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Surrogate-Based Multiobjective Optimization of Detention Pond Volume in Sponge City

WATER 2023年第15期15卷 2705-2705页

作者： Yang, Yuanyuan Xin, Yanfei Li, Jiake Xian Univ Technol State Key Lab Ecohydraul Northwest Arid Reg China Xian 710048 Peoples R China

Detention ponds are effective structures for stormwater management in the urban drainage system of sponge cities. The pond size is taken as the decision variable, while the cost, total suspended solids (TSS), and catchment peak outflow (CPO) serve as the objectives for optimizing the detention pond volume. First, we randomly generated 10,000 pond areas and input them into the stormwater management model to simulate the time series of outflow and suspended solids concentration, thereby generating samples by combining the set of pond area, corresponding cost, TSS, and CPO. Then, two backpropagation neural network models (i.e., BPNN-TSS and BPNN-CPO) were trained, tested, and evaluated for predicting TSS and CPO, respectively. We employed them as surrogates and used the non-dominated sorting genetic algorithm-ii to solve the optimization problem. The results showed: (1) The BPNN models accurately predicted TSS and CPO (determination coefficient 0.988 +/- 0.996, Nash-Sutcliffe efficiency 0.988 +/- 0.997), and efficiently substituted stormwater management model simulations for optimization purposes (residuals -18.49 +/- 28.10 kg and -0.45 +/- 0.29 m(3)/s). (2) For the Pareto solutions, the detention pond reduced TSS by 0 +/- 8.33% and CPO by 0 +/- 72.44% and delayed their peaks by 4 +/- 52 min;the reduction in TSS and CPO tends to grow as pond size increases, and CPO reduction exhibits a minor marginal effect. (3) The surrogate-based approach saves 90.03% runtime while preserving the quality of the Pareto solutions, verifying reliability.

关键词： backpropagation neural network non-dominated sorting genetic algorithm-ii peak outflow stormwater drainage system suspended solids SWMM

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Multi-objective optimisation of friction stir welding parameters: integration of FEM and NSGA-ii

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INTERNATIONAL JOURNAL OF MANUFACTURING RESEARCH 2021年第1期16卷 82-101页

作者： Rao, Thella Babu Rao, C. S. P. Baki, Naresh Natl Inst Technol Andhra Pradesh Dept Mech Engn Tadepalligudem 534101 Andhra Pradesh India Natl Inst Technol Andhra Pradesh Tadepalligudem 534101 Andhra Pradesh India GITAM Univ Sch Technol Dept Mech Engn Hyderabad 502329 India

In this investigation, the friction stir welding (FSW) parameters were optimised by integrating the finite element analysis and non-dominated sorting genetic algorithm-ii. A thermo-mechanical finite element model to predict the peak temperature and flow stresses during welding were developed using Hyper Weld (R) FEM tool. The validity of the FE model was validated through experimental results. Using the validated FE model the process responses were predicted for varying conditions of welding speed, tool rotational speed, tilt angle, and shoulder diameter according to the design of experiments. The quadratic models for the process responses were postulated using response surface methodology and their adequacy was verified through analysis of variance. The problem was treated as multi-response optimisation due to the conflicting nature of the process responses and formulated to maximise the peak temperature and minimise the flow stress simultaneously. NSGA-ii was then implemented to derive the Pareto-optimal solutions and reported.

关键词： friction stir welding FSW thermo-mechanical modelling peak temperature flow stress response surface methodology multi-response optimisation non-dominated sorting genetic algorithm-ii NSGA-ii

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Multi-objective flexible job-shop scheduling problem with improved NSGA2 algorithm

Multi-objective flexible job-shop scheduling problem with im...

