Pub Date : 2025-06-21DOI: 10.1016/j.rico.2025.100582
Mostafa Kadiri , Mohammed Louaked , Houari Mechkour
In this paper, we present a mathematical formulation of an optimal design problem related to a vertical slot fishway. The work involves modeling, mathematical analysis and numerical approximation of a coupled problem between a primal hyperbolic system and adjoint problem of shallow water for the cost function of the optimal structure. We express the shape gradient of the cost function by introducing the associated adjoint state system. We proceed with the study of the adjoint system by using the Lax symbolic symmetrizer for hyperbolic systems and pseudo-differential techniques. The numerical resolution of this problem combines two main approaches: The first one relies on the finite volume method with the Roe solver for the spatial and temporal discretization, and the second one uses a minimizing algorithm, the gradient of the objective function, evaluated by an adjoint problem. Numerical simulations are given which illustrate the accuracy of this technique.
{"title":"Optimal design of vertical slot fishways by using shallow water equations","authors":"Mostafa Kadiri , Mohammed Louaked , Houari Mechkour","doi":"10.1016/j.rico.2025.100582","DOIUrl":"10.1016/j.rico.2025.100582","url":null,"abstract":"<div><div>In this paper, we present a mathematical formulation of an optimal design problem related to a vertical slot fishway. The work involves modeling, mathematical analysis and numerical approximation of a coupled problem between a primal hyperbolic system and adjoint problem of shallow water for the cost function of the optimal structure. We express the shape gradient of the cost function by introducing the associated adjoint state system. We proceed with the study of the adjoint system by using the Lax symbolic symmetrizer for hyperbolic systems and pseudo-differential techniques. The numerical resolution of this problem combines two main approaches: The first one relies on the finite volume method with the Roe solver for the spatial and temporal discretization, and the second one uses a minimizing algorithm, the gradient of the objective function, evaluated by an adjoint problem. Numerical simulations are given which illustrate the accuracy of this technique.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100582"},"PeriodicalIF":0.0,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502346","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}
Positioning a load in a two-dimensional subspace requires a two-degrees-of-freedom (2 DoF) position control system. The precise positioning of the load has been the driving motivation for electro-hydraulic actuation and its robust control. 2 DoF electro-hydraulic servo system (EHSS) is complex and nonlinear. Each of the 2 DoF is approximated by the second order model with uncertainty. A new sliding variable is proposed for precise finite-time positioning of a load. The extended state observer based controller is devised using higher-order sliding modes. Uncertainties and states are estimated to implement the controller in a finite time. The method is verified in both simulation and experiment. It is shown that the proposed method yield robust and precise positioning of load in two-dimensional subspace.
{"title":"Extended state observer based output feedback control of 2 DoF electro hydraulic servo system","authors":"Ashpana Shiralkar , Shailaja Kurode , Bhagyashri Tamhane","doi":"10.1016/j.rico.2025.100588","DOIUrl":"10.1016/j.rico.2025.100588","url":null,"abstract":"<div><div>Positioning a load in a two-dimensional subspace requires a two-degrees-of-freedom (2 DoF) position control system. The precise positioning of the load has been the driving motivation for electro-hydraulic actuation and its robust control. 2 DoF electro-hydraulic servo system (EHSS) is complex and nonlinear. Each of the 2 DoF is approximated by the second order model with uncertainty. A new sliding variable is proposed for precise finite-time positioning of a load. The extended state observer based controller is devised using higher-order sliding modes. Uncertainties and states are estimated to implement the controller in a finite time. The method is verified in both simulation and experiment. It is shown that the proposed method yield robust and precise positioning of load in two-dimensional subspace.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100588"},"PeriodicalIF":0.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366591","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}
Pub Date : 2025-06-19DOI: 10.1016/j.rico.2025.100585
Haldi Budiman , Shir Li Wang , Theam Foo Ng , Amr S. Ghoneim , Haidi Ibrahim , Bahbibi Rahmatullah
