Results in Control and Optimization最新文献

Optimal control of interactions between invasive alien and native species in a certain time period with the r-PINN approach 用 r-PINN 方法优化控制外来入侵物种和本地物种在一定时期内的相互作用

Results in Control and Optimization Pub Date : 2025-04-12 DOI: 10.1016/j.rico.2025.100557

Yudi Ari Adi , Danang A. Pratama , Maharani A. Bakar , Sugiyarto Surono , Suparman , Agung Budiantoro

{"title":"Optimal control of interactions between invasive alien and native species in a certain time period with the r-PINN approach","authors":"Yudi Ari Adi , Danang A. Pratama , Maharani A. Bakar , Sugiyarto Surono , Suparman , Agung Budiantoro","doi":"10.1016/j.rico.2025.100557","DOIUrl":"10.1016/j.rico.2025.100557","url":null,"abstract":"<div><div>The spread of invasive species poses a significant challenge to native biodiversity and ecosystem stability. An optimal control strategies to minimize the negative impacts of invasive species populations on native species and the ecosystem must be done in order to preserve the diversity in the ecosystem. This study proposes an optimal control framework to mitigate the impact of invasive species by enhancing native species preservation through a reaction–diffusion mathematical model. To solve the system efficiently, a restarting Physics-Informed Neural Network (r-PINN) is employed and benchmarked against the basic PINN. Numerical simulations reveal that r-PINN achieves a reduced training duration of 236.17 s compared to 289.18 s for the basic PINN, representing an 18.32% improvement in computational efficiency. Moreover, r-PINN demonstrates enhanced predictive accuracy, reducing the mean absolute error (MAE) by 4.12%, mean squared error (MSE) training loss by 12.04%, and MSE test loss by 5.11%. These results were validated against the Finite Difference Method (FDM), ensuring correctness of the proposed PINN-based approach. The implementation of the optimal control strategy led to a clear increase in native species populations and effective suppression of invasive species across spatial and temporal domains. Overall, the r-PINN framework offers a reliable and computationally efficient tool for solving nonlinear ecological models involving spatiotemporal control of species populations.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"19 ","pages":"Article 100557"},"PeriodicalIF":0.0,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuzzy TOPSIS technique for multi-criteria group decision-making: A study of crude oil price

Results in Control and Optimization Pub Date : 2025-04-09 DOI: 10.1016/j.rico.2025.100565

Sandhya Priya Baral, Prashanta Kumar Parida, Diptirekha Sahoo

引用次数: 0

Factors influencing women's participation in SHGs: An empirical evidence from mayurbhanj district of Odisha, India

Results in Control and Optimization Pub Date : 2025-04-09 DOI: 10.1016/j.rico.2025.100559

Itishree Panda , Damodar Jena , S Naveen , Saismita Swain , Shusrisangeeta Das

引用次数: 0

The maximum range method for finding initial basic feasible solution for transportation problems

Results in Control and Optimization Pub Date : 2025-04-08 DOI: 10.1016/j.rico.2025.100551

Fredrick Asenso Wireko, Ignatius Dennis Kwesi Mensah, Emmanuel Nii Apai Aborhey, Samuel Adu Appiah, Charles Sebil, Joseph Ackora-Prah

{"title":"The maximum range method for finding initial basic feasible solution for transportation problems","authors":"Fredrick Asenso Wireko, Ignatius Dennis Kwesi Mensah, Emmanuel Nii Apai Aborhey, Samuel Adu Appiah, Charles Sebil, Joseph Ackora-Prah","doi":"10.1016/j.rico.2025.100551","DOIUrl":"10.1016/j.rico.2025.100551","url":null,"abstract":"<div><div>The transportation problem is an essential branch of mathematics that industries use to minimize costs. The transportation problem is an optimization technique suitably modeled using linear programming. To obtain an optimal solution to the transportation problem, first compute the initial basic feasible solution, which is then subsequently optimized. Several algorithms, like Vogel’s approximation method, maximum difference extreme difference method, demand-based allocation method, and others, are used in literature to determine the initial basic feasible solution to these transportation problems. This paper proposes a robust algorithm that can produce an initial basic feasible solution asymptotic to the optimal solution. The study further carried out a performance analysis by comparing the proposed algorithm’s results with those of some existing algorithms. The observation was that the proposed algorithm in many cases produced an optimal initial basic feasible solution (IBFS) for both balanced and unbalanced transportation problems and also tend to have a very high average of correctness percentage compared to some existing algorithms.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"19 ","pages":"Article 100551"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributionally robust Lyapunov–Barrier Networks for safe and stable control under uncertainty