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第43届中国控制会议

作者： Hongyi Xu Minyun Li School of Engineering Hu Zhou University School of Intelligent Manufacturing Hu Zhou College

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Aiming at the situation of low scheduling efficiency resulting in resource waste in multi-objective flexible job-shop, this paper proposes an improved elitist non-dominated sorting genetic algorithm-ii(NSGA2). A population initialization method is designed based on three optimization objectives. At the same time, the individual fitness is defined according to the individual objective funtion and crowding distance. A parameter adaptive strategy is proposed to realize the adaptability of individual crossover and mutation probability. In the process of optimization, the Beetle Antennae Search(BAS) is used to carry out a second search on the dominated population to improve the single individual search speed of the algorithm and avoid falling into local optimal. Finally, the algorithm is verified on the classic Kacem dataset and the actual production case of the enter-prise, the experimental results show that the proposed method is superior to the existing methods, which proves the feasibility of the algorithm in multi-objective flexible job-shop scheduling problem.

关键词： Adaptive non-dominated sorting genetic algorithm-ii Beetle Antennae Search Initialization

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An Optimization of multi-level multi-objective cloud production systems with meta-heuristic algorithms

Decision Analytics Journal

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Decision Analytics Journal 2025年 14卷

作者： Razdar, Mohammadreza Adibi, Mohammad Amin Haleh, Hassan Department of Industrial Engineering Qazvin Branch Islamic Azad University Qazvin Iran Department of Industrial Engineering Golpayegan Isfahan University of Technology Golpayegan Iran

The inability of small companies to compete with large production systems has led to the sharing resources in cloud production systems among smaller companies to compensate for their production deficiencies. This study proposes a multi-objective, multi-level cloud production system by minimizing the maximum completion time of activities, the costs of the entire cloud production system, and the maximum risk of information disclosure. We use a support vector machine (SVM) to train the input data, including activities, micro activities, and services of production units. The correlation between the trained and predicted data from the machine learning model equals 0.9977, indicating this method's high accuracy and efficiency. The Lp-Metric, multi-objective grey wolf optimizer (MOGWO), and non-dominated sorting genetic algorithm-ii (NSGA-ii) algorithms were used to solve the problem using trained input data. The Lp-Metric results show that reducing the completion time of all activities and the maximum risk of information disclosure increases the costs of the entire cloud production system. We also examine the efficiency of the solution methods and demonstrate that MOGWO is more efficient in solving the cloud production system problem. © 2024 The Author(s)

关键词： Cloud production system Information disclosure risk Machine learning Multi-objective grey wolf optimizer non-dominated sorting genetic algorithm-ii Trustworthiness index

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A Multi-objective Multimodal Evolutionary algorithm Using a Novel Tournament and Environmental Selections

A Multi-objective Multimodal Evolutionary Algorithm Using a ...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Javadi, Mahrokh Mostaghim, Sanaz Otto von Guericke Univ Fac Comp Sci Magdeburg Germany

ISBN: (纸本)9781728190488

Multi-modal multi-objective optimization refers to multi-objective optimization problems that have more than one Pareto set. This paper proposes a new algorithm, modifying both the tournament and the environment selections. The purpose of this paper is to utilize the full potential of the parental population to produce diverse offspring and avoid producing duplicated solutions. In order to achieve the goal, we modify the selection mechanisms, which usually enforce the selection of solutions with high quality. This can lead to duplicates in the mating pool. Using the modified tournament selection, we aim to enable the algorithm to select diverse solutions in sparse regions of the search space. Our experiments agree with our expectations, confirming that our proposed algorithm is more effective than current competitor algorithms at approximating Pareto sets in most multimodal multi-objective optimization problems with different number of decision variables and objective functions.

关键词： Tournament Selection Multimodal Multi-objective Evolutionary algorithm non-dominated sorting genetic algorithm-ii Niching Techniques Mating Selection Environmental Selection

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Multi-Physics Multi-Objective Optimal Design of 5-DOF Magnetic Bearing System with High-speed Permanent Magnet Synchronous Motor 24

Multi-Physics Multi-Objective Optimal Design of 5-DOF Magnet...

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24th International Conference on Electrical Machines and Systems (ICEMS)

作者： Wang, Xiaoyuan Xu, Yuhao Tianjin Univ Tianjin Peoples R China

ISBN: (纸本)9788986510218

In view of the high-precision design requirements of suspension system under high-speed conditions, this paper used multi-objective genetic algorithm (MOGA) to carry out pareto optimization design of five-degree-of-freedom magnetic bearing system, and used finite element method to analyze the structure before and after optimization based on electromagnetic field, stress field and rotor dynamics theory respectively.

