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IEEE Congress on Evolutionary Computation

作者： Yeh, Wei-Chang Wei, Shang-Chia Univ Technol Sydney Adv Analyt Inst Integrat & Collaborat Lab Sydney NSW 2007 Australia Natl Tsing Hua Univ Dept Ind Engn & Engn Management Integrat & Collaborat Lab Hsinchu 300 Taiwan

ISBN: (纸本)9781479904549;9781479904532

The end-of-life (EOL) disassembly sequencing problem (DSP) has already become extremely important. We know that the parts, sub-components or raw materials in discarded EOL products are disassembled for reuse in the spirit of eco-friendliness. We also know that disassembly sequencing with adequate tool assignment would ameliorate the process in recycling, reclamation, or remanufacturing. Thus, this paper has investigated tool assignment in the context of DSPs, and formulates a tool-selected disassembly sequencing problem (TDSP) that considers both the tool assignment and disassembly sequence. This paper aims to minimize the disassembly time in the TDSP. In nature, the proposed TDSP is comparatively practical and reasonable to use in real-life applications, and is an NP-complete combinatorial optimization problem (COP). This paper proposes swarm-based soft computing with self-adaptive parameter control called simplified swarm optimization (SSO) to solve this new COP. Based on the statistical significance testing, the experimental results have shown that the advanced SSO can solve the proposed TDSP efficiently and effectively in comparison with GA and PSO.

关键词： disassembly sequencing problem tool allocation self-adaptive parameter control simplified swarm optimization

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simplified swarm optimization for the Time Dependent Competitive Vehicle Routing Problem with Heterogeneous Fleet

Simplified Swarm Optimization for the Time Dependent Competi...

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IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Huang, Chia-Ling Jiang, Yun-Zhi Tan, Shi-Yi Yeh, Wei-Chang Chung, Vera Yuk Ying Lai, Chyh-Ming Kainan Univ Dept Logist & Shipping Management Taoyuan 33857 Taiwan Guangdong Polytech Normal Univ Sch Math & Syst Sci Guangzhou 510006 Guangdong Peoples R China Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 300 Taiwan Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia Natl Def Univ Management Coll Inst Resources Management & Decis Sci Taipei 112 Taiwan

ISBN: (纸本)9781509060177

Nowadays, due to the development of e-commerce, delivery time has become an important factor influencing quality of distribution service. Especially in the urban distribution environment, many factors affect the distribution time, such as traffic condition on peak hours plays an important role in outcomes of the planned schedule. Hence, more and more research devoted to the "Time-depend vehicle routing problem (TDVRP)". Consider the competitors' factors for obtaining more sales, logistics company should serve customers before other rival distributors. Such problem is called " Time-depend vehicle routing problem in a competitive environment (TDVRPC)" whose main objectives of the problem are to minimize the travel time cost and maximize the sale. Many research papers have presented mathematical models for TDVRPC. The major weakness of the above models is they donot satisfy the "first-in-first-out (FIFO)" property which guarantee that if a vehicle leaves a for at a given time, any other vehicle leaving earlier will arrive earlier. Take into account the possible ways of handling the situation with heterogeneous fleet, so a novel model for the " Time-dependent vehicle routing problem in a competitive environment with heterogeneous fleet (TDVRPCHF)" is presented to satisfy the "FIFO" property. To solve such a problem, a simplified swarm optimization algorithm is proposed and it can also be promoted on the other "time-dependent vehicle routing problem".

关键词： Time-dependent vehicle routing problem Heterogeneous fleet Competitive environment simplified swarm optimization

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simplified swarm optimization for Task Assignment Problem in distributed computing system 13

Simplified Swarm Optimization for Task Assignment Problem in...

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13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Yeh, Wei-Chang Lai, Chyh-Ming Huang, Yen-Cheng Cheng, Tzu-Wei Huang, Hsin-Ping Jiang, Yunzhi Natl Tsing Hua Univ Dept IEEM Hsinchu Taiwan Natl Def Univ Inst Resources Management & Decis Sci Taoyuan Taiwan Taoyuan Gen Hosp Minist Hlth & Welf Taoyuan Taiwan Guangdong Polytech Normal Univ Sch Math & Syst Sci Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538621653

Task assignment is a crucial step in distributed computing system, the target of the task assignment problem in distributed computing system is to assign program tasks to processors so as to minimize overall costs made of execution and communication costs and satisfy with various limited resources constraints simultaneously within a system. The task assignment problem with more than three processors is NP-hard. In this paper, we propose an algorithm based on simplified swarm optimization (SSO) as an alternative method to solve the problem. The results of the proposed method ate demonstrated by comparing with well-known methods. The experimental results show that the proposed SSO produces good quality solutions and consumes moderate time in solving task assignment problem in distributed computing system.

