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IEEE International Conference on Networking, Sensing and Control

作者： Imed Kacem Ilyes Kadri Benoît Martin Isabelle Pecci LCOMS Université de Lorraine Metz France

ISBN: (纸本)9781665440493

The purpose of this paper is to elaborate approximate algorithms capable of generating two-dimensional interfaces that contain a given set of services adapted to the user’s context. The proposed methods give good solutions that minimize the used surface, by taking in consideration the placement and the positioning of the tiles. The obtained interface from packing all the tiles into a variable-size bin should be adapted and very simple to use. In order to increase the usability of these solutions (configurations) we develop a mathematical modeling incorporating different Human-Computer Interaction constraints for an exact resolution. Such a placement is performed on the basis of the importance and the category of the proposed service in the tiles. Moreover, we suggest two approximate approaches: a heuristic based on shelf strategy and a memetic algorithm. The exact resolution of the proposed multi-objective mathematical model gives an optimal solution only for small and medium instances. For the other instances, it is difficult to reach the optimal value in a short running time, which shows the practical interest of the proposed approaches.

关键词： Human computer interaction Memetics Adaptation models Heuristic algorithms Pareto optimization Approximation algorithms Mathematical models

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An argument-driven classification and comparison of reservoir operation optimization methods

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ADVANCES IN WATER RESOURCES 2019年 128卷 74-86页

作者： Dobson, Barnaby Wagener, Thorsten Pianosi, Francesca Univ Bristol Dept Civil Engn 95 Woodland Rd Bristol BS8 1US Avon England

Reservoir operation optimization aims to determine release and transfer decisions that maximise water management objectives such as ensuring a reliable water supply, hydropower production, mitigation of downstream floods, etc. An extensive and growing body of scientific literature exists on advancing and applying mathematical optimization methods to reservoir operation problems. In this paper, we review such literature according to a novel classification system of optimization approaches, which focuses on the characteristics of the actual operation problem - i.e. what needs to be optimized, or in mathematical terms, 'the argument' of the optimization problems - rather than the mathematical properties of the optimization algorithm. This enables us to discuss the advantages, limitations and the scope of application of the different optimisation methods;and to provide practical guidelines for matching the properties of a system and operation problem with a suitable optimization method. Alongside this paper we provide code to implement many of the methods we review for an illustrative reservoir system.

关键词： Reservoir operation Water resources multi-objective optimization

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Advancements in optimization Techniques for Active Magnetic Bearing Systems: Current Trends and Future Directions

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IEEE Access 2025年

作者： Nehal, Tasnemul Hasan Hamanah, Waleed M. Abido, Mohammad Ali King Fahd University of Petroleum and Minerals Department of Control and Instrumentation Enginering Dhahran Saudi Arabia Research & Innovation KFUPM Applied Research Center for Metrology Standards and Testing Dhahran31261 Saudi Arabia King Fahd University of Petroleum and Minerals Department of Electrical Engineering Dhahran Saudi Arabia Research & Innovation KFUPM IRC for Sustainable Energy Systems Dhahran31261 Saudi Arabia Research & Innovation KFUPM SDAIA-KFUPM Joint Research Center for Artificial Intelligence Dhahran31261 Saudi Arabia

Active Magnetic Bearings (AMBs) are revolutionizing high-speed, contactless operations across industries like aerospace, energy, and precision manufacturing. However, their complex dynamics— marked by nonlinear behavior, sensitivity to external disturbances, and intensive computational requirements—pose significant challenges. This review delves into cutting-edge optimization techniques for AMBs, from time-tested methods like PID control to innovative approaches such as metaheuristic algorithms, multi-objective optimization, and AI-powered strategies including reinforcement learning and iterative learning control. The emergence of hybrid optimization, adaptive fuzzy controllers, and machine learning-enhanced models is pushing the boundaries of AMB performance, offering substantial gains in stability, efficiency, fault tolerance, and vibration suppression. Through extensive simulations and real-world experiments, we highlight these advancements’ practical benefits in reducing energy consumption, combating harmonic vibrations, and ensuring resilient operation under dynamic *** also explore key challenges such as enhancing power density, lowering computational overhead, and boosting long-term system reliability, while outlining exciting future directions like data-driven methods, real-time adaptive control systems, and novel material innovations. This review emphasizes the pivotal role of optimization in unlocking the full potential of AMBs, meeting the ever-growing demands of high-performance industrial applications. © 2013 IEEE.

