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25th International Telecommunication Networks and Applications Conference (ITNAC)

作者： Huang, Chia-Ling Yeh, Wei-Chang Kainan Univ Dept Logist & Shipping Management Taoyuan 33857 Taiwan Natl Tsing Hua Univ Dept Ind Engn & Engn Managemen Hsinchu 300 Taiwan

ISBN: (纸本)9781467393485

An effective and simple solution methodology is applied and demonstrated to optimize the reliability redundancy allocation problems (RRAP) for the series-parallel system, the complex (bridge) system, and the overs peed protection of gas turbine system. The objective of the RRAP is the best known to maximize the system reliability for numerous decades. For this paper, in order to maximize the system reliability, it has to decide simultaneously the number of redundant components and the reliability of corresponding components in each subsystem with nonlinear constraints. This work is one difficulty for the RRAP. Hence, the RRAP is the mixed-integer programming problem with the nonlinear constraints that belongs to the NP-hard problem. In this paper, the simplified swarm optimization (SSO) algorithm is proposed to solve the RRAP and improve computation efficiency for these NP-hard problems. The proposed SSO belongs to the category of swarm Intelligence methods and is also an evolutionary computation method. Total three RRAP problems are successfully demonstrated by the proposed SSO algorithm. There are the comparisons of the experiment results among the proposed SSO algorithm with other available algorithms in this literature. On the average performance in the reliability of the three systems, the proposed SSO algorithm outperforms the previously best-known solutions.

关键词： Reliability Redundancy allocation problem simplified swarm optimization algorithm

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Parameters extraction of solar cell models using a modified simplified swarm optimization algorithm

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SOLAR ENERGY 2017年第Mar.期144卷 594-603页

作者： Lin, Peijie Cheng, Shuying Yeh, Weichang Chen, Zhicong Wu, Lijun Fuzhou Univ Coll Phys & Informat Engn Fuzhou Peoples R China Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu Taiwan Changzhou Univ Jiangsu Collaborat Innovat Ctr Photovolta Sci & E Changzhou Peoples R China

The parameters of solar cells models have an effect on the simulation of solar cells and can be applied to monitor the working condition and diagnose potential faults for photovoltaic (PV) modules in a PV system. To accurately and efficiently extract the optimal parameters of solar cells in a limited CPU run time, a modified simplified swarm optimization (MSSO) algorithm is presented for the single diode and double diode models by minimizing the least square error between the calculated and experimental data. In MSSO, a new one-variable-update mechanism and survival-of-the-fittest policy are applied to enhance the ability of traditional SSO. To investigate the performance of MSSO, comparative studies with other well-known optimization algorithms, i.e., SSO, artificial bee colony (ABC) and simplified bird mating optimizer (SBMO), are presented, and extensive computational results are shown. The statistical data indicate that the MSSO method has the best performance among these methods in terms of efficiency, robustness and accuracy. Moreover, the current vs. voltage characteristics of the parameters extracted by MSSO coincide well with those of experimental data. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： simplified swarm optimization algorithm Solar cell models Parameter extraction I-V characteristic

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simplified swarm optimization algorithm for Reliability Redundancy Allocation Problems

Simplified Swarm Optimization Algorithm for Reliability Redu...

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International Telecommunication Networks and Applications Conference

作者： Chia-Ling Huang Wei-Chang Yeh Department of Logistics and Shipping Management Kainan Universit Department of Industrial Engineering and Engineering Managemen National Tsing Hua University

ISBN: (纸本)9781467393492

An effective and simple solution methodology is applied and demonstrated to optimize the reliability redundancy allocation problems (RRAP) for the series-parallel system, the complex (bridge) system, and the overspeed protection of gas turbine system. The objective of the RRAP is the best known to maximize the system reliability for numerous decades. For this paper, in order to maximize the system reliability, it has to decide simultaneously the number of redundant components and the reliability of corresponding components in each subsystem with nonlinear constraints. This work is one difficulty for the RRAP. Hence, the RRAP is the mixed-integer programming problem with the nonlinear constraints that belongs to the NP-hard problem. In this paper, the simplified swarm optimization (SSO) algorithm is proposed to solve the RRAP and improve computation efficiency for these NP-hard problems. The proposed SSO belongs to the category of swarm Intelligence methods and is also an evolutionary computation method. Total three RRAP problems are successfully demonstrated by the proposed SSO algorithm. There are the comparisons of the experiment results among the proposed SSO algorithm with other available algorithms in this literature. On the average performance in the reliability of the three systems, the proposed SSO algorithm outperforms the previously best-known solutions.