Results in Control and Optimization Pub Date : 2025-04-06 DOI: 10.1016/j.rico.2025.100556

Ali Baheri

引用次数: 0

Interactive cardiovascular disease prediction system using learning techniques: Insights from extensive experiments

Results in Control and Optimization Pub Date : 2025-04-06 DOI: 10.1016/j.rico.2025.100560

Purnima Pal , Harsh Vikram Singh , Veena Grover , R. Manikandan , Rasoul Karimi , Mohammad Khishe

{"title":"Interactive cardiovascular disease prediction system using learning techniques: Insights from extensive experiments","authors":"Purnima Pal , Harsh Vikram Singh , Veena Grover , R. Manikandan , Rasoul Karimi , Mohammad Khishe","doi":"10.1016/j.rico.2025.100560","DOIUrl":"10.1016/j.rico.2025.100560","url":null,"abstract":"<div><div>In Today's medical field, cardiovascular disease prediction is a significant challenge due to the influence of multiple variables affecting the circulatory system, such as hypertension, hyperlipidemia, and irregular pulse rates. Accurately classifying cardiac diseases proves to be a complex task. Consequently, the deep and machine learning techniques hold substantial potential for facilitating early identification. In this research paper, we explore the effectiveness of various models of machine learning, ensemble machine learning, and deep learning for predicting heart disease. These models undergo comprehensive experiments and cross-validation to evaluate their performance. To prepare the dataset, we apply standard scaling to numerical features, aligning them on a similar scale and enhancing the performance of specific learning algorithms. Our results demonstrate that deep learning models achieve high accuracy and robustness in predicting cardiovascular disease risk, with the InceptionNet model achieving an impressive 98.89 % accuracy. Additionally, ensemble learning models also show promise, with the Random Forest model delivering competitive accuracy, effectively capturing attributes and temporal dependencies within cardiovascular disease data. The findings of this study underscore the possibilities of deep learning and ensemble machine learning approaches in accurately predicting heart disease risk. Ultimately, this contributes to improved patient care and reduced mortality rates amidst the rising prevalence of heart-related conditions.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"19 ","pages":"Article 100560"},"PeriodicalIF":0.0,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Portfolio optimization with MOPSO-Shrinkage hybrid model

Results in Control and Optimization Pub Date : 2025-03-28 DOI: 10.1016/j.rico.2025.100553

Minh Tran, Nhat M. Nguyen

{"title":"Portfolio optimization with MOPSO-Shrinkage hybrid model","authors":"Minh Tran, Nhat M. Nguyen","doi":"10.1016/j.rico.2025.100553","DOIUrl":"10.1016/j.rico.2025.100553","url":null,"abstract":"<div><div>This paper introduces a novel framework for portfolio optimization that integrates Multi-Objective Particle Swarm Optimization (MOPSO) with shrinkage covariance estimators, referred to as the MOPSO-Shrinkage hybrid model. The main contribution of this study lies in combining the adaptive search capabilities of evolutionary algorithms with robust covariance estimation techniques to enhance portfolio allocation in mature financial markets. Unlike traditional shrinkage covariance models, which struggle in highly dynamic environments, our hybrid model optimally selects stocks and improves risk-adjusted returns. Empirical analysis on US stock market data from 2013 to 2023 demonstrates that MOPSO-Shrinkage models consistently outperform traditional shrinkage models, achieving higher returns, lower volatility, and superior Sharpe ratios. Among the hybrid models, MOPSO-SSIM exhibits the best performance, with an average annual return of 18.86% and a Sharpe ratio of 1.27, while significantly reducing portfolio risk. Rigorous statistical tests confirm the robustness of the model, showing that MOPSO-Shrinkage significantly outperforms traditional methods. These findings suggest that the proposed approach is well-suited for traders seeking higher risk-adjusted returns and portfolio stability in volatile markets.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"19 ","pages":"Article 100553"},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767865","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}

