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Groundwater Level Modeling Using multiobjective Optimization with Hybrid Artificial Intelligence Methods

ENVIRONMENTAL MODELING & ASSESSMENT 2024年第1期29卷 45-65页

作者： Banadkooki, Fatemeh Barzegari Haghighi, Ali Torabi Payame Noor Univ Agr Dept Tehran Iran Univ Oulu Water Energy & Environm Engn Res Unit Oulu Finland

Estimating groundwater level (GWL) fluctuations is essential for integrated water resource management in arid and semiarid regions. In this study, we used hybrid evolutionary algorithms to promote the multilayer perceptron (MLP) learning process. A hybrid metaheuristic algorithm was applied to overcome MLP difficulties in the learning process, including low conversions and local minima. Additionally, the hybrid model benefited from the advantages of two objective function procedures in obtaining MLP parameters resulting in a robust model regardless of over- and underestimation problems. These algorithms include the nondominated sorting genetic algorithm (NSGA II) and the multiobjective particle swarm optimization (MOPSO) algorithm in different combinations, including MLP-NSGA-II, MLP-MOPSO, MLP-MOPSO-NSGA-II, and MLP-2NSGA-II-MOPSO. Temperature, precipitation, and GWL datasets were used as model input candidates in various combinations with different delays. Finally, the best model inputs were selected using the coefficient of determination (R2). In summary, the contribution of the paper is the development of a robust model for estimating groundwater level fluctuations in arid and semiarid regions through the application of hybrid evolutionary algorithms, careful selection of input parameters, and the identification of a superior model that combines the advantages of multiple optimization techniques. The input parameters include temperature and precipitation with delays of 3, 6, and 9 months and GWL with delays of 1 to 12 months. In the next step, the performance of the different combinations of the MLP and hybrid evolutionary algorithms was evaluated using the root mean square error (RMSE), correlation coefficient (R), and mean absolute error (MAE) indices. The evaluation outcomes revealed that the MLP-2NSGA-II-MOPSO model, with RMSE=0.112, R2=0.97, and MAE=0.095, outperforms the other models in estimating GWL fluctuations. The selected model benefited from the adv

关键词： multiobjective genetic algorithm Particle swarm optimization Multilayer perceptron Groundwater Arid region

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Analysing skewness of credibilistic coherent trapezoidal fuzzy numbers: Implications for portfolio selection

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2025年第6期56卷 1143-1170页

作者： Mandal, Pawan Kumar Thakur, Manoj Mittal, Garima Indian Inst Technol Mandi Sch Math & Stat Sci Mandi 175075 Himachal Prades India Indian Inst Management Lucknow Decis Sci Lucknow Uttar Pradesh India

The outset of this manuscript involves introducing an analytical formula for the credibility of coherent trapezoidal fuzzy numbers, which represent an extended version of the traditional trapezoidal fuzzy numbers. With this formula, we present a precise counterpart to compute the credibilistic mean, semivariance, and skewness of coherent trapezoidal fuzzy numbers. Leveraging these analytical formulations, we construct a novel tri-objective portfolio selection problem, integrating credibilistic mean, semivariance, and skewness as objectives, along with some practical constraints. When used with the returns of the portfolio as a whole, the derived analytical expressions help to overcome the otherwise computationally expensive approach involving the simulation of results using individual assets' returns. The proposed model is solved by adapting an efficient multiobjective genetic algorithm. The algorithm has been specifically designed to solve portfolio selection models of this type. The efficiency of the proposed model is then demonstrated by taking a real-world scenario of financial stock market datasets from the NSE India. Based on the results of this study, it appears that the proposed novel tri-objective portfolio selection model produces promising results compared to the baseline model and the NIFTY 50 Index considered as a benchmark.

关键词： Coherent trapezoidal fuzzy numbers skewness multiobjective portfolio optimization multiobjective genetic algorithm K-medoids clustering

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Optimized Virtual Sources Distributions for 3-D Ultrafast Diverging Wave Compounding Imaging: A Simulation Study

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IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 2023年第10期70卷 1319-1328页

作者： Le Moign, Goulven Masson, Patrice Basset, Olivier Liebgott, Herve Quaegebeur, Nicolas Univ Sherbrooke Dept Mech Engn GAUS Sherbrooke PQ J1K 2R1 Canada Univ Lyon UJM St Etienne CREATIS INSA LyonCNRSUMR 5220UCBL1Inserm U1206 F-69100 Villeurbanne France