Differential Evolution (DE) is extensively applied due to its simplicity, robustness, and computational efficiency. However, the performance of DE is influenced by several factors, including the nature of the problem, the specific algorithm variant, and user-defined settings. Numerous studies have explored adaptive parameter settings to reduce the sensitivity of DE’s performance to user inputs, parameter choices, and problem characteristics. DE’s ability to find optimal solutions depends on offspring generation and population diversity. One of the ways to improve DE’s population diversity is by adjusting the population size, either by introducing new individuals or eliminating existing ones. This work investigates the adaptation of population sizing of a self-adaptive differential evolution algorithm called Self-Adaptive Ensemble-based DE with Enhanced Population Sizing (SAEDE-EP). The adaptation of population sizing in SAEDE-EP is influenced by two parameters: the threshold value for stagnation comparison of the best individual over generations and the population size’s growth rate. The effect of these two parameters on population sizing adaptation is evaluated using 26 benchmark single-objective unconstrained optimization functions consisting of unimodal, multimodal, hybrid, and composition functions. SAEDE-EP is compared against 18 state-of-the-art evolutionary algorithms on 10 functions from the 100-Digit Challenge on CEC 2019 single-objective real parameter optimization. Additionally, SAEDE-EP is tested on 57 problems from the CEC-2020 Competitions on Real-World Single Objective Constrained Optimization. Comparative analysis indicates that SAEDE-EP performs well in single-objective unconstrained optimization problems with various characteristics and solves 86% of the real-world single-objective constrained optimization, requiring less computational time and less exhaustive effort to set parameters.
{"title":"A study of adaptive population sizing in a self-adaptive differential evolution","authors":"Haldi Budiman , Shir Li Wang , Theam Foo Ng , Amr S. Ghoneim , Haidi Ibrahim , Bahbibi Rahmatullah","doi":"10.1016/j.rico.2025.100585","DOIUrl":"10.1016/j.rico.2025.100585","url":null,"abstract":"<div><div>Differential Evolution (DE) is extensively applied due to its simplicity, robustness, and computational efficiency. However, the performance of DE is influenced by several factors, including the nature of the problem, the specific algorithm variant, and user-defined settings. Numerous studies have explored adaptive parameter settings to reduce the sensitivity of DE’s performance to user inputs, parameter choices, and problem characteristics. DE’s ability to find optimal solutions depends on offspring generation and population diversity. One of the ways to improve DE’s population diversity is by adjusting the population size, either by introducing new individuals or eliminating existing ones. This work investigates the adaptation of population sizing of a self-adaptive differential evolution algorithm called Self-Adaptive Ensemble-based DE with Enhanced Population Sizing (SAEDE-EP). The adaptation of population sizing in SAEDE-EP is influenced by two parameters: the threshold value for stagnation comparison of the best individual over generations and the population size’s growth rate. The effect of these two parameters on population sizing adaptation is evaluated using 26 benchmark single-objective unconstrained optimization functions consisting of unimodal, multimodal, hybrid, and composition functions. SAEDE-EP is compared against 18 state-of-the-art evolutionary algorithms on 10 functions from the 100-Digit Challenge on CEC 2019 single-objective real parameter optimization. Additionally, SAEDE-EP is tested on 57 problems from the CEC-2020 Competitions on Real-World Single Objective Constrained Optimization. Comparative analysis indicates that SAEDE-EP performs well in single-objective unconstrained optimization problems with various characteristics and solves 86% of the real-world single-objective constrained optimization, requiring less computational time and less exhaustive effort to set parameters.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100585"},"PeriodicalIF":0.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470001","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}
Pub Date : 2025-06-19DOI: 10.1016/j.rico.2025.100586
Maissa Farhat , Azzeddine Dekhane , Abdelhak Djellad , Maen Takruri , Aws Al-Qaisi , Oscar Barambones
The accurate prediction of the Maximum Power Point (PMPP) in photovoltaic (PV) systems is critical for optimizing energy yield and enhancing solar energy harvesting efficiency. This study explores the application of data-driven methods to improve PMPP prediction, utilizing advanced regression techniques such as Ridge Regression, Lasso Regression, Decision Tree Regression, and Random Forest Regression. By analyzing a dataset of irradiance, temperature, and PMPP measurements, the research compares the performance of these models in capturing complex nonlinear relationships between key variables. Results indicate that tree-based models, particularly Random Forest Regression, outperform linear models, demonstrating superior predictive accuracy and robustness. Feature importance analysis further highlights the dominant influence of irradiance (GPOA) on PMPP, emphasizing the value of precise irradiance data. These findings underscore the potential of machine learning techniques in optimizing PV system performance. Future research should focus on integrating additional features, such as atmospheric conditions and panel characteristics, and exploring deep learning methods to enhance prediction accuracy further.
