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2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

作者： Handong Zhang Ling Fang Yuwan Cen School of Electrical Engineering & Information Anhui University of Technology Maanshan Anhui China

Hybrid particle swarm optimization(PSO) is such a kind of algorithms, which combine other algorithms or technologies to enhance the basic PSO. This paper compares three representative hybrid PSOs: breeding PSO, immune PSO, and chaos PSO. These comparisons are carried out in following aspects: the goals for hybrid, basic hybrid modes, the key implementation steps, optimization performances and so on. Through these comparisons and analysis, three hybrid algorithms’ construction, implementation and respective application scope are summarized,especially, the optimization performances of three hybrid PSOs have been researched carefully. The goal is to find features of the three HPSOs and provide some guidence to develop new kinds of HPSO.

关键词： hybrid PSO algorithm comparison optimization performance immune PSO chaos PSO

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Assessment of a heuristic algorithm for scheduling Theater Security Cooperation naval missions

Assessment of a heuristic algorithm for scheduling Theater S...

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作者： Rodman, Michael R. Naval Postgraduate School

学位级别：硕士

Theater Security Cooperation (TSC) is a U. S. strategy for improving multi-national relationships through cooperative efforts. Spitz develops the Central-West Africa Resource and Mission Allocation (CARMA) optimization model, which posits a naval vessel carrying various expeditionary partnership teams to transit an area of responsibility and conduct missions garnering the maximum amount of TSC value. CARMA can be solved with formal, mixed-integer optimization, at the expense of computational time. This thesis modifies the original Spitz's scenarios to test H-CARMA, a fast heuristic algorithm developed by Dwyer, and its performance under shorter planning horizons, multiple budget constraints and different distribution of missions and TSC value across countries. Most of the scenarios evidence shortcomings of H-CARMA that were not apparent in the earlier scenarios tested by Dwyer. In all but one of the reviewed cases, H-CARMA generates solutions with total TSC value less than 81% of those using Spitz's algorithms, and, in the worst of these cases, the solution only achieves 51 percent. When there is no slack in terms of time and budget, MIP solutions outperform those of H-CARMA by more than 25% in most cases examined. We identify sources for some of these deficiencies and recommend changes to address them.

关键词： Gulf of Guinea Logistics Scheduling Optimization Mission Routing Theater Security Cooperation algorithm comparison

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Research on Image Feature Matching algorithm Applied to UAV Obstacle Avoidance

Research on Image Feature Matching Algorithm Applied to UAV ...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Zhu Haijie Li Jiafeng Cheng Sifan Qin Linlin Univ Sci & Technol China Dept Automat Hefei 230024 Peoples R China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158556

The ORB, SIFT, and SURF algorithms are currently common feature matching algorithms. These three algorithms have certain differences in execution speed and matching robustness. In this paper, the data sets provided by Mikolajczyk and Schmid are used to compare the algorithms in terms of execution speed, image transformation robustness, and noise robustness. The conclusion of this paper is that the ORB algorithm is the fastest, the SURF algorithm is the most robust, and the SIFT algorithm has the most feature points.

关键词： ORB SIFT SURF Feature Matching algorithm comparison

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A three-stage optimization method for the classroom envelope in primary and secondary schools in China

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JOURNAL OF BUILDING ENGINEERING 2022年 52卷

作者： Xu, Yizhe Yan, Chengchu Pan, Yan Zhao, Ke Li, Mei Zhu, Faxing Jiang, Yanlong Nanjing Tech Univ Coll Urban Construction 200 North Zhongshan Rd Nanjing 210009 Peoples R China Nanjing Univ Aeronaut & Astronaut Key Lab Aircraft Environm Control & Life Support MIIT 29 Yudao St Nanjing 210016 Peoples R China Technol & Higher Educ Inst Hong Kong Fac Sci & Technol Hong Kong Peoples R China Chuzhou Univ Sch Biol & Food Engn Chuzhou 239000 Peoples R China