Ultrafast ultrasound imaging allows observing rapid phenomena;combined with 3-D imaging it has the potential to provide a more accurate analysis of organs which leads, in the end, to better diagnosis. Coherent compounding using diverging waves is commonly used to reconstruct high-quality images on large volumes while keeping the frame rate high enough to allow dynamic analysis. In practice, the virtual sources (VSs) that drive the diverging waves are often distributed in a deterministic way: following a regular grid, concentric rings, and spirals. Even though those deterministic distributions can offer various tradeoffs in terms of imaging performance, other distributions can be considered to improve imaging performance. It is herein suggested to look at alternative VSs distributions for optimizing the lateral resolution and the secondary lobes level (SLL) on several point spread functions (PSFs) by means of a multiobjective genetic algorithm. The optimization framework has led to seven pseudo-irregular distributions of VSs distributions that have not yet been found in the literature. An analysis of the imaging performance with a simulated phantom shows that these new distributions offer different tradeoffs between lateral resolution and contrast, respectively, measured on point-like reflectors and anechoic cysts. As an example, one of these optimized distributions improves the lateral resolution by 16% and gives equivalent contrast values on cysts and PSF isotropy properties, when compared to a concentric-rings-based distribution.

关键词： 2-D ultrasound probe 2-D ultrasound probe 3-D ultrafast ultrasound imaging 3-D ultrafast ultrasound imaging diverging wave compounding diverging wave compounding multiobjective genetic algorithm multiobjective genetic algorithm virtual sources (VSs) virtual sources (VSs)

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A Comprehensive Analytical Sizing Methodology for Transverse and Radial Flux Machines

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IEEE ACCESS 2023年 11卷 106063-106082页

作者： Ballestin-Bernad, Victor Egea-Caceres, Aritz Artal-Sevil, Jesus Sergio Dominguez-Navarro, Jose Antonio Univ Zaragoza Dept Elect Engn Zaragoza 50018 Spain Mondragon Univ Dept Elect & Comp Sci Arrasate Mondragon 20500 Spain

Transverse flux machines have the potential to offer high torque density in direct-drive vehicle traction applications. Besides, sizing equations are a wide-spread technique for transverse flux machines design, as their computational cost is much lower than the finite element method. In this paper a novel analytical sizing methodology for transverse and radial flux machines is presented, focusing on the current load and the pole length factor as the main design parameters. The motor specifications are intended for a light-duty electric vehicle application. As transverse flux machines have a single, hoop-shaped coil per phase that embraces the flux of all the pole pairs, their principle of operation and therefore their sizing equations differ from radial flux machines. The proposed analytical method allows to compare transverse and radial flux machines easily through a similarity analysis and a parametric study. Furthermore, the discrepancies between the analytical model and the finite element method are quantified and then included in previous equations. Then the analytical model is optimized with a multiobjective genetic algorithm in the final stage. According to the sizing methodology presented here, transverse flux machines have a superior performance than radial flux machines in terms of torque density and efficiency.

关键词： Analytical sizing equations electric vehicles finite element method multiobjective genetic algorithm permanent magnet synchronous machines transverse flux machines

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Novel explicit models for assessing the frictional resistance of pipe piles subjected to seismic effects

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JOURNAL OF SAFETY SCIENCE AND RESILIENCE 2025年第1期6卷 29-37页

作者： Al-Jeznawi, Duaa Sadik, Laith Alzabeebee, Saif Al-Janabi, Musab Aied Qissab Keawsawasvong, Suraparb Al Nahrain Univ Coll Engn Dept Civil Engn Jadriya Baghdad Iraq Univ Cincinnati Dept Civil & Architectural Engn & Construct Manage Cincinnati 45221 OH USA Univ Al Qadisiyah Dept Rd & Transport Engn Al Qadisiyah Iraq Thammasat Univ Thammasat Sch Engn Dept Civil Engn Res Unit Sci & Innovat Technol Civil Engn Infrastr Bangkok 12120 Pathumthani Thailand