引用次数: 0

On the modeling and stability analysis of fractional typhoid fever model with optimal control

Results in Control and Optimization Pub Date : 2025-03-27 DOI: 10.1016/j.rico.2025.100552

Ayuba Sanda , M.R. Odekunle , Abdulfatai Atte Momoh , Déthié Dione

{"title":"On the modeling and stability analysis of fractional typhoid fever model with optimal control","authors":"Ayuba Sanda , M.R. Odekunle , Abdulfatai Atte Momoh , Déthié Dione","doi":"10.1016/j.rico.2025.100552","DOIUrl":"10.1016/j.rico.2025.100552","url":null,"abstract":"<div><div>Typhoid fever remains a major public health hazard on a global scale. It is primarily transmitted by contaminated food and water, particularly in places with poor sanitation. This work presents a novel deterministic fractional model for the dynamics of typhoid fever transmission that considers memory and genetic influences using the Atangana–Baleanu derivative. This work uses fractional calculus to show the dynamics of typhoid transmission while accounting for factors such as environmental contamination and the emergence of drug-resistant variants. Requirements for the global asymptotic stability of both endemic and disease-free equilibria are developed by a thorough stability analysis, providing a theoretical basis for comprehending the thresholds required to reduce or eliminate typhoid fever. A comprehensive sensitivity investigation identifies key parameters influencing the basic reproduction number <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>. The application of optimal control theory, which demonstrates that the best outcomes in reducing the burden of disease are achieved when vaccination, treatment, and personal hygiene are integrated, also makes it possible to evaluate various intervention choices. The practical significance of the model for public health authorities is illustrated by numerical simulations that compare the model’s predictions with actual epidemiological data. The value of the model for public health professionals is highlighted by numerical statistics. The application of optimal control theory, which demonstrates that the best outcomes in reducing the burden of disease are achieved when vaccination, treatment, and personal hygiene are integrated, also makes it possible to evaluate various intervention choices.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"19 ","pages":"Article 100552"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784008","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}

引用次数: 0

Experimental realization of PSO-based hybrid adaptive sliding mode control for force impedance control systems

Results in Control and Optimization Pub Date : 2025-03-22 DOI: 10.1016/j.rico.2025.100548

Sarucha Yanyong, Somyot Kaitwanidvilai

引用次数: 0

A novel swarm intelligence-based approach for solving optimal power flow problems in modern power systems

Results in Control and Optimization Pub Date : 2025-03-22 DOI: 10.1016/j.rico.2025.100555

Haewon Byeon , Wajdi Alghamdi , Munni Evin , M. Sucharitha , D. David Neels Ponkumar , A. Prakash , J. Sunil

{"title":"A novel swarm intelligence-based approach for solving optimal power flow problems in modern power systems","authors":"Haewon Byeon , Wajdi Alghamdi , Munni Evin , M. Sucharitha , D. David Neels Ponkumar , A. Prakash , J. Sunil","doi":"10.1016/j.rico.2025.100555","DOIUrl":"10.1016/j.rico.2025.100555","url":null,"abstract":"<div><div>Optimal power flow is the major concern with electric power systems. According to the OPF issue solution, the most suitable points are the compensator output, transformer tap, generator voltage, and generator output powers. In order to solve the optimum power flow difficulties related to the C-UPFC, this research suggested an Improved Pelican optimization algorithm (IPOA). Install the superior flexible AC transmission system (FACTS) in accordance with the transmission line (TL) in a series configuration to provide independent voltage control that combines power flow regulation. The suggested POA method ensures efficiency over the conventional IEEE 57 bus systems. Define the emission fuel cost and fuel cost during the C-UPFC installation. The suggested C-UPFC, when integrated optimally, considerably improves the voltage profile by decreasing power loss, as shown in the experiments. In addition, compared to all previous methods, the suggested algorithm produces superior outcomes, with the suggested method, you may expect to pay 202 tons per hour for emissions and 799.56 tons per hour for gasoline.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"19 ","pages":"Article 100555"},"PeriodicalIF":0.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705252","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}

引用次数: 0