Results in Control and Optimization最新文献

{"title":"Runge–Kutta method for solving uncertain fractional differential equations","authors":"Keying Yang,&nbsp;Zhi Li,&nbsp;Liping Xu","doi":"10.1016/j.rico.2026.100741","DOIUrl":"10.1016/j.rico.2026.100741","url":null,"abstract":"<div><div>In this paper, a new numerical method for solving Caputo-type uncertain fractional differential equations (UFDEs) is designed based on the fractional order Runge–Kutta 4-step method. The UFDEs are transformed into corresponding fractional differential equations via the <span><math><mi>α</mi></math></span>-path approach, and the numerical approximate solutions are obtained by traversing different <span><math><mi>α</mi></math></span>-paths using the fractional order Runge–Kutta 4-step method. Furthermore, a formula for calculating the expected value of monotonic functions of UFDE solutions is derived based on the inverse uncertainty distribution extracted from <span><math><mi>α</mi></math></span>-paths. Finally, several numerical experiments are conducted to demonstrate the effectiveness and accuracy of the proposed method under typical conditions, while acknowledging the aforementioned dependencies.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"23 ","pages":"Article 100741"},"PeriodicalIF":3.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178546","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}

{"title":"A neurodynamical neural network approach for solving support vector machine problems with generalized pinball loss functions via hyperbolic tangent smoothing technique","authors":"Rattanaporn Wangkeeree ,&nbsp;Rabian Wangkeeree ,&nbsp;Yirga Abebe Belay ,&nbsp;Kasamsuk Ungchittrakool","doi":"10.1016/j.rico.2026.100742","DOIUrl":"10.1016/j.rico.2026.100742","url":null,"abstract":"<div><div>In this paper, we propose a neurodynamical neural network approach for finding solutions of support vector machines involving hyperbolic tangent smoothed generalized pinball loss function. First, we use the hyperbolic tangent smoothing technique to find a smooth approximation of the non-smooth generalized pinball loss function associated with the SVM problem. Then we propose a continuous-time neurodynamical neural network model which enables us to find continuous real-time solutions of the given support vector machine problem with smooth objective function. We rigorously prove the equivalence between the set of solutions of the hyperbolic tangent smoothed optimization problem and the set of equilibrium points of the neural network dynamics. Then we analyze the existence and uniqueness of solutions and different stability properties of the introduced model. Next we conduct numerical simulations both on synthetic and benchmark real-world datasets to show the effectiveness of the proposed method. We then evaluate effectiveness of the proposed model using different performance metrics. Finally, we compare our model against three existing benchmark models. A Friedman test was employed to examine the statistical differences in performance of the proposed model against the benchmark methods. Our model surpasses the traditional benchmark models in the sense that it achieves better performance with real-time solutions and guarantees global convergence properties.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"23 ","pages":"Article 100742"},"PeriodicalIF":3.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178550","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}

{"title":"Dynamic resource allocation and coordination framework for multi-robot systems in grid-based environments","authors":"Sheng-Wei Sim ,&nbsp;Chee-Henn Chng ,&nbsp;Chung-Men Tan ,&nbsp;Danny Wee-Kiat Ng ,&nbsp;Shalini Darmaraju ,&nbsp;Jen-Hahn Low ,&nbsp;Soung-Yue Liew ,&nbsp;Choon-Hian Goh","doi":"10.1016/j.rico.2026.100745","DOIUrl":"10.1016/j.rico.2026.100745","url":null,"abstract":"<div><div>This paper proposes DyDRAM, a centralized hierarchical coordination framework for lifelong multi-robot operation in grid-based Rack-to-Person warehouses. DyDRAM combines congestion-aware global routing with execution-level conflict handling in a single pipeline. The global planner uses weighted A* with directional constraints, tabu mechanisms, and a heat map cost layer to reduce repeated bottleneck usage, while execution is coordinated through sliding-window reservations and conflict-driven responses to short-horizon conflicts. The framework is evaluated against Windowed Hierarchical Cooperative A* (WHCA*), a Large Neighborhood Search MAPF solver (LNS2), and Enhanced Edge Conflict-Based Search (EECBS) under matched task generation, robot dynamics, and fixed method-specific planning policies. In the baseline long-corridor warehouse layout, DyDRAM with heat map shows the highest observed mean throughput across the tested 10–70-robot range, increasing from approximately 271 to 1126 tasks/hour, while maintaining the lowest observed mean task completion time, from approximately 130 to 215 s. At the highest tested density, the method improves throughput by about 35.5% over the best-performing baseline in throughput and reduces mean task completion time by about 12.8–28.6% relative to the tested baselines. Additional evaluations on a reduced-crossover layout and a scaled-down layout show that the method remains effective within the tested long-corridor warehouse family, with the largest gains appearing when rerouting flexibility is limited and shared-corridor congestion persists. These results are bounded to the fixed simulation protocol, and formal paired significance analysis is reported for the key high-density baseline comparisons.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"23 ","pages":"Article 100745"},"PeriodicalIF":3.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178459","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}

