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Shape optimization of U-shaped steel dampers subjected to cyclic loading using an efficient hybrid approach

ENGINEERING STRUCTURES 2019年 197卷

作者： Khatibinia, Mohsen Jalaipour, Mehdi Gharehbaghi, Sadjad Univ Birjand Dept Civil Engn Birjand Iran Behbahan Khatam Alanbia Univ Technol Dept Civil Engn Behbahan Iran

U-shaped steel damper (USSD), as an energy dissipation device, has been recommended in the literature for using in the isolation systems. This type of damper is capable of appropriately dissipating the input energy which a structure imparts from an earthquake. The capability is provided through a large range of plastic deformations occurred in the USSD. This paper aimed at presenting a methodology structured in the framework of a shape optimization problem to enhance the seismic energy dissipation and deformation capability of the USSD under cyclic loading. To achieve this goal, the straight part, thickness and height of the USSD were considered as the design variables of the optimization problem and optimized through maximizing the ratio of energy dissipation through plastic deformation to the maximum equivalent plastic strain. In order to find the optimum shape of the USSD under cyclic loading, a hybrid approach consisted of two phases was applied. In the first phase, as an alternative for the time-consuming finite element analysis, a support vector machine (SVM) approach was trained, tested and used to predict the inelastic responses of the USSD. In the second phase, a modified particle swarm optimization (PSO) algorithm was adopted to find the optimum shape of the USSD subjected to two critical directions of cyclic loading. After finding the optimum shape of the USSD, the energy dissipation and deformation capability of the optimum shaped-USSD were assessed. Results demonstrate that the proposed shape optimization methodology renders an optimum-shaped USSD with significantly improved energy dissipation and deformation capability compared with those of available in the literature.

关键词： U-shaped steel damper Cyclic Loading Energy dissipation Shape optimization Support vector machine particle swarm optimization algorithm

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optimization of the Local Split-Window algorithm for FY-4A Land Surface Temperature Retrieval

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REMOTE SENSING 2019年第17期11卷

作者： Wang, Lijuan Guo, Ni Wang, Wei Zuo, Hongchao Lanzhou Univ Coll Atmospher Sci Lanzhou 730000 Gansu Peoples R China China Meteorol Adm Key Lab Drought Climate Change & Disaster Reduct Key Lab Drought Climate Change & Disaster Reduct Inst Arid Meteorol Lanzhou 730020 Gansu Peoples R China

FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). In this paper, different underlying surfaces-observed data were applied to evaluate the applicability of the local split-window algorithm for FY-4A, and the local split-window algorithm parameters were optimized by the artificial intelligent particle swarm optimization (PSO) algorithm to improve the accuracy of retrieved LST. Results show that the retrieved LST can efficiently reproduce the diurnal variation characteristics of LST. However, the estimated values deviate hugely from the observed values when the local split-window algorithms are directly used to process the FY-4A satellite data, and the root mean square errors (RMSEs) are approximately 6K. The accuracy of the retrieved LST cannot be effectively improved by merely modifying the emissivity-estimated model or optimizing the algorithm. Based on the measured emissivity, the RMSE of LST retrieved by the optimized local split-window algorithm is reduced to 3.45 K. The local split-window algorithm is a simple and easy retrieval approach that can quickly retrieve LST on a regional scale and promote the application of FY-4A satellite data in related fields.

关键词： land surface temperature split-window algorithm FY-4A particle swarm optimization algorithm

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Knowledge modeling for root cause analysis of complex systems based on dynamic fuzzy cognitive maps

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IFAC-PapersOnLine 2018年第21期51卷 13-18页

作者： Yue, Weichao Chen, Xiaofang Huang, Keke Zeng, Zhaohui Xie, Yongfang School of Information Science and Engineering Central South University Changsha410083 China

This paper proposes a knowledge model for root cause analysis (RCA) of complex systems based on fuzzy cognitive maps (FCMs) and particle swarm optimization algorithm (PSO). The process knowledge and experience of technicians can be captured by FCMs that are characterized by briefness of knowledge modeling and execution. The traditional methods for RCA based on FCMs are restricted to fixed incidence matrix. However, the individualized features are there existing in each system of the same kind, therefore fixed weights are unreasonable. PSO is introduced to detect the weight that can reveal the individualized features of systems among concepts of FCMs. And then a dynamic knowledge model for RCA is obtained, including predictive, diagnostic, and hybrid RCA. The three types RCA can be used for forecasting future event of output, identifying root cause and presenting measures of abnormal event. The effectiveness of proposed method is validated in aluminum reduction process, and the experiments results show the proposed method is effective and application potential. © 2018

关键词： Cognitive systems Fuzzy rules Large scale systems Models Ore reduction particle swarm optimization (PSO) Aluminum reduction Fuzzy cognitive map Fuzzy cognitive maps (FCMs) Incidence matrices Knowledge particle swarm optimization algorithm Process knowledge Root cause analysis