{"title":"Optimizing photovoltaic performance: Data-driven maximum power point prediction via advanced regression models","authors":"Maissa Farhat , Azzeddine Dekhane , Abdelhak Djellad , Maen Takruri , Aws Al-Qaisi , Oscar Barambones","doi":"10.1016/j.rico.2025.100586","DOIUrl":"10.1016/j.rico.2025.100586","url":null,"abstract":"<div><div>The accurate prediction of the Maximum Power Point (PMPP) in photovoltaic (PV) systems is critical for optimizing energy yield and enhancing solar energy harvesting efficiency. This study explores the application of data-driven methods to improve PMPP prediction, utilizing advanced regression techniques such as Ridge Regression, Lasso Regression, Decision Tree Regression, and Random Forest Regression. By analyzing a dataset of irradiance, temperature, and PMPP measurements, the research compares the performance of these models in capturing complex nonlinear relationships between key variables. Results indicate that tree-based models, particularly Random Forest Regression, outperform linear models, demonstrating superior predictive accuracy and robustness. Feature importance analysis further highlights the dominant influence of irradiance (GPOA) on PMPP, emphasizing the value of precise irradiance data. These findings underscore the potential of machine learning techniques in optimizing PV system performance. Future research should focus on integrating additional features, such as atmospheric conditions and panel characteristics, and exploring deep learning methods to enhance prediction accuracy further.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100586"},"PeriodicalIF":0.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489372","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}
There is a dearth of mathematical models that combine climatic stress, mutualism (plant-bee), and antagonism (Vespa predation on bees) in a single ecological system, despite the fact that numerous research look at how climate change impacts particular species or paired interactions. Our capacity to forecast the cascade impacts of warming on pollination services and plant reproduction is hampered by this gap. The densities of the flowering plant depend on the behavior of the pollinators. We describe a novel pollinator model consisting of flowering plants and two pollinator species (honey bees and Vespa orientalis). It assumed that flowering plants’ densities depend on flower visitation rates by honey bees and Vespa orientalis. Also, the global warming phenomenon is assumed to harm the growth of flowering plants and honey bees. The Vespa orientalis hinders the expansion of bees, as they kill them and take the honey inside their stomachs. After presenting the model, the system’s positivity and boundedness, which are crucial for ensuring well-posedness in any dynamical model, are confirmed. The conditions under which the possible equilibrium points exist are established. Furthermore, discussed are the conditions for obtaining local stability around each equilibrium point. Uniform persistence, which ensures the simultaneous existence of all species, is executed. The required conditions for the occurrence of Hopf bifurcations are performed. Analytical confirmation is obtained by the use of numerical simulation. By manipulating the parametric values, the system displays phenomena such as stability, periodic attracts, and the eradication of some species. Therefore, the present study can assist ecologists in determining the parameters necessary to investigate and acquire significant data regarding flowering plant-pollinator systems.