{"title":"A modified restart strategy for a derivative-free projection method with three-term conjugate-gradient direction for monotone equations and application","authors":"Muhammad Salisu Sarkinbai ,&nbsp;Muhammad Abdullahi ,&nbsp;Auwal Bala Abubakar ,&nbsp;Seyed Yaser Mousavi Siamakani","doi":"10.1016/j.rico.2026.100733","DOIUrl":"10.1016/j.rico.2026.100733","url":null,"abstract":"<div><div>Image recovery is one of the most important scientific applications, with broad relevance across science and engineering. Motivated by existing three-term methods for unconstrained optimization, we propose a modified derivative-free three-term method with a restart strategy for solving large-scale convex-constrained monotone nonlinear equations. Unlike prior approaches, the proposed method integrates a projection technique into the iterative framework, while the restart strategy ensures key convergence properties, namely sufficient descent and boundedness of the search direction. Moreover, the global convergence of the proposed method was established under some appropriate assumptions. Numerical experiments on benchmark problems demonstrate the strength of the proposed algorithm, showing that it is more robust and efficient than existing approaches, as presented in the numerical section. Finally, we apply the proposed method to recover blurred images, illustrating its applicability to image restoration problems.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"23 ","pages":"Article 100733"},"PeriodicalIF":3.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178460","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}

{"title":"A novel hybrid Zebra-Archimedes Optimization Algorithm for DG placement and sizing: Single and multi-objective performance analysis","authors":"Prisma Megantoro ,&nbsp;Syahirah Abd Halim ,&nbsp;Muhammad Rafi Nabil Arsalan ,&nbsp;Makbul A.M. Ramli ,&nbsp;Nor Azwan Mohamed Kamari ,&nbsp;Ahmad H. Milyani","doi":"10.1016/j.rico.2026.100716","DOIUrl":"10.1016/j.rico.2026.100716","url":null,"abstract":"<div><div>This study proposes a novel hybrid Zebra-Archimedes Optimization Algorithm (Z-AOA) for optimal distributed generation (DG) placement and sizing in the IEEE 57-bus system. The Z-AOA merges the strong exploration ability of the Zebra Optimization Algorithm (ZOA) with the precise exploitation capability of the Archimedes Optimization Algorithm (AOA). This hybridization allows the algorithm to search widely for potential solutions while fine-tuning them effectively. The unique strength of Z-AOA lies in its adaptive switching mechanism between exploration and exploitation phases, and ensuring optimal balance throughout the optimization process. This characteristic makes Z-AOA particularly effective for complex, multi-dimensional power system optimization problems. The optimization framework incorporates essential technical constraints, including generator capacity limits, bus voltage magnitude thresholds, and reactive power boundaries. Single-objective variants independently minimize active power loss, voltage deviation, and voltage stability index. Meanwhile, multi-objective variants simultaneously minimize both power loss and voltage deviation using weighted sum method with Pareto front analysis. Simulation results validate the superiority of the proposed Z-AOA, achieving 52.72% power loss reduction and 99.9% voltage regulation compliance. This performance outperforms standalone AOA, which achieves 51.91% reduction and 98.2% compliance. The multi-objective Z-AOA provides balanced trade-offs with 48.54% power loss reduction and 30.20% voltage deviation reduction. Comprehensive comparisons against various categories of metaheuristic algorithms demonstrate the competitive performance of the proposed approach. These categories include swarm-based, physics-based, and evolutionary-based methods. Wilcoxon signed-rank statistical tests further confirm the significant performance advantage of the novel Z-AOA across all optimization objectives.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"23 ","pages":"Article 100716"},"PeriodicalIF":3.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178386","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}

