarios.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"18 ","pages":"Article 100534"},"PeriodicalIF":0.0,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143202008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiple sclerosis diagnosis with brain MRI retrieval: A deep learning approach","authors":"R.M. Haggag ,&nbsp;Eman M. Ali ,&nbsp;M.E. Khalifa ,&nbsp;Mohamed Taha","doi":"10.1016/j.rico.2025.100533","DOIUrl":"10.1016/j.rico.2025.100533","url":null,"abstract":"<div><div>Multiple Sclerosis (MS) is an auto-immune disorder affecting the central nervous system, affecting 2.8 million people worldwide. Early diagnosis is crucial due to its profound social and economic impacts. MRI is commonly used for monitoring abnormalities. This study proposes a novel Content-Based Medical Image Retrieval (CBMIR) framework using Convolutional Neural Networks (CNN) and Transfer Learning (TL) for MS diagnosis using MRI data. Our framework utilizes The Inception V3 model that is pre-trained on ImageNet and RadImageNet datasets, and we modified the model by adding a new block of six layers to reduce the features’ dimensionality in the feature extraction phase. Fine-tuning the hyper-parameters for the whole system was done using the Bayesian optimizer. We experiment with Nine different distance metrics to measure query and database image similarity. Experiments on four public MS-MRI datasets demonstrated the end-to-end deep learning framework’s generalizability without extensive pre-processing, with mAP scores of 86.20%, 93.77%, 94.18%, and 90.46%, respectively demonstrating its effectiveness in retrieval. Moreover, a comparison with related CBMIR systems confirmed the effectiveness of our model.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"18 ","pages":"Article 100533"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143202007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparison study on optical character recognition models in mathematical equations and in any language","authors":"Sofi.A. Francis,&nbsp;M. Sangeetha","doi":"10.1016/j.rico.2025.100532","DOIUrl":"10.1016/j.rico.2025.100532","url":null,"abstract":"<div><div>Optical Character Recognition[OCR] is a technology that makes use of artificial intelligence and machine learning to extract readable text from documents, images, tags or any other type of sources. It allows one to convert characters and text objects into digital data that can be easily processed, analyzed, and modified. OCR can be applied to various types of languages in both written and spoken format. It can process everything from hand-written documents to typed-out text, making it a highly versatile technology. OCR makes use of a variety of algorithms and methods to process images, and then produces readable output, whatever language it is used for. This technology has the potential to be used for industries, banking, the medical field, security, and document storage among others. OCR faces significant challenges in accurately predicting language and mathematical expressions due to variations in handwriting styles, complex layouts, and the ambiguity of symbols. In this research, we propose assessing the results of different models that have been trained to identify an improved OCR system. The best OCR model is With the help of a decision tree model chosen.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"18 ","pages":"Article 100532"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integral invariant manifold method applied to a mathematical model of osteosarcoma","authors":"Ophir Nave","doi":"10.1016/j.rico.2025.100529","DOIUrl":"10.1016/j.rico.2025.100529","url":null,"abstract":"<div><div>In this study, an asymptotic method called the method of integral invariant manifold (MIM) was applied to a mathematical model of Osteosarcoma cancer. The mathematical model describes the interactions of the immune system cells and the osteosarcoma tumor. These interactions are described by nonlinear ordinary differential equations. By applying the MIM method, two critical dynamic variables (Cancer cells and Necrotic cells) were identified, providing insights into the dynamics of osteosarcoma over time during treatment.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"18 ","pages":"Article 100529"},"PeriodicalIF":0.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143202009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating consumers benefits in electronic-commerce using fuzzy TOPSIS","authors":"Rakesh Kumar ,&nbsp;Janjhyam Venkata Naga Ramesh ,&nbsp;Sachi Nandan Mohanty ,&nbsp;Muhammad Rafiq ,&nbsp;Iskandar Shernazarov ,&nbsp;M. Ijaz Khan","doi":"10.1016/j.rico.2025.100535","DOIUrl":"10.1016/j.rico.2025.100535","url":null,"abstract":"<div><div>Online platforms are preferred by customers when choosing and buying products. The contemporary digital era is witness to e-commerce platforms. Customers rely on various aspects of an e-commerce website for their needs. Sometimes they get more benefit from other digital platforms. This research focusses on how consumers can benefit from digital platforms. The study explores how consumers can embrace a website by leveraging various factors on an online business. The study identified five advantage factors, each offering six benefits, based on previous literature reviews. The study conducted interviews with E-marketing experts based on a questionnaire. This Research used Fuzzy TOPSIS method to determined ranking of most advantageous factor. The factors that provide advantages include usability, service quality, information quality, and online trust. The research analyses the ranking of these factors based on results. The research finds that usability of a website, i.e., functionality, efficiency, and search mechanism, is the most important advantage factor. This research is useful for businesspeople who engage in e-commerce platforms. The study explores future scope of research like cost, time, and regional issues on different products and regions.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"18 ","pages":"Article 100535"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}