The design optimization of primary and secondary school classrooms should consider the effects of energy, thermal comfort, and daylighting. This paper proposes a three-stage optimization design method of classroom envelope based on multi-objective optimization to realize the efficient and accurate optimization of primary and secondary school classrooms. The method is divided into three modules: building modeling and analysis, meta-model training and hyperparameter optimization, and multi-objective optimization algorithm comparison. The application effect of electrochromic technology in typical primary and secondary school classrooms is discussed through sensitivity analysis, and an improved algorithm SEGA-Q based on GA is proposed. In addition, the performance of three typical multi-objective optimization algorithms in three objective optimizations (i.e., energy, thermal comfort, and daylighting) is compared. Finally, two typical school classrooms are taken as cases to verify the effectiveness of the proposed optimization method. The results show that the application significance of the electrochromic window indicates skylights > south side window > north side window. The SEGA-Q algorithm has more accuracy, stability, and convergence speed advantages in meta-model training and hyperparametric optimization, but its calculation time increases significantly. Among the three multiobjective optimization algorithms, the NSGA-II algorithm has the most significant advantages in most aspects. In addition, compared with the benchmark schemes, the optimized common classroom can reduce the TES by 692.5 kWh at most and increase the PT by 41.9% and UDINO by 57.4%. The optimized classroom with skylight can reduce TES by 1261.9 kWh at most and increase PT by 26% and UDINO by 78.5%.

关键词： Envelope optimization Hybrid algorithm MFNN School classroom algorithm comparison

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Unveiling the development of intracranial injury using dynamic brain EIT: an evaluation of current reconstruction algorithms

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PHYSIOLOGICAL MEASUREMENT 2017年第9期38卷 1776-1790页

作者： Li, Haoting Chen, Rongqing Xu, Canhua Liu, Benyuan Tang, Mengxing Yang, Lin Dong, Xiuzhen Fu, Feng Fourth Mil Med Univ Dept Biomed Engn Xian 710032 Shaanxi Peoples R China Imperial Coll London Dept Biomed Engn London SW7 2AZ England

Objective: Dynamic brain electrical impedance tomography (EIT) is a promising technique for continuously monitoring the development of cerebral injury. While there are many reconstruction algorithms available for brain EIT, there is still a lack of study to compare their performance in the context of dynamic brain monitoring. Approach: To address this problem, we develop a framework for evaluating different current algorithms with their ability to correctly identify small intracranial conductivity changes. Firstly, a simulation 3D head phantom with realistic layered structure and impedance distribution is developed. Next several reconstructing algorithms, such as back projection (BP), damped least-square (DLS), Bayesian, split Bregman (SB) and GREIT are introduced. We investigate their temporal response, noise performance, location and shape error with respect to different noise levels on the simulation phantom. The results show that the SB algorithm demonstrates superior performance in reducing image error. To further improve the location accuracy, we optimize SB by incorporating the brain structure-based conductivity distribution priors, in which differences of the conductivities between different brain tissues and the inhomogeneous conductivity distribution of the skull are considered. We compare this novel algorithm (called SB-IBCD) with SB and DLS using anatomically correct head shaped phantoms with spatial varying skull conductivity. Main results and Significance: The results showed that SB-IBCD is the most effective in unveiling small intracranial conductivity changes, where it can reduce the image error by an average of 30.0% compared to DLS.