{"title":"Climate-adaptive optimization of poultry house ventilation using model predictive control and TRNSYS–Python Co-simulation","authors":"Hamza Benzzine ,&nbsp;Rachid El Bouayadi ,&nbsp;Driss Zejli ,&nbsp;Yasmine Achour","doi":"10.1016/j.rico.2026.100755","DOIUrl":"10.1016/j.rico.2026.100755","url":null,"abstract":"<div><div>This study proposes a TRNSYS–Python co-simulation framework for optimizing mechanical ventilation in poultry buildings under heterogeneous climatic conditions. The approach integrates a dynamic building model with a Model Predictive Control (MPC) strategy, enabling real-time data exchange and closed-loop control to simultaneously regulate indoor temperature, humidity, and energy consumption. The framework is applied to broiler poultry houses, where four ventilation configurations (Cross, Tunnel, Longitudinal, and Combined) are evaluated across six representative Moroccan climatic zones ranging from temperate coastal to hot desert environments. Simulation results demonstrate that the proposed MPC-based strategy significantly improves indoor environmental conditions by reducing Temperature–Humidity Index (THI) exceedance duration and enhancing setpoint tracking performance under diverse weather conditions. The findings highlight the dominant influence of climatic forcing on both microclimate regulation and energy demand: in cooler regions, total energy consumption remains below 1600 kWh, while THI exceedance still reaches 430–445 h; conversely, in hot semi-arid and desert climates, energy demand exceeds 3500–4000 kWh, largely driven by humidification loads above 3300 kWh. Pareto analysis shows a trade-off between energy use and thermal comfort. The Combined configuration reduces thermal stress but increases energy consumption, while Cross and Tunnel provide more efficient compromises. These results support climate-adaptive recommendations: Cross ventilation in coastal zones, Tunnel in cooler inland regions, and Combined systems in arid climates with high thermal stress. Although validated in Morocco, the framework is generalizable to other regions and building types. Overall, the MPC-based co-simulation approach is an effective and scalable tool for ventilation design and control.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"23 ","pages":"Article 100755"},"PeriodicalIF":3.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148241169","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}

{"title":"Enhanced Ant Lion Optimizer for cascade (1+PI)-PIDF control in hydro-thermal power systems with capacitive storage","authors":"Wassana Kasemsin ,&nbsp;Sitthisak Audomsi ,&nbsp;Worawat Sa-Ngiamvibool ,&nbsp;Sarawoot Boonkirdram","doi":"10.1016/j.rico.2026.100749","DOIUrl":"10.1016/j.rico.2026.100749","url":null,"abstract":"<div><div>It is becoming harder to keep the frequency steady in linked power systems since demand is changing in ways that can not be expected, the systems are getting more complicated, and more renewable energy sources are being used. When there are random disturbances and fast changes in load, classic controllers like PI and PID typically do not work as well as they might because of how the system works. This work introduces a cascade (1+PI)-PIDF controller for automated generation control (AGC) in a two-area hydro-thermal power system using capacitive energy storage (CES). The suggested cascade architecture keeps global ACE control independent from local frequency stability. This improves transient responsiveness and steady-state performance compared to standard single-loop controllers. The EALO approach makes global search operate better and stops controller settings from converging too quickly by using random perturbation and adaptive exploration. MATLAB/Simulink examines how effectively the controller operates with step load disturbances by using four objective functions: IAE, ITAE, ISE, and ITSE. The optimization process is done across 30 different trials, each with 50 iterations, to make sure the results are statistically sound. The simulation results show that the suggested cascade controller makes controlling load frequency much better. The suggested technique gets an ITAE value of 0.0068 in the ITAE-based optimization context. The regular PIDF controller gets 0.0643, while the 1+PI controller gets 0.2540. After a step load disruption, the time it takes for the frequency to settle goes from around 14 s to about 3–4 s.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"23 ","pages":"Article 100749"},"PeriodicalIF":3.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178388","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}