关键词： Fabrication

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CMLSGA: A Co-evolutionary multi-level selection genetic algorithm for multi-objective optimization

arXiv

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arXiv 2021年

作者： Grudniewski, P.A. Sobey, A.J. Fluid Structure Interactions Group University of Southampton Southampton EnglandSO16 7QF United Kingdom Marine and Maritime Group Data-centric Engineering The Alan Turing Institute The British Library London EnglandNW1 2DB United Kingdom

In practical optimisation the dominant characteristics of the problem are often not known prior. Therefore, there is a need to develop general solvers as it is not always possible to tailor a specialised approach to each application. The hybrid form of multi-Level Selection Genetic Algorithm (MLSGA) already shows good performance on range of problems due to its diversity-first approach, which is rare among Evolutionary Algorithms. To increase the generality of its performance this paper proposes a distinct set of co-evolutionary mechanisms, which defines co-evolution as competition between collectives rather than individuals. This distinctive approach to co-evolutionary provides less regular communication between sub-populations and different fitness definitions between individuals and collectives. This encourages the collectives to act more independently creating a unique sub-regional search, leading to the development of co-evolutionary MLSGA (cMLSGA). To test this methodology nine genetic algorithms are selected to generate several variants of cMLSGA, which incorporates these approaches at the individual level. The new mechanisms are tested on over 100 different functions and benchmarked against the 9 state-of-the-art competitors in order to find the best general solver. The results show that the diversity of co-evolutionary approaches is more important than their individual performances. This allows the selection of two competing algorithms that improve the generality of cMLSGA, without large loss of performance on any specific problem type. When compared to the state-of-the-art, the proposed methodology is the most universal and robust, leading to an algorithm more likely to solve complex problems with limited knowledge about the search space. © 2021, CC BY.

关键词： Genetic algorithms

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optimization of zero-energy building by multi-criteria optimization method: A case study

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JOURNAL OF BUILDING ENGINEERING 2021年 44卷

作者： Zhu, Neng Liu, Xiangzhe Dong, Qi Rodriguez, Dragan Tianjin Univ Sch Environm Sci & Engn Tianjin 300072 Peoples R China Tianjin Univ Sch Architecture Tianjin 300072 Peoples R China Natl Police Univ Criminal Justice Baoding 071000 Hebei Peoples R China Case Western Reserve Univ Cleveland OH 44106 USA

Many subjects and objects are included into the zero-energy building investigation while the conventional optimal approach is suffering from its limitations. The boundary of energy balance and physic of zero-energy buildings in various regions highlighted with various architectural attributions require to be more investigated and assured. As reported by the statistics, it needs further integrating materials and installing costs in zeroenergy buildings. In this paper, due to the increasing energy consumption in buildings, a new global multiplecriteria optimization model for zero-energy buildings, combining this model with the comprehensive assessment approach is conducted. The optimization design approaches for renewable energy structures included here are the multiple-criteria optimization by Enhanced Red Fox optimization Algorithm on the initial design as concerns energy optimum models and costs, and the integration of Grey multiple-Level Complete Assessment approach is employed to optimum model, which are the energy-conserving, solar energy application effectiveness, social and economic aspects. The optimum solution is proposed based on the chosen decision according to the global optimum model. This solution can help the engineer to design practical constructions. Finally, a multiple-objective optimal model for a case study placed in Beijing, China, is proposed to achieve an optimum design effect. Based on the study, efficient decisions can be made by designers with utilization of the proposed method. This design can easily be used in the considered real applications.

关键词： multiple-criteria optimization Zero-energy building Life cycle cost Life cycle energy Energy efficiency actions Solar PV system Enhanced red fox optimization algorithm Grey multiple-level complete assessment approach

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Introductory overview: optimization using evolutionary algorithms and other metaheuristics

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ENVIRONMENTAL MODELLING & SOFTWARE 2019年 114卷 195-213页

作者： Maier, H. R. Razavi, S. Kapelan, Z. Matott, L. S. Kasprzyk, J. Tolson, B. A. Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA 5005 Australia Univ Saskatchewan Global Inst Water Secur Saskatoon SK Canada Univ Saskatchewan Sch Environm & Sustainabil Saskatoon SK Canada Univ Saskatchewan Dept Civil & Geol Engn Saskatoon SK Canada Univ Exeter Coll Engn Math & Phys Sci Harrison BldgNorth Pk Rd Exeter EX4 4QF Devon England SUNY Buffalo Ctr Computat Res Buffalo NY 14203 USA Univ Colorado Civil Environm & Architectural Engn Dept UCB 428 Boulder CO 80309 USA Univ Waterloo Dept Civil & Environm Engn Waterloo ON N2L 3G1 Canada Delft Univ Technol Fac Civil Engn & Geosci Dept Water Management Stevinweg 1 NL-2628 CN Delft Netherlands

