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Optimizing production planning and sequencing in hot strip mills: an approach using multi-objective genetic algorithms

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-48页

作者： Fardad, Hamidreza Safi-Esfahani, Faramarz Barekatain, Behrang Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran

Planning and sequencing for hot strip mills in the steel industry is a challenging, complex problem that has fascinated optimization researchers and practitioners alike. This paper applies a combinatory heuristic search and a multi-objective metaheuristic that is a novel approach called HSMO-NSGA-II and employs the HSMO heuristic search method and NSGA-II multi-objective genetic optimization as a metaheuristic algorithm to address complex hot strip mills scheduling tasks. This research aims to enhance the efficiency and effectiveness of production planning and sequencing in hot strip mill, while minimizing operational costs and maximizing rolling utilization. The output consists of slabs categorized into three parts, which converge toward a set of Pareto-optimal solutions while maintaining diversity across the entire solution space. The results demonstrate a significant improvement in comparing the base methods with the HSMO-NSGA-II method, and the proposed method shows better average performance at 23.01%. Notably, the HSMO-NSGA-II method demonstrated a remarkable improvement in performance across the evaluated scenarios, showcasing its potential to enhance productivity and operational efficiency in industrial applications significantly. These findings not only support the viability of using advanced genetic algorithms in complex industrial settings but also open avenues for future research into hybrid optimization techniques.

关键词： Production planning Sequencing Hot strip mill (HSM) multi-objective genetic algorithms Optimization

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Optimization of a hybrid photovoltaic/thermal-ground source heat pump system using artificial neural networks and multi-objective genetic algorithms

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JOURNAL OF BUILDING ENGINEERING 2025年 103卷

作者： Andalibi, Ali Safarzadeh, Mohammad Sobhani, Seyed Mohammad Jafar Pasdarshahri, Hadi Tarbiat Modares Univ Fac Mech Engn POB 14115-111 Tehran Iran

The current study focuses on determining the optimal operating conditions of a cycle using multiobjective genetic algorithms;this cycle harnesses energy from the sun and the ground to supply a multi-zone and large sports and administrative complex. The cycle is designed to incorporate various control scenarios;numerous decision variables, some of which are related to these control scenarios, are utilized to optimize the performance of the cycle. COP, ground temperature variation over a year, and reduced CO2 emissions are the objective functions of the optimization problem, which is divided into two distinct problems. In both problems, there are 36 decision variables that inherently originate from four independent variables, which are modified over the months. So, the optimization process is time-consuming, and computations are reduced by developing surrogate models via artificial neural networks. Also, a total of 1,080 different artificial neural network architectures and training data sets, each with 36 inputs (corresponding to the decision variables of the optimization problems) and one output (representing one of the objective functions) are analyzed to identify the best-performing architectures using hyperparameter tuning. Then, the Pareto fronts of the mentioned optimization problems are extracted, proposing a number of optimal conditions. The highest and the lowest amounts of the coefficient of performance on the Pareto fronts of the problems are 2.28 and 3.02, respectively. The highest possible amount is 41.15%, 7.35 %, and 7.27% more than three baseline scenarios. Among all the suggested conditions on the Pareto fronts, ten conditions (including the Knee points) are extensively studied.

关键词： Photovoltaic/thermal collectors Ground-source heat pumps Artificial neural networks multi-objective genetic algorithms Hyperparameter tuning

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Constructing ecological network based on multi-objective genetic algorithms: a case study of Changsha City, China

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LANDSCAPE ECOLOGY 2024年第12期39卷 212页

作者： Xiao, Shancai Peng, Jian Hu, Tao Tang, Hui Anhui Univ Sch Management Hefei 230039 Peoples R China Peking Univ Minist Nat Resources Coll Urban & Environm Sci Technol Innovat Ctr Integrated Ecosyst Restorat & Beijing 100871 Peoples R China