{"title":"The impact of climate change on flowering plants-bees-Vespa orientalis model","authors":"Shireen Jawad , Ashraf Adnan Thirthar , Kottakkaran Sooppy Nisar","doi":"10.1016/j.rico.2025.100583","DOIUrl":"10.1016/j.rico.2025.100583","url":null,"abstract":"<div><div>There is a dearth of mathematical models that combine climatic stress, mutualism (plant-bee), and antagonism (Vespa predation on bees) in a single ecological system, despite the fact that numerous research look at how climate change impacts particular species or paired interactions. Our capacity to forecast the cascade impacts of warming on pollination services and plant reproduction is hampered by this gap. The densities of the flowering plant depend on the behavior of the pollinators. We describe a novel pollinator model consisting of flowering plants and two pollinator species (honey bees and Vespa orientalis). It assumed that flowering plants’ densities depend on flower visitation rates by honey bees and Vespa orientalis. Also, the global warming phenomenon is assumed to harm the growth of flowering plants and honey bees. The Vespa orientalis hinders the expansion of bees, as they kill them and take the honey inside their stomachs. After presenting the model, the system’s positivity and boundedness, which are crucial for ensuring well-posedness in any dynamical model, are confirmed. The conditions under which the possible equilibrium points exist are established. Furthermore, discussed are the conditions for obtaining local stability around each equilibrium point. Uniform persistence, which ensures the simultaneous existence of all species, is executed. The required conditions for the occurrence of Hopf bifurcations are performed. Analytical confirmation is obtained by the use of numerical simulation. By manipulating the parametric values, the system displays phenomena such as stability, periodic attracts, and the eradication of some species. Therefore, the present study can assist ecologists in determining the parameters necessary to investigate and acquire significant data regarding flowering plant-pollinator systems.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100583"},"PeriodicalIF":0.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329589","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}
Pub Date : 2025-06-19DOI: 10.1016/j.rico.2025.100587
Yoshihiro Iwanaga , Yasutaka Fujimoto
Efficiently solving nonlinear optimal control problems is crucial in trajectory planning and model predictive control. This can be achieved by utilizing differential dynamic programming (DDP) and iterative linear quadratic regulator (iLQR), which have recently gained attention. As these algorithms partition the problem into subproblems at each time step, they exhibit linear complexity of one iteration in the length of the prediction horizon. While these methodologies are computationally efficient, industrial applications demand further improvements in computational efficiency, primarily due to the limitations of embedded CPUs. The parametric representation of control inputs has been widely adopted to reduce the dimensionality of decision variables in optimal control problems. However, the subproblem partitioning inherent in DDP and iLQR presents challenges for directly incorporating this representation. In this study, we present a computationally efficient algorithm that integrates a parametric representation of control inputs into DDP- or iLQR-like algorithms. We exemplified a scenario in which parametric representation was introduced by considering interior-point DDP and iLQR, which could handle nonlinear inequality constraints. The effectiveness of this approach for practical applications was demonstrated through a series of numerical experiments. In particular, these numerical experiments mainly focused on key real-world problems, such as trajectory planning for forklifts and optimal excavation trajectory planning for an excavator. Regarding trajectory planning for forklifts and excavators, we achieved a maximum reduction of about 70% in the total computation time.