关键词： Reliability Redundancy allocation problem simplified swarm optimization algorithm

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A simplified swarm optimization-based restricted Boltzmann machine algorithm for a time series clustering

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APPLIED SOFT COMPUTING 2025年 173卷

作者： Zulvia, Ferani E. Kuo, R. J. Inst Teknol Bandung Dept Ind Engn Jalan Ganesha 10 Bandung 40132 Indonesia Natl Taiwan Univ Sci & Technol Dept Ind Management 43Sect 4Kee Lung Rd Taipei 106 Taiwan

Time series clustering is an important area of research, driven by the prevalence of time series data in domains such as power and maintenance data. However, most existing clustering algorithms are not specialized for time series data. Therefore, this study introduces a metaheuristics-based approach, utilizing a restricted Boltzmann machine (RBM) for feature extraction in time series clustering. The RBM, a neural network algorithm with two layers and undirected weights, is kept simple to avoid complex parameter settings. The study employs the RBM as an encoder, coupled with a K-step contrastive divergence and an improved, simplified swarm optimization (iSSO) algorithm for training. The proposed hybrid of iSSO-based RBM and K-means algorithm is compared with RBM and particle swarm optimization-based RBM across the tested time series datasets. Results indicate that the proposed algorithm yields superior performance, reconstructing time series data with minimal error and achieving the highest clustering accuracy compared to other baseline algorithms.

关键词： Feature-based time series clustering Restricted Boltzmann machine simplified swarm optimization algorithm Particle swarm optimization algorithm

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Solving reliability redundancy allocation problems with orthogonal simplified swarm optimization

Solving reliability redundancy allocation problems with orth...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yeh, Wei-Chang Jiang, Yun-Zhi Chung, Vera Yuk Ying He, Xiangjian Natl Tsing Hua Univ Dept Ind Engn & Engn Managemen Hsinchu 300 Taiwan Jiangxi Agr Univ Sch Software Nanchang 330045 Peoples R China S China Univ Technol Sch Software Engn Guangzhou 510006 Guangdong Peoples R China Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia Univ Technol Sydney Comp Vis & Recognit Lab Res Ctr Innovat IT Serv & Applicat iNEXT Broadway NSW 2007 Australia

ISBN: (纸本)9781479919598

This study applies a penalty guided strategy and the orthogonal array test (OA) based on the simplified swarm optimization algorithm (SSO) to solve the reliability redundancy allocation problems (RRAP) in the series system, the series-parallel system, the complex (bridge) system, and the overspeed protection of gas turbine system. For several decades, the RRAP has been one of the most well known techniques. The maximization of system reliability, the number of redundant components, and the reliability of corresponding components in each subsystem have to be decided simultaneously with nonlinear constraints, acting as one difficulty for the use of the RRAP. In other words, the objective function of the RRAP is the mixed-integer programming problem with the nonlinear constraints. The RRAP is of the class of NP-hard. Hence, in this paper, the SSO algorithm is proposed to solve the RRAP and improve computation efficiency for these NP-hard problems. There are four RRAP problems used to illustrate the applicability and the effectiveness of the SSO. The experimental results are compared with previously developed algorithms in literature. Moreover, the maximum-possible-improvement (MPI) is used to measure the amount of improvement of the solution found by the SSO to the previous solutions. According to the results, the system reliabilities obtained by the proposed SSO for the four RRAP problems are as well as or better than the previously best-known solutions.

关键词： Reliability Redundancy allocation problem RRAP simplified swarm optimization algorithm SSO Mixed-integer nonlinear programming

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Solving reliability redundancy allocation problems with orthogonal simplified swarm optimization

Solving reliability redundancy allocation problems with orth...

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International Joint Conference on Neural Networks

作者： Wei-Chang Yeh Vera Yuk Ying Chung Yun-Zhi Jiang Xiangjian He Department of Industrial Engineering and Engineering Managemen National Tsing Hua University Hsinchu 300 Taiwan School of Information Technology University of Sydney NSW 2006 Australia School of Software Jiangxi Agricultural University Nanchang China 330045 School of Software Engineering South China University of Technology Guangzhou China 510006 Computer Vision and Recognition Laboratory Research Centre for Innovation in IT Services and Applications (iNEXT) University of Technology Sydney (UTS) PO Box 123 Broadway NSW 2007 Australia

ISBN: (纸本)9781479919611

关键词： Reliability Redundancy allocation problem RRAP simplified swarm optimization algorithm SSO Mixed-integer nonlinear programming

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