ContextRegional ecological security faces serious threats in a changing world. Ecological network (EN) provides decision-makers with spatial strategies for maintaining ecological security and landscape sustainability via alleviating the contradiction between ecological conservation and economic growth. Despite years of intense and fruitful studies, accurately identifying ecological source patches when facing multiple conflicting objectives still remains a *** study aimed to propose an advanced framework for recognizing ecological source patches with consideration of multiple objectives and further constructing EN, which would promote a more profound understanding of local ecological condition and provide spatial guidance for ecological conservation *** Changsha City as the study area, we evaluated the ecological condition by considering three key ecosystem services, i.e., habitat maintenance, carbon sequestration and water yield using the InVEST model. Ecological source patches were identified using multi-objective genetic algorithms (MOGA) in view of ecosystem services, landscape connectivity and the total area of ecological source patches. Ecological corridors were extracted by applying Minimum Cumulative Resistance (MCR) model based on modified ecological resistance surface. The EN was established by combining these ecological source patches with ecological *** EN in Changsha City was comprised of 51 ecological source patches and 50 ecological corridors. The ecological source patches were primarily distributed across the eastern and western mountainous areas with the total area of 2842 km2, occupying 24.05% of the study area. There was a clear lack of ecological source patches along the Xiangjiang River owing to the high level of urbanization, which deserved particular attention for ecological restoration. Overall, the identified ecological source patches provided 87.31% of ecosystem service supply and 82.4

关键词： Ecosystem services Landscape connectivity Ecological network multi-objective genetic algorithms Changsha City

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Robust Optimum Design of Tuned Mass Dampers to Mitigate Pedestrian-Induced Vibrations Using multi-objective genetic algorithms

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STRUCTURAL ENGINEERING INTERNATIONAL 2017年第4期27卷 492-501页

作者： Fernando Jimenez-Alonso, Javier Saez, Andres Univ Seville Dept Bldg Engn & Geotech Engn Seville Spain Univ Seville Dept Continuum Mech & Struct Anal Seville Spain

Tuned mass dampers (TMDs) are well-known control devices used widely to mitigate the pedestrian-induced vibrations on footbridges. The main purpose of this study is the robust optimum design of TMDs using multi-objective genetic algorithms in order to control the pedestrian-induced vibrations on footbridges considering the uncertainties associated with their modal properties. The performance of the TMD is improved via the robust optimum design of its modal parameters using multi-objective genetic algorithms. As an example, a benchmark footbridge, modelled by frame and shell elements, is used to assess numerically the performance and accuracy of the proposed method. The pedestrian action is simulated by an equivalent harmonic load. The proposed method is compared with the conventional Den Hartog's proposal. The results show that this new method is more effective than the conventional proposal and the resulting damping device is more economic due to the smaller value of its design parameters.

关键词： footbridge passive structural control tuned mass dampers robust design optimization multi-objective genetic algorithms

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Robust H∞ Controller Based on multi-objective genetic algorithms for Active Magnetic Bearing applied to cryogenic centrifugal compressor 29

Robust H∞ Controller Based on Multi-objective Genetic Algor...

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29th Chinese Control And Decision Conference (CCDC)

作者： Zhang, S. Y. Wei, C. B. Li, J. Wu, J. H. Tech Inst Phys & Chem CAS Key Lab Cryogen Beijing 100190 Peoples R China State Key Lab Technol Space Cryogen Propellants Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Huazhong Univ Sci & Technol Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781509046577

This paper proposes a robust H infinity controller based on multi-objective genetic algorithms (MOGAs) to control the active magnetic bearings (AMBs) with application to superfluid helium cryogenic centrifugal compressor (CCC). Basic weighting function formulas with seven parameters in all are suggested for H infinity controller, whose physical connections with system performance are clearly explained. Weighting function parameters are defined in a relatively narrow range according to actual operating feature of AMB systems. Then, based on these priori information, such as search-domain and weights expression, tuning and optimizing of the design performance function is carried out applying a MOGA. The control strategy avoids time consuming task in the progress of tuning parameters and provides a complete and versatile method to obtain weighting functions for a H infinity controller. Simulation results demonstrate that the optimized H controller guarantees AMB system better closed-loop behavior performance while retaining low value of control signals.