{"title":"Enhancing computational efficiency of iLQR and DDP via the parametric representation of control inputs","authors":"Yoshihiro Iwanaga , Yasutaka Fujimoto","doi":"10.1016/j.rico.2025.100587","DOIUrl":"10.1016/j.rico.2025.100587","url":null,"abstract":"<div><div>Efficiently solving nonlinear optimal control problems is crucial in trajectory planning and model predictive control. This can be achieved by utilizing differential dynamic programming (DDP) and iterative linear quadratic regulator (iLQR), which have recently gained attention. As these algorithms partition the problem into subproblems at each time step, they exhibit linear complexity of one iteration in the length of the prediction horizon. While these methodologies are computationally efficient, industrial applications demand further improvements in computational efficiency, primarily due to the limitations of embedded CPUs. The parametric representation of control inputs has been widely adopted to reduce the dimensionality of decision variables in optimal control problems. However, the subproblem partitioning inherent in DDP and iLQR presents challenges for directly incorporating this representation. In this study, we present a computationally efficient algorithm that integrates a parametric representation of control inputs into DDP- or iLQR-like algorithms. We exemplified a scenario in which parametric representation was introduced by considering interior-point DDP and iLQR, which could handle nonlinear inequality constraints. The effectiveness of this approach for practical applications was demonstrated through a series of numerical experiments. In particular, these numerical experiments mainly focused on key real-world problems, such as trajectory planning for forklifts and optimal excavation trajectory planning for an excavator. Regarding trajectory planning for forklifts and excavators, we achieved a maximum reduction of about 70% in the total computation time.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100587"},"PeriodicalIF":0.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364470","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}
Pub Date : 2025-06-16DOI: 10.1016/j.rico.2025.100578
Brandon Cortés-Caicedo , Jhony Andrés Guzmán-Henao , Oscar Danilo Montoya , Luis Fernando Grisales-Noreña , Rubén Iván Bolaños
The inherent characteristics of unbalanced three-phase distribution networks can have negative technical and financial effects. In this vein, optimal conductor size selection and phase balancing are among the most common strategies for improving these indicators, which involves dealing with complex optimization problems. This article presents a mixed-integer nonlinear programming model to address conductor selection and phase balancing in distribution systems. Given the complexity of the model, a leader–follower methodology based on the vortex search algorithm (VSA) is employed to determine the conductor caliber and load phase configuration, in conjunction with the three-phase successive approximations power flow method to compute the objective function. This methodology is compared against the hurricane optimization algorithm, the sine cosine algorithm, and the salp swarm optimization algorithm. Simulation results demonstrate that the proposed methodology provides the best solution for unbalanced distribution systems comprising eight and 25 nodes. The VSA yielded the best response, with values of 125,348.4870 USD and 94,475.1477 USD in the two test feeders, respectively, as well as the lowest standard deviation (0.1948% and 0.2147%) while requiring reasonable computational times, within the average for the 8-node system and the best time for the 25-node system. The VSA demonstrated superior performance in terms of cost minimization and solution consistency with a reasonable computational effort, which makes it a valuable tool for optimizing unbalanced distribution systems and enhancing their overall efficiency.
{"title":"Optimized conductor selection and phase balancing in unbalanced distribution networks: Economic optimization via the vortex search algorithm","authors":"Brandon Cortés-Caicedo , Jhony Andrés Guzmán-Henao , Oscar Danilo Montoya , Luis Fernando Grisales-Noreña , Rubén Iván Bolaños","doi":"10.1016/j.rico.2025.100578","DOIUrl":"10.1016/j.rico.2025.100578","url":null,"abstract":"<div><div>The inherent characteristics of unbalanced three-phase distribution networks can have negative technical and financial effects. In this vein, optimal conductor size selection and phase balancing are among the most common strategies for improving these indicators, which involves dealing with complex optimization problems. This article presents a mixed-integer nonlinear programming model to address conductor selection and phase balancing in distribution systems. Given the complexity of the model, a leader–follower methodology based on the vortex search algorithm (VSA) is employed to determine the conductor caliber and load phase configuration, in conjunction with the three-phase successive approximations power flow method to compute the objective function. This methodology is compared against the hurricane optimization algorithm, the sine cosine algorithm, and the salp swarm optimization algorithm. Simulation results demonstrate that the proposed methodology provides the best solution for unbalanced distribution systems comprising eight and 25 nodes. The VSA yielded the best response, with values of 125,348.4870 USD and 94,475.1477 USD in the two test feeders, respectively, as well as the lowest standard deviation (0.1948% and 0.2147%) while requiring reasonable computational times, within the average for the 8-node system and the best time for the 25-node system. The VSA demonstrated superior performance in terms of cost minimization and solution consistency with a reasonable computational effort, which makes it a valuable tool for optimizing unbalanced distribution systems and enhancing their overall efficiency.