{"title":"Secure and robust blind watermarking for medical images using convolutional neural networks and discrete wavelet transform integration","authors":"Rania Hamami ,&nbsp;Narima Zermi ,&nbsp;Larbi Boubchir ,&nbsp;Amine Khaldi","doi":"10.1016/j.rico.2026.100750","DOIUrl":"10.1016/j.rico.2026.100750","url":null,"abstract":"<div><div>The increasing digitization of data offers significant advantages, particularly in digital images, which continue to grow rapidly due to advances in image acqui sition systems. However, this transition also presents significant security risks, necessitating robust methods to protect digital images from illegal manipulation. While existing watermarking approaches for medical imaging often struggle to balance robustness, computational efficiency, and diagnostic integrity, this study introduces a robust blind watermarking technique that integrates cryptographic hashing, deep learning, and frequency-domain embedding to address these lim itations. The methodology leverages the Haar wavelet transform to embed a binary watermark, generated from personal information using the secure hashing algorithm SHA-256, into the HH2 sub-band of the red channel. A convolutional neural network-based model optimized watermark insertion and facilitated blind extraction. Comprehensive evaluations across diverse medical imaging modalities, including MRI, ultrasound, and CT scans, demonstrate excellent performance with peak signal-to-noise ratio (PSNR) values exceeding 41 dB achieving up to 43.13 dB for fundus images, representing a 3.1% improvement over the best exist ing method and structural similarity (SSIM) values around 0.94. The proposed method shows significant speed improvements of approximately 75% compared to state-of-the-art techniques, with embedding and extraction times of 0.4430 sec onds and 0.1800 s, respectively, compared to 1.5 s total processing time of recent deep learning methods. These findings underscore the technique’s potential for telemedicine applications, where maintaining high image quality and security is paramount for accurate diagnosis.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"23 ","pages":"Article 100750"},"PeriodicalIF":3.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178389","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}

{"title":"RETRACTED: Support Vector Regression for Adaptive Endpoint Detection in Automated Potentiometric Titration: A Methodological Integration of Machine Learning and Process Control","authors":"Junmei Huang,&nbsp;Qiang Ma,&nbsp;Feiyu Huang,&nbsp;Ruihan Zhang,&nbsp;Xiangfo Luo,&nbsp;Weijun Dang,&nbsp;Pengtao Li","doi":"10.1016/j.rico.2026.100698","DOIUrl":"10.1016/j.rico.2026.100698","url":null,"abstract":"<div><div>This article has been retracted: please see Elsevier Policy on Article Withdrawal (<span><span>https://www.elsevier.com/about/policies/article-withdrawal</span><svg><path></path></svg></span>).</div><div>This article has been retracted at the request of the authors and with the approval of the Editor-in-Chief.</div><div>Following acceptance and online availability of this article as a pre-proof, a substantive concern was raised by one of the co-authors regarding the reliability of the data analysis, specifically relating to the convergence of results presented in the study. The author indicated that the findings require further verification and validation to ensure their robustness.</div><div>Given that this issue affects the scientific validity of the key results and conclusions, the authors collectively requested retraction of the article.</div><div>The authors apologize for any inconvenience caused.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"23 ","pages":"Article 100698"},"PeriodicalIF":3.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148539543","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":"Retraction notice to “Customer Satisfaction in Peer-to-Peer Lending Platforms: A Text Mining and Sentiment Analysis Approach” [RICO (2025) 100598]","authors":"Sarasanabelli Prasanna Kumari,&nbsp;Ali B.M. Ali,&nbsp;Madhusmita Mohanty,&nbsp;Bibhuti Bhusan Dash,&nbsp;Muhammad Rafiq,&nbsp;Sachi Nandan Mohanty,&nbsp;Iskandar Shernazarov,&nbsp;Nashwan Adnan Othman,&nbsp;Nadia Batool","doi":"10.1016/j.rico.2026.100684","DOIUrl":"10.1016/j.rico.2026.100684","url":null,"abstract":"","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"23 ","pages":"Article 100684"},"PeriodicalIF":3.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148539676","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}