关键词： Active magnetic bearings robust control weighting function selection multi-objective genetic algorithms optimal design

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Optimisation of an exemplar oculomotor model using multi-objective genetic algorithms executed on a GPU-CPU combination

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BMC SYSTEMS BIOLOGY 2017年第20170327期11卷 40-40页

作者： Avramidis, Eleftherios Akman, Ozgur E. Univ Exeter Ctr Syst Dynam & Control Coll Engn Math & Phys Sci North Pk Rd Exeter EX4 4QF Devon England Natl Univ Ireland Dept Elect Engn Maynooth Kildare Ireland

Background: Parameter optimisation is a critical step in the construction of computational biology models. In eye movement research, computational models are increasingly important to understanding the mechanistic basis of normal and abnormal behaviour. In this study, we considered an existing neurobiological model of fast eye movements (saccades), capable of generating realistic simulations of: (i) normal horizontal saccades;and (ii) infantile nystagmus pathological ocular oscillations that can be subdivided into different waveform classes. By developing appropriate fitness functions, we optimised the model to existing experimental saccade and nystagmus data, using a well-established multi-objective genetic algorithm. This algorithm required the model to be numerically integrated for very large numbers of parameter combinations. To address this computational bottleneck, we implemented a master-slave parallelisation, in which the model integrations were distributed across the compute units of a GPU, under the control of a CPU. Results: While previous nystagmus fitting has been based on reproducing qualitative waveform characteristics, our optimisation protocol enabled us to perform the first direct fits of a model to experimental recordings. The fits to normal eye movements showed that although saccades of different amplitudes can be accurately simulated by individual parameter sets, a single set capable of fitting all amplitudes simultaneously cannot be determined. The fits to nystagmus oscillations systematically identified the parameter regimes in which the model can reproduce a number of canonical nystagmus waveforms to a high accuracy, whilst also identifying some waveforms that the model cannot simulate. Using a GPU to perform the model integrations yielded a speedup of around 20 compared to a high-end CPU. Conclusions: The results of both optimisation problems enabled us to quantify the predictive capacity of the model, suggesting specific modifications that

关键词： Systems biology Parameter optimisation multi-objective genetic algorithms High-performance computing Oculomotor control Mathematical modelling Infantile nystagmus

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Robust Hoo Controller Based on multi-objective genetic algorithms for Active Magnetic Bearing applied to cryogenic centrifugal compressor

Robust Hoo Controller Based on Multi-objective Genetic Algor...

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第29届中国控制与决策会议

作者： S.Y.Zhang C.B.Wei J.Li J.H.Wu CAS Key Laboratory of Cryogenics Technical Institute of Physics and Chemistry State Key Laboratory of Technologies in Space Cryogenic Propellants University of Chinese Academy of Sciences Huazhong University of Science and Technology

This paper proposes a robust H controller based on multi-objective genetic algorithms（MOGAs） to control the active magnetic bearings（AMBs） with application to super-fluid helium cryogenic centrifugal compressor（CCC）.Basic weighting function formulas with seven parameters in all are suggested for Hoo controller,whose physical connections with system performance are clearly *** function parameters are defined in a relatively narrow range according to actual operating feature of AMB ***,based on these priori information,such as search-domain and weights expression,tuning and optimizing of the design performance function is carried out applying a *** control strategy avoids time consuming task in the progress of tuning parameters and provides a complete and versatile method to obtain weighting functions for a Hoo *** results demonstrate that the optimized Hoo controller guarantees AMB system better closed-loop behavior performance while retaining low value of control signals.

关键词： Active magnetic bearings robust control weighting function selection multi-objective genetic algorithms optimal design

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Optimization of Control Parameters based on multi-objective genetic algorithms for Spacecraft Large Angle Attitude

Optimization of Control Parameters based on Multi-objective ...