关键词： dynamic electrical impedance tomography brain injury algorithm comparison inhomogeneous brain conductivity distribution

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Maximum number of generations as a stopping criterion considered harmful

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APPLIED SOFT COMPUTING 2022年第0期128卷

作者： Ravber, Miha Liu, Shih-Hsi Mernik, Marjan Crepinsek, Matej Univ Maribor Fac Elect Engn & Comp Sci Koroska cesta 46 Maribor 2000 Slovenia Calif State Univ Fresno Dept Comp Sci Fresno CA 93740 USA

Evolutionary algorithms have been shown to be very effective in solving complex optimization problems. This has driven the research community in the development of novel, even more efficient evolutionary algorithms. The newly proposed algorithms need to be evaluated and compared with existing state-of-the-art algorithms, usually by employing benchmarks. However, comparing evolutionary algorithms is a complicated task, which involves many factors that must be considered to ensure a fair and unbiased comparison. In this paper, we focus on the impact of stopping criteria in the comparison process. Their job is to stop the algorithms in such a way that each algorithm has a fair opportunity to solve the problem. Although they are not given much attention, they play a vital role in the comparison process. In the paper, we compared different stopping criteria with different settings, to show their impact on the comparison results. The results show that stopping criteria play a vital role in the comparison, as they can produce statistically significant differences in the rankings of evolutionary algorithms. The experiments have shown that in one case an algorithm consumed 50 times more evaluations in a single generation, giving it a considerable advantage when max gen was used as the stopping criterion, which puts the validity of most published work in question. (C) 2022 The Author(s). Published by Elsevier B.V.

关键词： Evolutionary algorithms Stopping criteria Benchmarking algorithm termination algorithm comparison

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Infrared image segmentation algorithm for defect detection based on FODPSO

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INFRARED PHYSICS & TECHNOLOGY 2019年 102卷 103051-000页

作者： Tang, Qingju Gao, Shuaishuai Liu, Yongjie Yu, Fengyun Heilongjiang Univ Sci & Technol Sch Mech Engn Harbin 150022 Heilongjiang Peoples R China

To address the problems of high noise and fuzzy edges of acquired infrared images when detecting debonding defective components of SiC thermal barrier coating structure by pulsed infrared thermal waves, an image segmentation method based on fractional Darwin particle swarm optimization algorithm (FODPSO) is adopted to reconstruct infrared image sequences. This algorithm adopts fractional calculus to control the convergence of the system, and can perform n - 1 threshold optimization calculation on n - 1 level images. Experimental results show that FODPSO algorithm effectively overcomes the shortcomings of traditional algorithm such as easy to enter local optimal and slow convergence speed, and can realize the edge identification of SiC coating defects. The processed image improves the contrast and signal-to-noise ratio.

关键词： Fractional order Fractional-order Darwin particle swarm algorithm Infrared image Edge detection Multi-scale segmentation algorithm comparison SiC coating

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Multi-Objective Optimization for Energy Performance Improvement of Residential Buildings: A Comparative Study

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ENERGIES 2017年第2期10卷 245-245页

作者： Li, Kangji Pan, Lei Xue, Wenping Jiang, Hui Mao, Hanping Jiangsu Univ Sch Elect Informat Engn Zhenjiang 212013 Peoples R China Jiangsu Univ Inst Agr Engn Zhenjiang 212013 Peoples R China

Numerous conflicting criteria exist in building design optimization, such as energy consumption, greenhouse gas emission and indoor thermal performance. Different simulation-based optimization strategies and various optimization algorithms have been developed. A few of them are analyzed and compared in solving building design problems. This paper presents an efficient optimization framework to facilitate optimization designs with the aid of commercial simulation software and MATLAB. The performances of three optimization strategies, including the proposed approach, GenOpt method and artificial neural network (ANN) method, are investigated using a case study of a simple building energy model. Results show that the proposed optimization framework has competitive performances compared with the GenOpt method. Further, in another practical case, four popular multi-objective algorithms, e.g., the non-dominated sorting genetic algorithm (NSGA-II), multi-objective particle swarm optimization (MOPSO), the multi-objective genetic algorithm (MOGA) and multi-objective differential evolution (MODE), are realized using the propose optimization framework and compared with three criteria. Results indicate that MODE achieves close-to-optimal solutions with the best diversity and execution time. An uncompetitive result is achieved by the MOPSO in this case study.