关键词： task assignment problem distributed computing system simplified swarm optimization

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simplified swarm optimization with Modular Search for the General Multi-level Redundancy Allocation Problem in Series-parallel Systems

Simplified Swarm Optimization with Modular Search for the Ge...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Yeh, Wei-Chang Luo, Cyuan-Yu Lai, Chyh-Ming Hsu, Chi-Ting Chung, Yuk Ying Lin, Jsen-Shung Natl Tsing Hua Univ Dept IEEM Hsinchu Taiwan Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia Management Cent Police Univ Dept Informat Taoyuan Taiwan

ISBN: (纸本)9781509006229

In recent decade, reliability has been an important factor which may affect the performance of the system. In order to enhance the system reliability, redundancy allocation problem (RAP) is becoming an increasingly important tool in the stages of planning, designing, and controlling of systems. Moreover, the multi-level redundancy allocation problem (MRAP) and multiple multi-level redundancy allocation problem (MMRAP) are extensions derived from the redundancy allocation problem (RAP) for practical modeling of real-life problems. However, while formulating the model, the two problems mentioned above have some restrictions which may not deal with real world problem and lost its generality. Therefore, this paper formulates a new kind of MRAP called general multi-level redundancy allocation problem (GMRAP) to break the restrictions and generalize previous problems. Furthermore, a novel algorithm called simplified swarm optimization with modular search (SSO-MS) is proposed to solve the GMRAP in this paper. Finally, the results obtained by SSO-MS are compared with those obtained from genetic algorithm and particle swarm optimization algorithm. The comparative results show that the proposed SSO-MS is promising among three algorithms and demonstrate the effectiveness of the proposed model and method.

关键词： Redundancy Allocation problem Multi-Level problem simplified swarm optimization System Reliability

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simplified swarm optimization for Repairable Redundancy Allocation Problem in Multi-state Systems with Bridge Topology

Simplified Swarm Optimization for Repairable Redundancy Allo...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yeh, Wei-Chang Wang, Siang-Tai Lai, Chyh-Ming Huang, Yen-Cheng Chung, Yuk Ying Lin, Jsen-Shung Natl Tsing Hua Univ Dept IEEM Hsinchu Taiwan Univ Sydney Sch Informat Technol Sydney NSW Australia Management Cent Police Univ Dept Informat Taoyuan Taiwan

ISBN: (纸本)9781509006199

In recent decades, the redundancy allocation problem (RAP) is becoming an increasingly important issue in the initial stages of planning, designing, and controlling of systems. However, RAP in multi-state system (MSSs) still has some restrictions that components have only two performances: perfect functionality and complete failure. Therefore, this paper formulates a new kind of RAP called repairable redundancy allocation problem (RRAP) in multi-state systems (RRAP in MSSs) to break the restrictions. RRAP in MSSs is nearer to reality and widespread in many critical systems. RRAP in MSSs is not only an NP-hard problem, but also a nonlinear integer optimization problem. In order to deal with RRAP in MSSs in a reasonable time, this paper employs an optimization method of soft computing called simplified swarm optimization (SSO). Finally, the results obtained by SSO have been compared with that obtained from genetic algorithm (GA). Computational results show that the SSO is very competitive and effective in this problem.

关键词： redundancy allocation problem multi-state systems repairable components universal generating function simplified swarm optimization

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Gene selection using information gain and improved simplified swarm optimization

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NEUROCOMPUTING 2016年 218卷 331-338页

作者： Lai, Chyh-Ming Yeh, Wei-Chang Chang, Chung-Yi Natl Def Univ Coll Management Inst Resources Management & Decis Sci Taipei 112 Taiwan Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 300 Taiwan

Recently, gene selection (also called feature selection in data mining) has played an important role in the development of efficient cancer diagnoses and classification because gene expression data are coded by huge measured variables (genes), and only a small number of them present distinct profiles for different classes of samples. Gene selection problem involves reducing irrelevant, redundant and noisy genes and identifying the most distinguished genes to improve the classification accuracy. In this paper, a hybrid filter/wrapper method, known as IG-ISSO is proposed for gene selection problem. In this method, information gain (IG) as a filter is applied to select the most informative genes and an improved simplified swarm optimization (ISSO) is proposed as a gene search engine to guide the search for an optimal gene subset. The support vector machine (SVM) with a linear kernel serves as a classifier of the IG-ISSO. To evaluate the performance of the proposed method empirically, experiments are examined using ten gene expression datasets, and the corresponding results are compared with up-to-date works. The results of the statistical analysis indicate that the proposed method is better than its competitors. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Gene selection Feature selection simplified swarm optimization Information gain

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Integrating simplified swarm optimization with AHP for solving capacitated military logistic depot location problem

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APPLIED SOFT COMPUTING 2019年 78卷 1-12页

作者： Lai, Chyh-Ming Natl Def Univ Management Coll Inst Resources Management & Decis Sci Taipei 112 Taiwan

