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第26届中国控制与决策会议

作者： ZHOU Wei BU Yan-ping ZHOU Ye-qing School of Business East China University of Science and Technology School of Technology Shanghai Jiaotong University School of Mathematical sciences Fudan University

The flexible job shop scheduling problem(FJSP) is an extension of the classic job shop scheduling problem(JSP),which breaks through the uniqueness of limit resources,allows a procedure in many machines processing and one machine processing many kinds of different types of *** is more practical and complex than *** computational complexity of FJSP is much higher,which disables exact solution methods and makes heuristic approaches more qualified.A hybrid optimization algorithm,CPSO,based on the cultural algorithm and particle swarm optimization algorithm,is proposed in this paper to solve the *** objective is to minimize *** results show that this hybrid method is able to solve efficiently these kinds of problems.

关键词： cultural algorithm particle swarm optimization algorithm flexible job shop scheduling problem makespan

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Cultural algorithm Based on particle swarm optimization for Partner Selection of Virtual Enterprise

Cultural Algorithm Based on Particle Swarm Optimization for ...

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第三十一届中国控制会议

作者： ZHOU Wei~1,BU Yan-ping~2 1.School of Business,East China University of Science and Technology,Shanghai,200237,P.R.China 2.School of Technology,Shanghai Jiaotong University,Shanghai,201101,P.R.China

A novel cultural algorithm based on particle swarm optimization(PSO) algorithm was proposed in this *** analyzing the partner selection problems of virtual enterprise,the CPSO algorithm was presented to solve enterprise alliance problem within reasonable time and *** are certain number partners of each sub-task in virtual enterprise environment. The objective is,by selecting the optimal combination of partners,to minimize project's completion time and project's total cost. We tested the CPSO algorithm against the PSO *** results demonstrate that it can be superior to the regular PSO. We also tested the CPSO algorithm with the exhaustion method to show the algorithm's efficiency.

关键词： cultural algorithm particle swarm optimization algorithm virtual enterprise partner selection

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Ground-state structure determination and mechanical properties of palladium seminitride

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Chinese Physics B 2013年第11期22卷 431-435页

作者：张刚台白婷婷赵亚儒卢成 Department of Physics and Information Technology Baoji University of Arts and Sciences Department of Mathematics Baoji University of Arts and Sciences Department of Physics Nanyang Normal University

Combining first-principles calculations with the particle swarm optimization （PSO） algorithm, we have explored the ground-state structure of Pd2N, whose structure is in debate although it is the first synthesized binary platinum group nitride. The ground-state structure is predicted to be tetragonal with space group P^-4m2, which is energetically more favorable than the previously proposed orthorhombic Co2N-type structure. The stability is confirmed by the subsequent calculations on the phonon dispersion curves and elastic constants. Furthermore, the calculated mechanical properties indicate that Pd2N has low incompressibility and is a common hard material.

关键词： Pd2N particle swarm optimization algorithm ground-state structure first-principles calculations

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System reliability theory based multiple-objective optimization model for construction projects

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AUTOMATION IN CONSTRUCTION 2013年 31卷 54-64页

作者： Tao, Ran Tam, Chi-Ming City Univ Hong Kong Dept Civil & Architectural Engn Hong Kong Hong Kong Peoples R China

Although the system reliability theory has a high capability in quality quantification, while system reliability optimization (SRO) has been well developed in manufacturing engineering, seldom can their applications be found in the construction industry. This study aims to develop a system reliability theory based multiple-objective optimization model to conduct SRO, and then identify the cost-quality trade-off solution for construction projects. First the whole construction project is treated as a system composed of different work packages. Second, the reliability function is employed to quantify the quality performance and the nonlinear cost-reliability function is set up. Moreover, according to the physical arrangements among each work package, the system reliability structural function is determined. Third, the total construction cost minimization and system reliability maximization are defined as multiple-objectives. The particle swarm optimization algorithm is employed to search for the Pareto-optimal solutions, from which the final cost-quality trade-off solution can be selected. A real construction case is used to evaluate the workability of the proposed model and the results have fully proven its validity and practicality. (C) 2012 Elsevier B.V. All rights reserved.