This paper introduces novel explicit models to predict the frictional resistance of open and closed-ended pipe piles subjected to seismic loading. This research employs genetic programming (GP) and multiobjective genetic algorithm-based evolutionary polynomial regression (EPR-MOGA) to develop closed-form expressions for estimating pile frictional resistance, utilizing widely used input parameters for enhanced practicality and applicability in engineering practice. The proposed models are developed using only three input variables: the corrected standard penetration test (SPT) blow count (N1)60, the pile slenderness ratio (L/D), and the peak ground acceleration (PGA). This deliberate reduction in input complexity significantly enhances the models' applicability across a wide range of geotechnical scenarios and industries. The accuracy of the developed models was assessed via the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). In the case of the GP model, the evaluation metrics for the testing set for open-ended piles (R2, RMSE, and MAE values) are 0.89, 0.43, and 0.35, respectively, whereas the corresponding values for closed-ended piles are 0.93, 0.38, and 0.3, respectively. On the other hand, the EPR-MOGA approach achieves similarly encouraging results, with performance metrics of 0.92, 0.37, and 0.29 for open-ended piles and 0.91, 0.39, and 0.30 for closed-ended piles.

关键词： Seismic resilience genetic programming Evolutionary polynomial regression multiobjective genetic algorithm Pipe piles Frictional resistance Seismic excitation Corrected SPT test blow count

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Optimization of the Low-Impact Development Facility Area Based on a Surrogate Model

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WATER RESOURCES MANAGEMENT 2023年第14期37卷 5789-5806页

作者： Feng, Jing Yang, Yuanyuan Li, Jianzhu Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin 300350 Peoples R China Xian Univ Technol State Key Lab Ecohydraul Northwest Arid Reg China Xian 710048 Peoples R China

Low-impact development (LID) facilities constitute an important element of sponge cities. In this paper, a system of siting suitability indicators was established for green roofs, permeable pavement and rain gardens. Based on the ArcGIS superposition analysis tool, the suitable area and area upper limit of LID facilities in each subcatchment were determined. Nonlinear functions of the LID deployment area and various rainfall characteristics were constructed using a multilayer perceptron (MLP) as a surrogate model. The results showed that (1) the upper limit of the suitable area for green roof deployment in each subcatchment ranged from 5.46% to 22.87%. The upper limit of the suitable area for permeable pavement application varied between 8.31% and 23.65%. The upper limit of the suitable area for rain garden deployment varied between 18.12% and 36.95%;(2) a final LID facility layout scheme was selected at a total outflow of 10,700 m3, peak discharge of 3.49 m3/s and total investment of 0.76 billion yuan during the construction and management period;(3) the total outflow reduction rate under the LID model ranged from 32.15% to 40.29%, and the peak discharge reduction rate ranged from 17.93% to 36.20%;(4) the computation time of the surrogate optimization model could be reduced to 0.37% of that of Storm Water Management Model (SWMM) conventional numerical optimisation. Herein, the surrogate model can be used to determine the optimal solution for the LID facility placement area assuming fixed reduction amounts of the outflow and peak discharge.

关键词： LID facilities Multilayer perceptron multiobjective genetic algorithm Analytic hierarchy process SWMM

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Mixed Compositional Pattern-Producing Network-NeuroEvolution of Augmenting Topologies Method for the Locomotion Control of a Snake-Like Modular Robot

Mixed Compositional Pattern-Producing Network-NeuroEvolution...

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SICE International Symposium on Control Systems (SICE ISCS) part of the 10th SICE Multi-Symposium on Control Systems (MSCS)

作者： Song, Yu-Lun Chiu, Wei-Yu Manoharan, Shri Harish Natl Tsing Hua Univ Dept Elect Engn MOCaRL Lab Hsinchu Taiwan

ISBN: (纸本)9784907764760

Snake robots, which are a type of bionic robots, have high adaptability to various environments. Because of the unique structure and many degrees of freedom (DoFs) of snake robots, these robots can move on rough terrain or in narrow spaces. However, developing a controller for the locomotion of snake robots with many DoFs is challenging. A control model developed for a snake robot moving in one environment might be unsuitable in other environments. Thus, a mixed compositional pattern-producing network (CPPN)-NeuroEvolution of Augmenting Topologies (NEAT) method is proposed in this paper. This method is based on a neuroevolution algorithm and serpentine locomotion, and it can be used to control the locomotion of a snake-like modular robot in multiple environments with different obstacles. In the proposed method, a group of neural networks is constructed for each environment, and these networks are trained using a neuroevolution algorithm for several generations. Some pretrained neural networks for each environment are selected and then integrated to obtain mixed neural networks. These mixed neural networks are then evolved using a multiobjective genetic algorithm (MOGA) to improve the locomotion of a snake-like modular robot in multiple environments. The results of this study indicated that the mixed CPPN-NEAT method outperformed the combined CPPN-NEAT method and use of MOGA alone (without pretraining, combined networks, and mixed networks) in locomotion control for a snake-like modular robot in three environments with different obstacles.