This work addresses a two-level facility location problem in a military logistic system, named the military logistic depot location problem (MLDLP). Unlike most previous research on facility location problems, the objective of MLDLP is to maximize the average utility of requisitioned buildings, and the utility of a selected building depends on its attributes. This work develops an integer programming model and provides a two-stage method for this problem. In the first stage, the analytic hierarchy process (AHP) is applied to estimate the relative weights of the attributes as the coefficients of the objective function. In the second stage, simplified swarm optimization (SSO) is combined with a novel local search scheme based on AHP, called SSO AHP, which is proposed to deal with the problem. SSOAHP is empirically verified on thirty-six randomly generated problems, and the corresponding results are reported comparing the results with those obtained by up-to-date evolutionary computation methods. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Facility location problem Military logistic system Analytic hierarchy process simplified swarm optimization

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A NEW simplified swarm optimization (SSO) USING EXCHANGE LOCAL SEARCH SCHEME

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2012年第6期8卷 4391-4406页

作者： Bae, Changseok Yeh, Wei-Chang Wahid, Noorhaniza Chung, Yuk Ying Liu, Yao Elect & Telecommun Res Inst Personal Comp Res Team Taejon 305700 South Korea Univ Technol Sydney Fac Engn & Informat Technol Adv Analyt Inst Sydney NSW 2007 Australia Univ Tun Hussein Onn Malaysia Fac Sci Comp & Informat Technol Batu Pahat Johor Malaysia Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia

swarm-based optimization algorithms have demonstrated to have effective ability to solve the classification problem in multiclass databases. However, these algorithms tend to suffer from premature convergence in the high dimensional problem space. This paper proposes a novel simplified swarm optimization (SSO) algorithm to overcome the above convergence problem by incorporating it with the new local search strategy. The proposed algorithm can find a better solution from the neighbourhood of the current solution produced by SSO. The performance of the proposed algorithm has been evaluated by using 13 different widely used databases and compared with the standard PSO and three other well-known classification algorithms. In, addition, the practicability of the approach is studied by applying it in analysing golf swing from weight shift data. Empirical results illustrate that the proposed algorithm can achieve the highest classification accuracy.

关键词： Particle swarm optimization Discrete particle swarm optimization simplified swarm optimization Local search Data classification Data mining

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Entropic simplified swarm optimization for the task assignment problem

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APPLIED SOFT COMPUTING 2017年 58卷 115-127页

作者： Lai, Chyh-Ming Yeh, Wei-Chang Huang, Yen-Cheng Natl Def Univ Inst Resources Management & Decis Sci Management Coll Taipei 112 Taiwan Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 300 Taiwan

The task assignment problem (TAP) aims to assign application tasks to a number of distributed processors in a computation system in order to increase the efficiency and effectiveness of the system for minimizing or maximizing a certain cost. The problem is NP-hard;thus, finding the exact solutions is computationally intractable for larger size problems. In this paper, a novel entropic simplified swarm optimization, known as ESSO, is proposed for solving this problem. In this method, an entropic local search (ELS) inspired by information theory is proposed to enhance the exploitation capability of SSO. Entropy is adopted to describe the uncertainty level of assigned tasks;the task with higher uncertainty then has more chance to be reassigned. Furthermore, for each reassigned task, the corresponding list of potential processors can be constructed using information theory;this enhances the probability of finding promising solutions in ELS. To empirically evaluate the performance of the proposed method, experiments are conducted using twenty-four randomly generated problems ranging from small to large scale, and the corresponding results are compared with existing works. The experiment results indicate that ESSO is better than its competitors in both solution quality and efficiency. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Task assignment problem simplified swarm optimization Information theory

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Forecasting wind power in the Mai Liao Wind Farm based on the multi-layer perceptron artificial neural network model with improved simplified swarm optimization

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 55卷 741-748页

作者： Yeh, Wei-Chang Yeh, Yuan-Ming Chang, Po-Chun Ke, Yun-Chin Chung, Vera Univ Technol Sydney Integrat & Collaborat Lab Adv Analyt Inst Broadway NSW 2007 Australia Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 300 Taiwan Univ Sydney Fac Sci Sydney NSW 2006 Australia Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia

Of the many kinds of renewable energy, wind power is low in cost and non-polluting, so it is especially well-suited to Taiwan. The Mai Liao Wind Farm is the most important wind farm in Taiwan, and forecasting the wind power output for national sustainable development continues to be a challenging research feature. In this study, we attempt to forecast the wind power data collected from the Mai Liao Wind Farm. Our forecast model is based on a Multi-Layer Perceptron Artificial Neural Network (MLP) model using the data collected at the Mai Liao Wind Farm over a period of five years from September 2002 to August 2007. We proposed a new algorithm, namely improved simplified swarm optimization (iSSO), which improves simplified swarm optimization (SSO) by justifying the weights and bias in training the MLP. The proposed iSSO combines Principal Component Analysis (PCA), Autocorrelation Function (AF) and Partial Autocorrelation Function (PAF) for the selection of features which increases the efficiency of the proposed model. The experimental results demonstrate that the performance of iSSO outperforms the other six most popular algorithms. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Artificial intelligence simplified swarm optimization Machine learning Neural network Wind power Forecast

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