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100578"},"PeriodicalIF":0.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306407","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}
Pub Date : 2025-06-06DOI: 10.1016/j.rico.2025.100584
Mohamed Salah Dahassa, Nadjet Zioui
This paper explores the use of a quantum genetic algorithm (QGA) for finding the best control considering a calculated path for a six-jointed robotic arm. Classical genetic algorithms (GAs) are typically used to solve optimization problems in robot manipulators, however, QGAs bring a consistent advantage in terms of solution quality. In fact, this study uses a QGA simulated on classical hardware to create optimal control law based on a fifth-order polynomial path, aiming to minimize the tracking error of the position. Eventually, it compares positional error and energy consumption used by actuators through its cost function with to the classical methods. The simulation demonstrates that the QGA tends to be better than real-coded and binary-coded genetic algorithms (respectively RCGA and BCGA), especially when it comes to tracking accuracy, energy, and maintaining stability in noisy conditions. This indicates its potential use in real-time robotics applications by exploring quantum algorithms' practical benefits over traditional optimization methods for complex tasks with multiple dimensions in robot systems control.
{"title":"Optimal control-based quantum genetic algorithm for a six jointed articulated robotic arm","authors":"Mohamed Salah Dahassa, Nadjet Zioui","doi":"10.1016/j.rico.2025.100584","DOIUrl":"10.1016/j.rico.2025.100584","url":null,"abstract":"<div><div>This paper explores the use of a quantum genetic algorithm (QGA) for finding the best control considering a calculated path for a six-jointed robotic arm. Classical genetic algorithms (GAs) are typically used to solve optimization problems in robot manipulators, however, QGAs bring a consistent advantage in terms of solution quality. In fact, this study uses a QGA simulated on classical hardware to create optimal control law based on a fifth-order polynomial path, aiming to minimize the tracking error of the position. Eventually, it compares positional error and energy consumption used by actuators through its cost function with to the classical methods. The simulation demonstrates that the QGA tends to be better than real-coded and binary-coded genetic algorithms (respectively RCGA and BCGA), especially when it comes to tracking accuracy, energy, and maintaining stability in noisy conditions. This indicates its potential use in real-time robotics applications by exploring quantum algorithms' practical benefits over traditional optimization methods for complex tasks with multiple dimensions in robot systems control.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100584"},"PeriodicalIF":0.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322609","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}
Pub Date : 2025-06-01DOI: 10.1016/j.rico.2025.100576
Mohamed Sadok Cherif
A relevant extension of traditional goal programming (GP), fuzzy goal programming (FGP) can handle uncertainty and imprecision in multi-objective optimization problems. Based on fuzzy set theory, the notion of membership functions has been introduced to consider the fuzziness related to objectives and constraints. These membership functions are mainly intended for fuzziness in the GP rather than modeling the decision-maker’s (DM’s) preferences and his/her attitude toward risk in the decision-making process. In the satisfying philosophy of FGP, little attention has been given to how preferences evolve in terms of the behavior of the decision-maker (DM) and how these preferences may affect decisions in risky scenarios. To address this issue, we suggest novel behavioral-type utility functions for the FGP approach by introducing the concept of behavioral membership functions. This concept offers an innovative procedure for simulating the DM’s behavioral preferences in the FGP approach. First, two main categories of objectives in relation to the DM’s behavioral preferences are distinguished in this work. A risk aversion parameter is integrated into membership functions according to the nature of each objective type, obtaining the so-called behavioral membership functions. A behavioral FGP approach is subsequently formulated. Finally, an illustrative example of venture capital investments, a sensitivity analysis, and comparisons with other FGP approaches are provided to demonstrate the validity and practicality of our proposed approach.