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International Conference on Mechanical, Electronic and Engineering Technology (ICMEET)

作者： Huang, Qiang Zhang, Jianxin Zhang, Qiang Wei, Xiaopeng Dalian Univ Minist Educ Key Lab Adv Design & Intelligent Comp Dalian 116622 Peoples R China

ISBN: (纸本)9783038350491

Based on multi-objective generic algorithms, a novel approach to optimizing control parameters for large angle spacecraft attitude was proposed. The large angle attitude maneuver controller was designed by taking the spacecraft nonlinear dynamics model and Lyapunov method. To optimize the controller parameters, the alterable weight coefficient method was adopted. Optimal value of time and power consumption acted as fitness goals of the algorithm. Simulation results showed that the algorithm proposed in this paper was superior to the traditional single-objective optimization design method.

关键词： multi-objective genetic algorithms Large Angle Attitude Parameter Optimization Weighting Coefficient

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Enhancing Efficiency in Electric Arc Furnace Steelmaking: A multi-objective Optimization Approach Using the Non-Dominated Sorting genetic Algorithm II

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STEEL RESEARCH INTERNATIONAL 2025年第1期96卷

作者： Yi, Xiaoyu Yue, Qiang Dou, Zhihe Bu, Qingcai Northeastern Univ Sch Met Shenyang 110819 Peoples R China Liaoning Engn Res Ctr Proc Ind Energy Saving & Lo Shenyang 110819 Peoples R China Northeastern Univ State Environm Protect Key Lab Eco Ind Shenyang 110819 Peoples R China Northeastern Univ Key Lab Ecol Met Multi Met Intergrown Ores Minist Shenyang 110819 Peoples R China Jimei Univ Sch Marine Engn Xiamen 361021 Peoples R China

To realize the overall optimization of electric arc furnace (EAF) steelmaking system, a multi-objective optimization model including smelting cost, energy consumption per ton of steel, and carbon emission per ton of steel is established. The model is optimized by multi-objective genetic algorithm to improve the charging structure. At the same time, the data in the optimal solution set are used to analyze the influence of the change of scrap ratio on smelting cost, carbon emission per ton of steel, and smelting cycle. According to the actual working conditions and the demand of steel plant, the optimized results are selected. Compared with the actual production data, the proportion of scrap steel increases to 50.9%, the ratio of molten iron decreases to 38.8%, the smelting cost per ton of steel decreases by 12 Yuan, the energy consumption per ton of steel decreases by 4%, the carbon emission per ton of steel significantly decreases by 13%, and the smelting cycle is shortened by 2min, but at the cost of increasing the power consumption per ton of steel. The optimized results and the analysis of the change of scrap ratio provide reference for the optimization of EAF steelmaking system.

关键词： carbon emissions costs electric arc furnace steelmakings energy consumptions multicomponents chargings multi-objective genetic algorithms

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Mining numerical association rules via multi-objective genetic algorithms

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INFORMATION SCIENCES 2013年 233卷 15-24页

作者： Minaei-Bidgoli, B. Barmaki, R. Nasiri, M. Iran Univ Sci & Technol Dept Comp Engn Tehran Iran

Association rule discovery is an ever increasing area of interest in data mining. Finding rules for attributes with numerical values is still a challenging point in the process of association rule discovery. Most of popular methods for association rule mining cannot be applied to the numerical data without data discretization. There have been efforts to resolve the problem of dealing with numeric data. These approaches suffer from problems which are discussed in this paper. This work proposes a multi-objective genetic algorithm approach for mining association rules for numerical data. Several measures are defined in order to determine more efficient rules. Three measures, confidence, interestingness, and comprehensibility have been used as different objectives for our multi objective optimization which is amplified with genetic algorithms approach. Finally, the best rules are obtained through Pareto optimality. This method is based on the notion of rough patterns that use rough values defined with upper and lower intervals to represent a range or set of values. Mutation and crossover operators give a powerful exploration ability to the method and allow it to find out the best intervals of existing numerical values. The experimental results show that the generated rules by this method are more appropriate - based on several different characteristics - than the similar approaches' results, and our method outperforms these methods. (c) 2013 Elsevier Inc. All rights reserved.

关键词： Numerical association rule multi-objective genetic algorithms Confidence Comprehensibility Interestingness Rough value

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