关键词： multiobjective genetic algorithm neuroevolution algorithm serpentine locomotion snake-like modular robot

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Constraint handling methods for a generative envelope design using genetic algorithms: The case of a highly constrained problem

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INTERNATIONAL JOURNAL OF ARCHITECTURAL COMPUTING 2022年第3期20卷 587-609页

作者： Duclos-Prevet, Claire Guena, Francois Efron, Mariano Ecole Natl Super Architecture Paris Villette 144 Ave Flandres F-75019 Paris France

The use of genetic algorithms as generative and performance design techniques often involves, in practice, constraint handling, which can be a complex task. Moreover, environmental simulations are computationally expensive and managing constraints can avoid wasting time on infeasible solutions. Despite these two incentives, and the benefits of an immense literature, both applied and theorical, on constrained optimization, there are only few guidelines and tools directly applicable by architects to address this issue. This paper proposes to fill this gap by identifying, classifying, and implementing different constraint management techniques available to architects. Seven methods have been tested for a highly constrained envelope design problem, consisting in the optimization of a sun-shading system. Three of them are easily replicable to different types of projects while the four others need to find a problem-specific heuristic. It appears that the second category is more efficient but implies the use of generative techniques that are more difficult to implement than parametric models.

关键词： Optimization under constraint performative envelope design generative and sustainable design agent-based modeling multiobjective genetic algorithm visual comfort

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Design of the external forced air cooling control strategy for the bulb tubular turbine generator based on multi-objective optimization

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FRONTIERS IN ENERGY RESEARCH 2024年 11卷

作者： Wei, Han Zhang, Yukun Song, Houbin Qiu, Hongbo Guan, Zhijun Wang, Shuai Xie, Wenqi Lanzhou Univ Technol Lanzhou Gansu Peoples R China Zhengzhou Univ Light Ind Zhenzhou Peoples R China Gansu Chaijiaxia Hydropower Co Ltd Lanzhou Peoples R China

The compact internal structure of the bulb tubular turbine generator (BTTG) leads to poor ventilation and heat dissipation capability (VHDC). It will result in high-temperature faults and lead to losses for the hydro-power station, if the staff at the hydropower station fail to promptly regulate the external forced air cooling for effective heat dissipation. Thus, a specific and effective ventilation control strategy (VCS) is of utmost importance for the operation of the BTTG. In this study, the finite element method (FEM) was employed to solve the magnetic-flow-thermal coupled field of a 24-MW BTTG. The distribution characteristics of electromagnetic losses and their impact on the temperature field were revealed. Experimental validation was conducted to verify the effectiveness of the numerical results. The response surface methodology (RSM) was employed to design an experimental plan by building upon this foundation. Integrated with a multiobjective genetic algorithm (MOGA), an optimal relationship between ambient temperature, fan flow rate, and heating components' temperature was established. This relationship serves as the optimized ventilation control strategy for the BTTG. This research provides a theoretical framework for the formulation of operational guidelines to ensure the safe operation of generators.

关键词： bulb tubular turbine generator ventilation control strategy finite element method response surface method multiobjective genetic algorithm

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Clustering Ensemble: A multiobjective genetic algorithm based Approach

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Procedia Technology 2013年 10卷 443-449页

作者： Sujoy Chatterjee Anirban Mukhopadhyay Department of Computer Science and Engineering University of Kalyani Kalyani - 741235 India

Clustering ensemble refers to the problem of obtaining a final clustering of some data set from a set of input clustering solutions. In this article, the clustering ensemble problem has been modeled as a multiobjective optimization problem and a multiobjective evolutionary algorithm has been used for this purpose. The proposed multiobjective evolutionary clustering ensemble algorithm (MOECEA) evolves a clustering solution from the input clusterings by optimizing two criteria simultaneously. The first objective is to maximize the similarity of the resultant clustering with all the input clusterings, where the similarity between two clustering solutions is computed using adjusted Rand index. The second criteria is to minimize the standard deviation among the similarity scores in order to prevent the evolved clustering solution to be very similar with one of the input clusterings and very dissimilar with the others. The performance of the proposed algorithm has been compared with that of other well-known existing cluster ensemble algorithms for a number of artificial and real-life data sets.

关键词： Clustering Ensemble Validity indices multiobjective genetic algorithm Pareto optimality

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