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Adaptive Balance Factor in particle swarm optimization 1

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6th International Conference on Soft Computing for Problem Solving (SocProS)

作者： Sharma, Siddhi Kumari Sharma, R. S. Rajasthan Tech Univ Kota India

ISBN: (数字)9789811033223

ISBN: (纸本)9789811033223;9789811033216

particle swarm optimization (PSO) is a refined optimization method, that has drawn interest of researchers in different areas because of its simplicity and efficiency. In standard PSO, particles roam over the search area with the help of two accelerating parameters. The proposed algorithm is tested over 12 benchmark test functions and compared with basic PSO and two other algorithms known as Gravitational search algorithm (GSA) and Biogeography based optimization (BBO). The result reveals that ABF-PSO will be a competitive variant of PSO.

关键词： Meta-heuristic optimization techniques particle swarm optimization algorithm swarm intelligence Acceleration coefficients Nature inspired algorithm

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Hybrid QPSO-NNIA2 algorithm for Multi-Objective optimization Problem

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2019年第8期33卷

作者： Zhang, Lan Northwestern Polytech Univ Dept Appl Math Xian Shaanxi Peoples R China Xian Aeronaut Polytech Inst Basic Dept Xian Shaanxi Peoples R China

To improve the convergence and distribution of a multi-objective optimization algorithm, a hybrid multi-objective optimization algorithm, based on the quantum particle swarm optimization (QPSO) algorithm and adaptive ranks clone and neighbor list-based immune algorithm (NNIA2), is proposed. The contribution of this work is threefold. First, the vicinity distance was used instead of the crowding distance to update the archived optimal solutions in the QPSO algorithm. The archived optimal solutions are updated and maintained by using the dynamic vicinity distance based m-nearest neighbor list in the QPSO algorithm. Secondly, an adaptive dynamic threshold of unfitness function for constraint handling is introduced in the process. It is related to the evolution algebra and the feasible solution. Thirdly, a new metric called the distribution metric is proposed to depict the diversity and distribution of the Pareto optimal. In order to verify the validity and feasibility of the QPSO-NNIA2 algorithm, we compare it with the QPSO, NNIA2, NSGA-II, MOEA/D, and SPEA2 algorithms in solving unconstrained and constrained multi-objective problems. The simulation results show that the QPSO-NNIA2 algorithm achieves superior convergence and superior performance by three metrics compared to other algorithms.

关键词： Multi-objective optimization problem Pareto optimal solution vicinity distance Quantum particle swarm optimization algorithm particle swarm optimization algorithm

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particle swarm optimization Approach for the Segmentation of Retinal Vessels from Fundus Images 14th

Particle Swarm Optimization Approach for the Segmentation of...

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14th International Conference on Image Analysis and Recognition (ICIAR)

作者： Khomri, Bilal Christodoulidis, Argyrios Djerou, Leila Babahenini, Mohamed Chaouki Cheriet, Farida Polytech Montreal Lab LIV4D Montreal PQ Canada Univ Biskra LESIA Lab Biskra Algeria

ISBN: (纸本)9783319598765;9783319598758

In this paper, we propose to use the particle swarm optimization (PSO) algorithm to improve the Multi-Scale Line Detection (MSLD) method for the retinal blood vessel segmentation problem. The PSO algorithm is applied to find the best arrangement of scales in the basic line detector method. The segmentation performance was validated using a public high-resolution fundus images database containing healthy subjects. The optimized MSLD method demonstrates fast convergence to the optimal solution reducing the execution time by approximately 35%. For the same level of specificity, the proposed approach improves the sensitivity rate by 3.1% compared to the original MSLD method. The proposed method will allow to reduce the amount of missing vessels segments that might lead to false positives of red lesions detection in CAD systems used for diabetic retinopathy diagnosis.

关键词： Retinal blood vessel segmentation optimization particle swarm optimization algorithm Multi-scale Line Detection

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Optimal Plan for Step-Stress Accelerated Life Test with Two Stress Variables for Lognormal Data

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY TRANSACTION A-SCIENCE 2018年第A4期42卷 2259-2271页

作者： Hakamipour, Nooshin Buein Zahra Tech Univ Dept Math Buein Zahra Qazvin Iran

A simple step-stress accelerated life testing plan with two stress variables is considered, when the failure times in each level of stress follow the lognormal distribution. The lognormal distribution is commonly used to model certain types of data that arise in several fields of engineering such as, for example, different types of lifetime data or coefficients of wear and friction. The problem of choosing the optimal times to change the stress level is investigated by minimizing the asymptotic variance of the reliability estimate and maximizing the determinant of Fisher information matrix. In this paper, we obtain the optimal bivariate step-stress accelerated life test using both the criteria. Due to the nonlinearity and complexity of problem, the particle swarm optimization algorithm is developed to calculate the optimal hold times. In this method, the research speed is very fast and the optimization ability is more. To illustrate the effect of the initial estimates on the optimal values, sensitivity analysis is performed. Finally, numerical studies are discussed to illustrate the proposed criterion. Simulation results show that the proposed optimum plan is robust.