Environmental models are used extensively to evaluate the effectiveness of a range of design, planning, operational, management and policy options. However, the number of options that can be evaluated manually is generally limited, making it difficult to identify the most suitable options to consider in decision-making processes. By linking environmental models with evolutionary and other metaheuristic optimization algorithms, the decision options that make best use of scarce resources, achieve the best environmental outcomes for a given budget or provide the best trade-offs between competing objectives can be identified. This Introductory Overview presents reasons for embedding formal optimization approaches in environmental decision-making processes, details how environmental problems are formulated as optimization problems and outlines how single- and multi-objective optimization approaches find good solutions to environmental problems. practical guidance and potential challenges are also provided.

关键词： multiobjective optimization

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DCDG-EA: Dynamic convergence-diversity guided evolutionary algorithm for many-objective optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 118卷 35-51页

作者： Li, Zhiyong Lin, Ke Nouioua, Mourad Jiang, Shilong Gu, Yu PKU HKUST Shenzhen HongKong Inst Shenzhen 518057 Peoples R China Hunan Univ Key Lab Embedded & Network Comp Hunan Prov Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Beijing Univ Chem Technol Beijing Adv Innovat Ctr Soft Matter Sci & Engn Beijing 100029 Peoples R China Goethe Univ Frankfurt Inst Inorgan & Analyt Chem Max von Laue Str 7 D-60438 Frankfurt Germany

Maintaining a good balance between the convergence and the diversity is particularly crucial for the performance of the evolutionary algorithms (EAs). However, the traditional multi-objective evolutionary algorithms, which have shown their competitive performance with a variety of practical problems involving two or three objectives, face significant challenges in case of problems with more than three objectives, namely many-objective optimization problems (MaOPs). This paper proposes a dynamic convergence diversity guided evolutionary algorithm, namely (DCDG-EA) for MaOPs by employing the decomposition technique. Besides, the objective space of MaOPs is divided into K subspaces by a set of uniformly distributed reference vectors. Each subspace has its own subpopulation and evolves in parallel with the other subspaces. In DCDG-EA, the balance between the convergence and the diversity is achieved through the convergence-diversity based operator selection (CDOS) strategy and convergence-diversity based individual selection (CDIS) strategy. In CDOS, for each operator of the set of operators, a selection probability is assigned which is related to its convergence and diversity capabilities. Based on the attributed selection probabilities, an appropriate operator is selected to generate the offsprings. Furthermore, CDIS is used which allows to greatly overcome the inefficiency of the Pareto dominance approaches. It updates each subpopulation by using two independent distance measures that represent the convergence and the control diversity, respectively. The experimental results on DTLZ and WFG benchmark problems with up to 15 objectives show that our algorithm is highly competitive comparing with the four state-of-the-art evolutionary algorithms in terms of convergence and diversity. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Many-objective optimization Convergence Diversity Decomposition Evolutionary algorithm Pareto optimality

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Durability prediction of sustainable marine concrete under freeze-thaw cycles using multi-objective machine learning models

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CASE STUDIES IN CONSTRUCTION MATERIALS 2025年 22卷

作者： Rezzoug, Aissa AlAteah, Ali H. Alinsaif, Sadiq Mostafa, Sahar A. Imam Mohammad Ibn Saud Islamic Univ IMSIU Coll Engn Riyadh 11432 Saudi Arabia Univ Hafr Al Batin Coll Engn Dept Civil Engn Hafar al Batin 39524 Saudi Arabia Univ Hafr Al Batin Coll Comp Sci & Engn Hafar Al Batin 39524 Saudi Arabia Beni Suef Univ Fac Engn Dept Civil Engn Bani Suwayf Egypt