关键词： building performance design multi-objective optimization residential building algorithm comparison

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Optimal Combination of Predictors and algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data

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REMOTE SENSING 2019年第4期11卷 414-414页

作者： Chen, Lin Wang, Yeqiao Ren, Chunying Zhang, Bai Wang, Zongming Chinese Acad Sci Northeast Inst Geog & Agroecol Key Lab Wetland Ecol & Environm Changchun 130102 Jilin Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Rhode Isl Dept Nat Resources Sci 1 Greenhouse Rd Kingston RI 02881 USA

Accurate forest above-ground biomass (AGB) mapping is crucial for sustaining forest management and carbon cycle tracking. The Shuttle Radar Topographic Mission (SRTM) and Sentinel satellite series offer opportunities for forest AGB monitoring. In this study, predictors filtered from 121 variables from Sentinel-1 synthetic aperture radar (SAR), Sentinal-2 multispectral instrument (MSI) and SRTM digital elevation model (DEM) data were composed into four groups and evaluated for their effectiveness in prediction of AGB. Five evaluated algorithms include linear regression such as stepwise regression (SWR) and geographically weighted regression (GWR);machine learning (ML) such as artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF). The results showed that the RF model used predictors from both the Sentinel series and SRTM DEM performed the best, based on the independent validation set. The RF model achieved accuracy with the mean error, mean absolute error, root mean square error, and correlation coefficient in 1.39, 25.48, 61.11 Mgha(-1) and 0.9769, respectively. Texture characteristics, reflectance, vegetation indices, elevation, stream power index, topographic wetness index and surface roughness were recommended predictors for AGB prediction. Predictor variables were more important than algorithms for improving the accuracy of AGB estimates. The study demonstrated encouraging results in the optimal combination of predictors and algorithms for forest AGB mapping, using openly accessible and fine-resolution data based on RF algorithms.

关键词： optimal predictors algorithm comparison Sentinel-1 SAR Sentinel-2 MSI SRTM DEM forest AGB mapping

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Machine learning exhibited excellent advantages in the performance simulation and prediction of free water surface constructed wetlands

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022年 309卷 114694-114694页

作者： Guo, Changqiang Cui, Yuanlai Chinese Acad Sci Nanjing Inst Geog & Limnol Key Lab Watershed Geog Sci Nanjing 210008 Peoples R China Changjiang River Sci Res Inst Changjiang Water Re Key Lab Basin Water Resources & Ecoenvironm Sci H Wuhan 430010 Peoples R China Wuhan Univ State Key Lab Water Resource & Hydropower Engn Sc Wuhan 430072 Peoples R China

Optimizing the design and operation parameters of free water surface constructed wetlands (FWS CWs) in runoff regulation and wastewater treatment is necessary to improve the comprehensive performance. In this study, nine machine learning (ML) algorithms were successfully developed to optimize the parameter combinations for FWS CWs. The scale effect of surface area on wetland performance was determined based on consistently smaller predictions (-6.2% to -28.9%) of the nine well-established ML algorithms. The models most suitable for FWS CW performance simulation and prediction were random forest and extra trees algorithms because of their high R-2 values (0.818 in both) with the training set and low mean absolute relative errors (4.7% and 3.8%, respectively) with the test set. Results from feature analysis of the six tree-based algorithms emphasized the importance of water depth and layout of inlet and outlet, and revealed the negligible effect of the aspect ratio. Feature importance and partial dependence analysis enhanced the interpretability of the tree-based algorithms. The proposed ML algorithms enabled the implementation of an extended scenario at a low cost in real time. Therefore, ML algorithms are suitable for expressing the complex and uncertain effects of the design and operation parameters on the performance of FWS CWs. Acquiring datasets consisting of more extensive, uniform, and unbiased parameter combinations is crucial for developing more robust and practical ML algorithms for the optimal design of FWS CWs.

关键词： Wetland performance algorithm comparison Scenario prediction Feature importance Interpretability

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