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Multi-objective optimisation algorithm for routability and timing driven circuit clustering on FPGAs

IET COMPUTERS AND DIGITAL TECHNIQUES 2019年第4期13卷 273-281页

作者： Wang, Yuan Trefzer, Martin A. Bale, Simon J. Walker, James A. Tyrrell, Andy M. Univ Hertfordshire Sch Engn & Technol Hatfield Herts England Univ York Dept Elect Engn York N Yorkshire England

Circuit clustering algorithms fit synthesised circuits into field programmable gate array (FPGA) configurable logic blocks (CLBs) efficiently. This fundamental process in FPGA CAD flow directly impacts both effort required and performance achievable in subsequent place-and-route processes. Circuit clustering is limited by hardware constraints of specific target architectures. Hence, better circuit clustering approaches are essential for improving device utilisation whilst at the same time optimising circuit performance parameters such as, e.g. power and delay. In this study, the authors present a method based on multi-objective genetic algorithm (MOGA) to facilitate circuit clustering. They address a number of challenges including CLB input bandwidth constraints, improvement of CLB utilisation, minimisation of interconnects between CLBs. The authors' new approach has been validated using the 'Golden 20' MCNC benchmark circuits that are regularly used in FPGA-related literature. The results show that the method proposed in this study achieves improvements of up to 50% in clustering, routability and timing when compared to state-of-the-art approaches including VPack, T-VPack, RPack, DPack, HDPack, MOPack and iRAC. The key contribution of this work is a flexible EDA flow that can incorporate numerous objectives required to successfully tackle real-world circuit design on FPGA, providing device utilisation at increased design performance.

关键词： field programmable gate arrays logic design genetic algorithms network routing integrated circuit design timing circuits electronic design automation flexible EDA flow interconnect minimisation place-and-route process configurable logic blocks field programmable gate array timing driven circuit clustering multiobjective optimisation algorithm real-world circuit design FPGA-related literature Golden 20 MCNC benchmark circuits CLB utilisation CLB input bandwidth constraints multiobjective genetic algorithm time optimising circuit performance parameters FPGA CAD flow

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Scheduling Renewal of Water Pipes While Considering Adjacency of Infrastructure Works and Economies of Scale

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2010年第5期136卷 519-530页

作者： Nafi, Amir Kleiner, Yehuda Natl Res Council Canada Inst Res Construct Ottawa ON K1A 0R6 Canada Cemagref Engees Joint Res Unit Publ Util Management F-67070 Strasbourg France

Much research effort has been dedicated to the development of optimal strategies for rehabilitation and/or replacement of water mains. Some of the methods are intended for high-level planning of groups or cohorts of pipes, while others address low-level scheduling of individual water mains. This paper focuses on the latter aspect. An approach is proposed for the efficient scheduling of individual water mains for replacement in a short to medium predefined planning period and subject to various budgetary constraints. This approach also accounts for economies of scale considerations as well as harmonization with other known infrastructure works. A multiobjective genetic algorithm scheme is used as a tool to search a vast combinatorial solution space, comprising various combinations of pipe replacement schedules.

关键词： Water mains Pipes Renewal planning multiobjective genetic algorithm Roadwork Economies of scale

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Simultaneous filter tuning and calibration of the camera and inertial measurement unit camera for a vision inertial navigation system

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IET IMAGE PROCESSING 2020年第12期14卷 2756-2767页

作者： Arbabmir, Mohammadvali Ebrahimi, Masoud Tarbiat Modares Univ Dept Mech Engn Jalal AleAhmad Tehran Iran

In this study, a novel approach based on multi-objective genetic algorithm (MO-GA) is used for simultaneous tuning the unscented Kalman Filter (UKF) parameters and camera and inertial measurement unit camera calibration in a vision inertial navigation system (VINS). This system consists of visual odometry and inertial navigation system (INS) which integrates with a UKF. In order to obtain simultaneous tuning and calibration of the parameters and variables, the MO-GA minimises the root mean square error of the position and velocity of the vehicle on a selected trajectory of the benchmark data set. Then, the tuned parameters and calibrated variables are placed in the VINS and an adjusted VINS (AVINS) is obtained. For investigating the AVINS, the mentioned system is compared with INS only, VINS based on calibration data of the benchmark data set, and GPS/INS as Real Data on the identical trajectory. Furthermore, in order to evaluate the results of the proposed approach, the AVINS is examined in the second trajectory. The results indicate the proper performance of the presented approach in the simultaneous tuning the filter parameters and calibrating the variables of sensors that are used in the uncalibrated VINS.