关键词： System reliability theory System reliability optimization Multiple objectives optimization model particle swarm optimization algorithm

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Based on region division setup planning for sheet metal assembly in aviation industry

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2013年第B1期227卷 153-161页

作者： Liu, Ping Li, Yuan Zhang, Kai-fu Cheng, Hui Northwestern Polytech Univ Minist Educ Key Lab Contemporary Design & Integrated Mfg Tech Xian 710072 Peoples R China

The task of setup planning is to determine the number and sequence of setups and the machining features or operations in each setup. Due to the locating method's character, the position of the locators may keep away joining tools from arriving at joining points during the joining process of sheet metal. In order to solve these problems, a novel method is proposed, which consists of three parts: (1) the points on the skin are divided into sets of supplied riveting and prior riveting points by convex hull generating method;(2) fixture vector is defined, and a suitable fixture region is generated by vector multiplication;and (3) the particle swarm optimization algorithm is applied to generate optimal setup scheme by setting up the parameters. The new setup planning approach can accomplish riveting work by minimum setup times and workpiece variation without finite element analysis software. At last, a certain kind of aircraft wing joining process is illustrated as an example to demonstrate the effect of the proposed approach.

关键词： Setup planning supplied riveting set particle swarm optimization algorithm sheet metal assembly

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STUDY ON CLOUD RESOURCE ALLOCATION STRATEGY BASED ON particle swarm ANT COLONY optimization algorithm

STUDY ON CLOUD RESOURCE ALLOCATION STRATEGY BASED ON PARTICL...

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2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems

作者： Zhengqiu Yang Meiling Liu Jiapeng Xiu Chen Liu School Of Software Engineering Beijing University of Posts and Telecommunications School Of Computer Science Beijing University of Posts and Telecommunications

Cloud computing environment for the efficient resources allocation is an important issue in the field of cloud computing. The resources in Cloud computing application platform are distributed widely and with great diversity. User demands of real-time dynamic change are very difficult to predict accurately. The heuristic ant colony algorithm could be used to solve this kind of problems, but the algorithm has slow convergence speed and parameter selection problems. Aiming at this problem, this paper proposes an optimized ant colony algorithm based on particle swarm to solve cloud computing environment resources allocation problem.

关键词： particle swarm optimization algorithm Ant colony optimization algorithm Cloud resource allocation

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Supply Reliability and Generation Cost Analysis Due to Load Forecast Uncertainty in Unit Commitment Problems

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IEEE TRANSACTIONS ON POWER SYSTEMS 2013年第3期28卷 2242-2252页

作者： Wang, Bo Li, You Watada, Junzo Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan

The goal of a unit commitment optimization problem is to reduce the total generation cost as much as possible while satisfying future power demands. Thus, analysis must be performed based on correct predictions of future demands. However, various uncertain factors affect these loadsmaking an exact forecasting unsuccessful. This study mitigates this difficulty by applying fuzzy set theory to evaluate the future uncertain loads. The objective of this research is to build a two-stage multi-objective fuzzy programming model based on 24-hour uncertain load forecasting. The first stage is a decision-making process on the interval data of the imprecise power loads, whereas the second stage pursues the optimization of the unit commitment scheduling, which can help find both optima simultaneously by maximizing power supply reliability and minimizing total generation cost. To define the supply reliability under uncertain forecasting, we propose a new concept of maximal blackout time during successful operation, which is based on the fuzzy credibility theory. Furthermore, as a solution approach to this model, an improved two-stage multi-objective particle swarm optimization algorithm is designed based on our previous studies. Finally, the performance of this algorithm is discussed in comparison with experimental results from several test systems.

关键词： Fuzzy set theory maximal blackout time during successful operation particle swarm optimization algorithm power loads two-stage multi-objective problem uncertainty

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Research on diversity of particle swarm optimization algorithm based on dynamic weight

Shiyou Huagong Gaodeng Xuexiao Xuebao/Journal of Petrochemic...