关键词： five-degree-of-freedom magnetic bearing system multi-objective genetic algorithm non-dominated sorting genetic algorithm-ii

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Data-driven multi-objective optimization design method for shale gas fracturing parameters

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JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2022年第0期99卷 104420-104420页

作者： Wang, Lian Yao, Yuedong Wang, Kongjie Adenutsi, Caspar Daniel Zhao, Guoxiang Lai, Fengpeng China Univ Petr State Key Lab Petr Resources & Prospecting Beijing 102249 Peoples R China CNPC Chuanqing Drilling Engn Co Ltd Changqing Downhole Technol Co Xian 710018 Peoples R China Kwame Nkrumah Univ Sci & Technol Fac Civil & Geoengn Dept Petr Engn Reservoir Simulat Lab Kumasi Ghana China Univ Geosci Sch Energy Resources Beijing 100083 Peoples R China

Accurate design of fracturing parameters in shale gas reservoirs does not only increase the productivity and profitability of wells but also minimize the damage fracturing fluids cause to fluid-sensitive formations. However, obtaining a highly accurate and optimized fracturing parameters with rapid as well cost-effective simulation run is difficult to achieve. This study proposed a framework for optimizing shale gas fracturing parameters based on least squares support vector regression (LSSVR) prediction model and non-dominated sorting genetic algorithm ii (NSGA-ii). This framework was named the multi-objective optimization prediction model (MOO-PM). In the proposed framework, the flowback ratio of fracturing fluid (FBR) and first month gas production (PROD) were considered as the objective functions while the fracturing parameters including horizontal length, number of fractured sections, fractured length, fracturing fluid injection rate, fracturing fluid viscosity, volume of fracturing fluid and proppant amount were chosen as the optimization variables. Firstly, based on field data the LSSVR prediction model was established. Secondly, the accuracy of the model was verified by the remaining field data. Subsequently, the Latin Hypercube method was used to construct a sample set of fracturing parameters within a predetermined range of variables and the corresponding objective functions were calculated by the LSSVR method. Finally, Pareto front was used to generate flowback ratio and first-month production by the NSGA-ii algorithm. To the best of our knowledge, this is the first time that LSSVR prediction model and multi objective method are applied simultaneously to optimize shale gas fracturing parameters. Furthermore, the novel multi-objective optimization framework was applied to data from the Weiyuan as well as the Changning shale gas reservoirs in Sichuan basin, southwest China. It was concluded that this framework could optimize fracturing parameters of shale g

关键词： Multi-objective optimization Fracturing parameters Shale gas non-dominated sorting genetic algorithm-ii

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MULTI-OBJECTIVE INVENTORY AND ROUTING MODEL FOR A MULTI- PRODUCT AND MULTI-PERIOD PROBLEM OF VETERINARY DRUGS

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2022年第4期29卷 464-486页

作者： Bardeji, Saeideh Farajzadeh Fakoor, Amir Mohammad Mirghaderi, Seyed-Hadi Ferdowsi Univ Mashhad FUM Dept Management Mashhad Iran Shiraz Univ Dept Management Shiraz Iran

In this study, a multi-objective, multi-product, and multi-period inventory management and routing model was designed for perishable products with a focus on veterinary drugs. The proposed model is a four-objective model to achieve a balance between the costs of drug distribution. The first and second objective functions are related to inventory management, covering inventory costs and demand forecast error, respectively. The third and fourth objective functions involve routing to minimize the transportation costs and greenhouse gas emissions, respectively. Data on three types of drugs were collected for 25 periods (weeks) from one drug distribution center and nine pharmacies in Iran. non-dominated sorting genetic algorithm-ii was employed to find a satisfactory solution. The meta-heuristic method was combined with a Multi-layer Perceptron artificial neural network algorithm to forecast the demand. The model was implemented in MATLAB software. The results show the ability of the proposed model and algorithm to find high-quality solutions.