Marine and cold-region concrete structures are prone to surface scaling, interior microcracking, and long-term strength degradation due to freeze-thaw cycles (FTCs). Understanding and accurately predicting FTC-induced damage is essential for sustainable and resilient structural design. This study introduces machine learning (ML)-based algorithms for forecasting FTC-induced durability of concrete while simultaneously predicting sustainability metrics such as CO2 emissions and production cost. To enhance accessibility and practical usability, a graphical user interface (GUI) was developed, enabling engineers to input concrete mix parameters and obtain performance predictions without requiring programming knowledge. Four machine learning techniques were utilized: a convolutional neural network (CNN), a genetic algorithm with optimized artificial neural network (GA-ANN), an adaptive neuro-fuzzy inference system (ANFIS), and multi-objective optimization (MOO). The dataset used in this study was compiled from previously published experimental results in the literature, allowing the development of predictive models without the need for new laboratory testing. All models demonstrated strong predictive accuracy (validation R-2 > 0.90), aligning well with observed experimental trends. The MOO model achieved the highest performance with R-2 training = 0.974, R-2 testing = 0.96, MAE = 1.12 MPa, and RMSE = 1.54 MPa, outperforming CNN (R-2 = 0.942 and 0.93), GA-ANN (R-2 = 0.931 and 0.902), and ANFIS (R-2 = 0.905 and 0.939) for training and testing phases, respectively. Evaluation metrics included R-2, MAE, MSE, RMSE, and MARE, with model ranking as follows: MOO > ANFIS > CNN > GA-ANN. The MOO model also achieved the most accurate predictions for CO2 emissions and production cost (R-2 = 0.98 and 0.99, respectively). These findings highlight the reliability of ML approaches in predicting concrete performance under FTC conditions, reinforcing their potential for use in the design of

关键词： multi-objective optimization Freeze-thaw cycles K-fold cross-validation Graphical user interface CO2 emission

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multiobjective Optimal Control: Algorithms, approaches and Advice for the Application

Multiobjective Optimal Control: Algorithms, Approaches and A...

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International Automatic Control Conference

作者： Adrian Gambier Fraunhofer Institute for Wind Energy Systems IWES Am Seedeich 45 Bremerhaven Germany

ISBN: (数字)9781728171982

ISBN: (纸本)9781728171999

The advanced control system design is turning more and more complex and, sophisticated optimization techniques become essential in order to satisfy such high requirements. In particular, multi-objective optimal (MOO) control is nowadays a way to reach a control system that conciliates at the same time several design objectives that sometimes could be contradictories. In the past ten years, numerous advances can be observed in the field of multi-objective optimization. Many optimization algorithms with high complexity are now available in order to solve different kind of optimization problems. The use of such approaches is not simple. Thus, the main contribution of this work is study of old and new MOO algorithms from the control point of view providing practical experience and design hints. A numerical study by using the pitch control system design of a wind turbine is included.

关键词： Optimal control Tools Turning Wind turbines Complexity theory optimization System analysis and design

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A Case-Driven Simulation-optimization Model for Sustainable Medical Logistics Network

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Socio-Economic Planning Sciences 2025年

作者： Fariba Goodarzian Peiman Ghasemi Edinburgh Business School (EBS) and School of Social Sciences Heriot-Watt University Riccarton Currie EH14 4AS University of Vienna Department of Business Decisions and Analytics Kolingasse 14-16 1090 Vienna Austria

The supply chain industry represents one of the largest and most critical sectors worldwide, and it is undergoing substantial transformation with the increasing integration of Electric Vehicles (EVs). In particular, EVs are being adopted within healthcare logistics networks to substantially mitigate carbon emissions and counteract escalating fuel costs, thereby enhancing the alignment of supply chain operations with broader public health and environmental sustainability objectives. This study proposes a novel Sustainable Healthcare Supply Chain Network (SHSCN) model that explicitly incorporates the deployment of EVs for the distribution of medical products and the optimal siting of Charging Stations (CSs) to support their operation. To quantitatively assess the queuing behavior of EVs at these charging facilities, an M/M/c queuing model is employed, providing insights into system performance in terms of vehicle waiting times. Additionally, the Simulation Method (SM) is utilized to estimate optimal fleet sizes and operational parameters. The validity and practical applicability of the proposed mathematical framework are demonstrated through a case study conducted within the medical industry context, employing the augmented ε-constraint method to handle the model’s multi-objective nature. Given the NP-hardness of the formulated optimization problems, two novel hybrid metaheuristic approaches are introduced: Hybrid Simulated Annealing integrated with K-Medoids clustering (HKMSA), and Hybrid Tabu Search combined with K-Medoids clustering (HKMTS). Computational results indicate that both HKMSA and HKMTS exhibit superior performance relative to alternative methods, particularly in terms of solution quality and computational efficiency across problem instances of varying scales. Sensitivity analyses further reveal that a 30% reduction in demand results in increases in all three objective function values, reaching 458,369, 894,100, and 761,790 units, respectively. Conversel

关键词： Case-Driven Simulation-optimization Model Sustainability Supply Chain Network Meta-heuristic algorithms Machine learning

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