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Shiyou Huagong Gaodeng Xuexiao Xuebao/Journal of Petrochemical Universities 2008年第4期21卷 91-94页

作者： Zhu, Pei-Yi Zhang, Yu-Lin Department of Automation Changshu Institute of Technology Changshu 215500 China Department of Electric Information Engineering Huaiyin Institute of Technology Huaian 223003 China

Stocks diversity is the precondition for ensuring the convergence of PSO algorithm. The definition of stocks diversity is clear and the operand is small, moreover which was analyzed by particle evolution degree and aggregation degree. A changed algorithm was proposed based on adjusting weight adaptively. The algorithm ensures population diversity and avoids premature convergence effectively. Simulation results indicate that this algorithm not only speeds up the population the evolution speed, but also strengthens the algorithm the overall situation astringency, and convergence of probability also increases from 15% to 100%.

关键词： Diversity Dynamic weight particle swarm optimization algorithm Premature

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Extraction of Multicrop Planning Rules in a Reservoir System: Application of Evolutionary algorithms

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JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING 2013年第6期139卷 490-498页

作者： Fallah-Mehdipour, E. Bozorg-Haddad, Omid Marino, M. A. Univ Tehran Coll Agr & Nat Resources Fac Agr Engn & Technol Dept Irrigat & Reclamat Engn Tehran *** Iran Univ Calif Davis Dept Civil & Environm Engn Dept Land Air & Water Resources Davis CA 95616 USA Univ Calif Davis Dept Biol & Agr Engn Davis CA 95616 USA

Multicropping is the practice of growing two or more crops in the same space during a single growing season. Planning rules are mathematical equations that use previous experiences of a water resource system to balance the system's water supply and demand, and calculate multicrop areas in various periods. In this paper, linear and nonlinear planning rules are developed for optimal multicrop irrigation areas associated with reservoir operation policies in a reservoir-irrigation system. Reservoir operations are related to water allocations to each irrigated area by considering inflow and storage volume of the reservoir as the water supply in a monthly operation period. Evolutionary algorithms (EAs) can determine optimal multicropping patterns planning rules by considering various mathematical patterns. In this paper, three EAs, namely, (1) genetic algorithm (GA), (2) particle swarm optimization (PSO), and (3) shuffled frog leaping algorithm (SFLA) are employed and compared to maximize the total net benefit of the water resource system by supplying irrigation water for a proposed multicropping pattern over the planning horizon. Results show that the SFLA achieves the best solution, with the maximum value of the objective function in both linear and nonlinear planning rules compared to the GA and PSO. Moreover, the best yield of nonlinear rules is 45.52% better (higher) than that obtained by linear rules. (C) 2013 American Society of Civil Engineers.

关键词： Multicrop planning rule Evolutionary algorithms Genetic algorithm particle swarm optimization algorithm Shuffled frog leaping algorithm

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A New Method For Machinery Fault Diagnoses Based On an Optimal Multiscale Morphological Filter

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STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING 2013年第12期59卷 719-724页

作者： Tan, Wei Chen, Xiaoan Dong, Shaojiang Chongqing Univ State Key Lab Mech Transmiss Chongqing 630044 Peoples R China Chongqing Univ Technol Key Lab Adv Mfg Technol Automobile Parts Chongqing Peoples R China

In order to effectively eliminate the noise and extract the impulse components in the vibration signals, a new method based on an optimal multiscale morphological filter is proposed. In this method, firstly, the average of the closing and opening operator is used to construct the morphological filter, then the multiscale morphological filters' structure elements (SEs) are optimized and selected using a particle swarm optimization algorithm (PSO). The noise in the original signal is filtered by the multiscale morphological filter. The proposed method was evaluated by simulated signals and bearing fault signals. The results show that the method can effectively filter the noise and extract the impulse characteristics of the vibration signals, which demonstrate the effectiveness of the proposed method.

关键词： multiscale morphological filter structure element particle swarm optimization algorithm noise reduction

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