关键词： Bivariate step-stress accelerated life test Determinant optimality Optimal design particle swarm optimization algorithm Reliability

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Study on the Bike-sharing Inventory Rebalancing and Vehicle Routing for Bike-sharing System

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Transportation Research Procedia 2019年 39卷 624-633页

作者： Lei Shi Yong Zhang Weina Rui Xinzheng Yang School of Transportation Southeast University Southeast university jiulonghu campus Nanjing and 211100 China Transportation Bureau of Huishan District Huishan District Xizhang West Road No.100 Wuxi and 214000 China School of Automobile Chang’an University Middle Section of South 2nd Ring Road Xi’an and 710064 China

Bike-sharing system has been launched in many cities, due to the essential merits. Along with the convenience brought by the rapid development of bike-sharing system, several severe problems also arise. The most serious problem is the uneven distribution of bicycles. Thus the VRP model for bike-sharing inventory rebalancing and vehicle routing is formulated. Additionally, an improved particle swarm optimization (PSO) algorithm is designed to solve this problem. Finally, a case study is undertaken to test the validity of the model and the algorithm. Five maintenance trucks are designated to execute all delivery tasks required by 25 spots. Capacities of all of the maintenance trucks are almost fully utilized. It is of considerable significance for bike-sharing enterprises to make optimal bike schedule.

关键词： Bike-sharing system inventory rebalancing vehicle routing problem particle swarm optimization algorithm

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Image reconstruction for an electrical capacitance tomography system based on a least-squares support vector machine and a self-adaptive particle swarm optimization algorithm

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MEASUREMENT SCIENCE AND TECHNOLOGY 2011年第10期22卷

作者： Chen, Xia Hu, Hong-li Liu, Fei Gao, Xiang Xiang Xi An Jiao Tong Univ State Key Lab Power Equipment & Elect Insulat Xian 710049 Peoples R China

The task of image reconstruction for an electrical capacitance tomography (ECT) system is to determine the permittivity distribution and hence the phase distribution in a pipeline by measuring the electrical capacitances between sets of electrodes placed around its periphery. In view of the nonlinear relationship between the permittivity distribution and capacitances and the limited number of independent capacitance measurements, image reconstruction for ECT is a nonlinear and ill-posed inverse problem. To solve this problem, a new image reconstruction method for ECT based on a least-squares support vector machine (LS-SVM) combined with a self-adaptive particle swarm optimization (PSO) algorithm is presented. Regarded as a special small sample theory, the SVM avoids the issues appearing in artificial neural network methods such as difficult determination of a network structure, over-learning and under-learning. However, the SVM performs differently with different parameters. As a relatively new population-based evolutionary optimization technique, PSO is adopted to realize parameters' effective selection with the advantages of global optimization and rapid convergence. This paper builds up a 12-electrode ECT system and a pneumatic conveying platform to verify this image reconstruction algorithm. Experimental results indicate that the algorithm has good generalization ability and high-image reconstruction quality.

关键词： ECT image reconstruction support vector machine particle swarm optimization algorithm

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Research on Multi-Objective Multidisciplinary Design optimization Based on particle swarm optimization 2

Research on Multi-Objective Multidisciplinary Design Optimiz...

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2nd International Conference on Reliability Systems Engineering (ICRSE)

作者： Wang, Yangyang Han, Minghong Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China

ISBN: (纸本)9781538609187

Complex systems consist of many disciplines or components, which are often difficult to the design optimize as a overall. They need to be broken down into different components, and then coordinate the links between different parts. A TC (Analysis Target Cascade) - one of the multidisciplinary design optimization methods, is an effective way to solve such intricate problems. In the traditional multidisciplinary design optimization methods, there is only one objective function. But the multi-objective optimization problems are often emerged in practical engineering problems. So, we will focus on the multi-objective optimization problems in multidisciplinary design optimization, and solve them with particle swarm optimization. The original problem is firstly decomposed into multiple coupled sub-problems and then coordinate the relation between each sub-problems by ATC method. The system-level sub-problem is a multi-objective optimization problem and the other subsystems are the general single-objective optimization problems, the MOPSO method and the sequence quadratic programming (SQP) method will be used to solve them respectively. The final optimization result is consistent with the optimization result before the original problem is decomposed. Finally, we used two examples to demonstrate the feasibility of particle swarm optimization (PSO) method to get the solution of the multiobjective problems with ATC method.

关键词： multidisciplinary design optimization analysis target cascade multi - objective optimization particle swarm optimization algorithm

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