关键词： Inventory Management and Routing Veterinary Drug Multi-Objective Multi-Product and Multi-Period Model non-dominated sorting genetic algorithm-ii Multi-Layer Perceptron Artificial Neural Network

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Operation and Multi-Objective Design Optimization of a Plate Heat Exchanger with Zigzag Flow Channel Geometry

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ENERGIES 2022年第21期15卷 8205-8205页

作者： Chen, Wei-Hsin Li, Yi-Wei Chang, Min-Hsing Chueh, Chih-Che Ashokkumar, Veeramuthu Saw, Lip Huat Natl Cheng Kung Univ Dept Aeronaut & Astronaut Tainan 701 Taiwan Tunghai Univ Res Ctr Smart Sustainable Circular Econ Taichung 407 Taiwan Natl Chin Yi Univ Technol Dept Mech Engn Taichung 411 Taiwan Tatung Univ Dept Mech & Mat Engn Taipei 104 Taiwan Saveetha Univ Saveetha Dent Coll Ctr Transdisciplinary ResDept Pharmacol Saveetha Inst Med & Tech SciBiorefineries Biofue Chennai 600077 Tamil Nadu India Tunku Abdul Rahman Univ Lee Kong Chian Fac Engn & Sci Kajang 43000 Malaysia

The performance of a plate heat exchanger (PHE) using water as the working fluid with zigzag flow channels was optimized in the present study. The optimal operating conditions of the PHE are explored experimentally by the Taguchi method, with effectiveness as the objective function. The results are further verified by the analysis of variance (ANOVA). In addition, the zigzag flow channel geometry is optimized by the non-dominated sorting genetic algorithm-ii (NSGA-ii), in which the effectiveness and pressure drop of the PHE are considered the two objective functions in the multi-objective optimization process. The experimental results show that the ratio of flow rates is the most important factor affecting the effectiveness of the PHE. The optimal operating conditions are the temperatures of 95 degrees C and 10 degrees C at the inlets of hot and cold water flows, respectively, with a cold/hot flow rate ratio of 0.25. The resultant effectiveness is 0.945. Three geometric parameters of the zigzag flow channel are considered, including the entrance length, the bending angle, and the fillet radius. The sensitivity analysis of the parameters reveals that a conflict exists between the two objective functions, and multi-objective optimization is necessary for the zigzag flow channel geometry. The numerical simulations successfully obtain the Pareto optimal front for the two objective functions, which benefits the determination of the geometric design for the zigzag flow channel.

关键词： plate heat exchanger Taguchi method analysis of variance zigzag flow channel multi-objective optimization non-dominated sorting genetic algorithm-ii

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Simultaneous maximization ofGenCo's revenue and minimization ofISO's purchase cost in adjacent power markets usingMOPSOandNSGA-iioptimizers

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2020年第11期30卷 1-20页

作者： Parmar, Ashokkumar Darji, Pranav SVNIT Dept Elect Engn Surat India

This paper presents a problem of simultaneous maximization of GenCo's (Generation Company) revenue and minimization of ISO's (Independent System Operator) purchase cost in interconnected power markets having different designs, namely capacity-plus-energy market and energy-only market. Both these problems have solved by multi-objective optimizers such as multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm-ii (NSGA-ii) using market data available in the literature. MOPSO and NSGA-ii are meta-heuristic optimization algorithms, which are used for several engineering applications to optimize multiple objectives simultaneously. The paper illustrates a comparative analysis of results with optimum allocation and pricing under different market conditions such as cost of recall, probability of recall and load forecasting (L. F.) error etc. Furthermore, main objective to emphasize on GenCo's total revenue maximization as well as ISO's purchase cost minimization instead of capacity cost only. Multi-objective optimization techniques have used to optimize both objectives simultaneously. Existing literature does not discuss the simultaneous solution of these problems by multi-objective optimization techniques, which makes this work novel. It has been observed that the capacity price, the maximum value of GenCo's revenue and the minimum value of ISO's purchase increases at a different rate, with increasing the probability of recall, cost of recall and L. F. error. GenCo's revenue and ISO's purchase cost have profoundly affected by the L. F. error, moderately affected by the probability of recall and very lowly affected by the cost of recall.

关键词： capacity market generation company independent system operator load serving entity multi-objective particle swarm optimization non-dominated sorting genetic algorithm-ii

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