{"title":"Decision-maker’s behavioral preferences modeling in fuzzy goal programming through linear-nonlinear membership functions","authors":"Mohamed Sadok Cherif","doi":"10.1016/j.rico.2025.100576","DOIUrl":"10.1016/j.rico.2025.100576","url":null,"abstract":"<div><div>A relevant extension of traditional goal programming (GP), fuzzy goal programming (FGP) can handle uncertainty and imprecision in multi-objective optimization problems. Based on fuzzy set theory, the notion of membership functions has been introduced to consider the fuzziness related to objectives and constraints. These membership functions are mainly intended for fuzziness in the GP rather than modeling the decision-maker’s (DM’s) preferences and his/her attitude toward risk in the decision-making process. In the satisfying philosophy of FGP, little attention has been given to how preferences evolve in terms of the behavior of the decision-maker (DM) and how these preferences may affect decisions in risky scenarios. To address this issue, we suggest novel behavioral-type utility functions for the FGP approach by introducing the concept of behavioral membership functions. This concept offers an innovative procedure for simulating the DM’s behavioral preferences in the FGP approach. First, two main categories of objectives in relation to the DM’s behavioral preferences are distinguished in this work. A risk aversion parameter is integrated into membership functions according to the nature of each objective type, obtaining the so-called behavioral membership functions. A behavioral FGP approach is subsequently formulated. Finally, an illustrative example of venture capital investments, a sensitivity analysis, and comparisons with other FGP approaches are provided to demonstrate the validity and practicality of our proposed approach.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"19 ","pages":"Article 100576"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205142","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}
The primary motivation of this paper is to identify the most effective implementation of delayed preventive measures, including active screening and testing, mask-wearing, and vaccination for COVID-19, in a way that minimizes the number of infected and exposed individuals, and maximizes the count of recovered individuals. Our goal is to comprehend the impact of delays on the epidemic’s spread and to offer guidance to health authorities on what steps to take if they implement COVID-19 preventive measures too late. We achieve this by employing optimal control theory on a SEIR model that illustrates the dynamics between susceptible, infected, exposed, and recovered individuals within the population. We set up our optimal control problem with multiple time delays in both the state and control variables, then, we used Pontryagin’s Maximum Principle to determine the solution to the delayed optimal control problem with multiple state-control constraints. Our results of simulation show that to control epidemic propagation when preventive measures are delayed, we should take immediate action after delay phase by enforcing mask wear and starting vaccinations to cover a significant portion of the population as quickly as possible. We should then implement active screening and testing measures to further control the spread.
{"title":"Optimal control problem for COVID-19 with multiple time-delays in state and control","authors":"Mohcine El Baroudi , Hassan Laarabi , Samira Zouhri , Mostafa Rachik , Abdelhadi Abta","doi":"10.1016/j.rico.2025.100579","DOIUrl":"10.1016/j.rico.2025.100579","url":null,"abstract":"<div><div>The primary motivation of this paper is to identify the most effective implementation of delayed preventive measures, including active screening and testing, mask-wearing, and vaccination for COVID-19, in a way that minimizes the number of infected and exposed individuals, and maximizes the count of recovered individuals. Our goal is to comprehend the impact of delays on the epidemic’s spread and to offer guidance to health authorities on what steps to take if they implement COVID-19 preventive measures too late. We achieve this by employing optimal control theory on a SEIR model that illustrates the dynamics between susceptible, infected, exposed, and recovered individuals within the population. We set up our optimal control problem with multiple time delays in both the state and control variables, then, we used Pontryagin’s Maximum Principle to determine the solution to the delayed optimal control problem with multiple state-control constraints. Our results of simulation show that to control epidemic propagation when preventive measures are delayed, we should take immediate action after delay phase by enforcing mask wear and starting vaccinations to cover a significant portion of the population as quickly as possible. We should then implement active screening and testing measures to further control the spread.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"19 ","pages":"Article 100579"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184741","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}
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