关键词： cameras Global Positioning System genetic algorithms calibration Kalman filters inertial navigation nonlinear filters units (measurement) image sensors mean square error methods minimisation image filtering root mean square error minimisation adjusted vision inertial navigation system visual odometry unscented Kalman filter parameters MO-GA AVINS INS VINS inertial measurement unit camera calibration UKF multiobjective genetic algorithm

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Groundwater Level Modeling Using multiobjective Optimization with Hybrid Artificial Intelligence Methods

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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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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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High Efficiency Short Backfire Antenna Using Electromagnetically Hard Walls

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2015年 14卷 1614-1617页

作者： Bray, Matthew G. Lier, Erik Lockheed Martin Space Syst Newtown PA 18940 USA

This letter presents a high efficiency short backfire antenna (SBFA) using electromagnetically hard walls. A hard surface composed of low permittivity dielectric liner with longitudinal metal surface strips is affixed to the inside walls of a conventional SBFA's main reflector. The hard-walled SBFA was optimized to operate at L1 and L2 GPS bands with circular polarization using an R2 indicator based evolutionary multiobjective algorithm. A hard-walled SBFA with 2 lambda aperture diameter at L1 achieved 90% and 98% aperture efficiency at L1 and L2, respectively. Also the antenna exhibited more than 90% aperture efficiency over 16% bandwidth around L1. Compared to a conventional SBFA with the same aperture diameter the new antenna achieves 1.25 dB higher directivity at L1 and 0.76 dB at L2, as well as improved cross-polarization due to a more uniform aperture distribution.

关键词： Hard electromagnetic surface multiobjective genetic algorithm short backfire antenna strip-loaded wall

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Process Simulation and Optimization of Ammonia-Containing Gas Separation and Ammonia Recovery with Ionic Liquids

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ACS SUSTAINABLE CHEMISTRY & ENGINEERING 2021年第1期9卷 312-325页

作者： Zhan, Guoxiong Cao, Fei Bai, Lu Chang, Fei Zhou, Beiya Duan, Yuanmeng Zeng, Shaojuan Dong, Haifeng Li, Zengxi Zhang, Xiangping Univ Chinese Acad Sci Sino Danish Coll Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Green Proc & Engn State Key Lab Multiphase Complex Syst Beijing Key Lab Ion Liquids Clean ProcInst Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Coll Chem & Engn Beijing 100049 Peoples R China Bohai Univ Coll Math & Phys Jinzhou 121013 Liaoning Peoples R China

Ionic liquids (ILs) have been experimenta ly proved to be effective for ammonia-containing gas separation and recovery. A systematic strategy including thermodynamic models, process simulation, multiobjective genetic algorithm, and assessment for novel IL-based separation of ammonia-containing tail gas and ammonia recovery process was proposed. The conventional IL ([C(4)Mim][NTf2]) and the functional IL ([C(4)im][NTf2]) were selected, and their NH3 removal performance was investigated. Physical properties of models of IL systems were established with temperature-dependent equations, and gas-liquid phase equilibria of the NH3-IL system were molded with the nonrandom two liquid model equation. Total purification cost (TPC), total process CO2 emission (TPCOE), and thermodynamic efficiency (eta(eff)) were selected as the objective functions to be optimized. Process simulation results indicated that under same operational parameters, using functional ILs results in lower NH3 concentration in purified gas and higher removal efficiency than that of conventional ILs. After optimization, a series of solutions satisfying the constraints was provided by the Pareto front. The lowest objective functions can achieve 0.0211 $/N m(3) (TPC), 265.67 kg CO2/h (TPCOE), and 48.05% (eta(eff)). Moreover, using functional ILs could greatly decrease purification cost and energy consumption and avoid wastewater discharge, which is an inevitable environmental problem in the water scrubbing process.

关键词： NH3-containing tail gas separation process simulation multiobjective genetic algorithm ionic liquid

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Unsupervised Pixel Classification in Satellite Imagery: A Two-stage Fuzzy Clustering Approach

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FUNDAMENTA INFORMATICAE 2008年第4期86卷 411-428页

作者： Mukhopadhyay, Anirban Maulik, Ujjwal Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 W Bengal India Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India

A popular approach for landcover classification in remotely sensed satellite images is clustering the pixels in the spectral domain into several fuzzy partitions. It has been observed that performance of the clustering algorithms deteriorate with more and more overlaps in the data sets. Motivated by this observation, in this article a two-stage fuzzy clustering algorithm is described that utilizes the concept of points having significant membership to multiple classes. The points situated in the overlapped regions of different clusters are first identified and excluded from consideration while clustering. Thereafter, these points are given class labels based on Support vector Machine classifier which is trained by the remaining points. The well known Fuzzy C-Means algorithm and some recently proposed genetic clustering schemes are utilized in the process. The effectiveness of the two-stage clustering technique has been demonstrated on IRS remote sensing satellite images of the cities of Bombay and Calcutta and compared with other well known clustering techniques. Also statistical significance test has been carried out to establish the statistical significance of the clustering results.

关键词： Unsupervised pixel classification significant multiclass membership cluster validity index variable string length genetic algorithm multiobjective genetic algorithm Support Vector Machine

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Signal coordination scheme based on traffic emission

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IET INTELLIGENT TRANSPORT SYSTEMS 2016年第2期10卷 89-96页

作者： Ji, Yanjie Hu, Bo Hill, Graeme Guo, Weihong Blythe, Phil Gao, Liangpeng Southeast Univ Jiangsu Key Lab Urban ITS Nanjing Peoples R China Newcastle Univ Sch Civil Engn & Geosci Transport Operat Res Grp Newcastle Upon Tyne NE1 7RU Tyne & Wear England

This study presents a new optimal signal coordination scheme with an emphasis on emission reduction as a result of minimised passenger delay. By analysing the limitation of the algebraic method, a new algorithm using arrival-departure curves was proposed. A microscopic emission simulation platform based on VISSIM (a microscopic multi-modal traffic flow simulation model) and comprehensive modal emission model (CMEM) was established to estimate traffic emission reduction. A multi-objective genetic algorithm was used to find Pareto solutions that simultaneously reduce both passenger delay and vehicle emissions. Using the real-world traffic data collected in Changzhou city, an example of the signal coordination scheme was developed and its impact on emissions was simulated in the VISSIM-CMEM platform. The simulation results show that the proposed scheme achieved an average emission reduction of 28.8% on public transport vehicles and 18.7% on all vehicles over existing signal schemes.

关键词： signal processing air pollution control genetic algorithms algebra Pareto optimisation optimal signal coordination scheme algebraic method arrival-departure curves microscopic emission simulation platform VISSIM microscopic multimodal traffic flow simulation model traffic emission reduction multiobjective genetic algorithm comprehensive modal emission model CMEM Pareto solutions real-world traffic data Changzhou city

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Prediction of irrigation event occurrence at farm level using optimal decision trees

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2019年 157卷 173-180页

作者： Gonzalez Perea, R. Camacho Poyato, E. Montesinos, P. Rodriguez Diaz, J. A. Univ Cordoba Dept Agron Campus RabanalesEdif da Vinci E-14071 Cordoba Spain

Irrigation water demand is highly variable and depends on farmers' decision about when to irrigate. Their decision affects the performance of the irrigation networks. An accurate daily prediction of irrigation events occurrence at farm scale is a key factor to improve the management of the irrigation districts and consequently the sustainability of the irrigated agriculture. In this work, a hybrid heuristic methodology that combines Decision Trees and genetic algorithm has been developed to find the optimal decision tree to model farmer's behaviour, predicting the occurrence of irrigation events. The methodology has been tested in a real irrigation district and results showed that the optimal models developed have been able to predict between 68% and 100% of the positive irrigation events and between 93% and 100% of the negative irrigation events.

关键词： Artificial intelligence multiobjective genetic algorithm Irrigation scheduling Expert systems

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