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Nodes organization for channel assignment with topology preservation in multi-radio wireless mesh networks

AD HOC NETWORKS 2012年第5期10卷 760-773页

作者： Cheng, Hongju Xiong, Naixue Vasilakos, Athanasios V. Yang, Laurence Tianruo Chen, Guolong Zhuang, Xiaofang Fuzhou Univ Coll Math & Comp Sci Fuzhou 350002 Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Univ Western Macedonia Dept Comp & Telecommun Engn Thessaloniki Greece St Francis Xavier Univ Dept Comp Sci Antigonish NS B2G 1C0 Canada

The wireless mesh network is a new emerging broadband technology providing the last-mile Internet access for mobile users by exploiting the advantage of multiple radios and multiple channels. The throughput improvement of the network relies heavily on the utilizing the orthogonal channels. However, an improper channel assignment scheme may lead to network partition or links failure. In this paper we consider the assignment strategy with topology preservation by organizing the mesh nodes with available channels, and aim at minimizing the co-channel interference in the network. The channel assignment with the topology preservation is proved to be NP-hard and to find the optimized solution in polynomial time is impossible. We have formulated a channel assignment algorithm named as DPSO-CA which is based on the discrete particle swarm optimization and can be used to find the approximate optimized solution. We have shown that our algorithm can be easily extended to the case with uneven traffic load in the network. The impact of radio utilization during the channel assignment process is discussed too. Extensive simulation results have demonstrated that our algorithm has good performance in both dense and sparse networks compared with related works. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Wireless mesh networks Multiple radios Multiple channels Channel assignment particle swarm optimization algorithm

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A multi-criteria decision support for optimal instrumentation in scoliosis spine surgery

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2012年第6期45卷 917-929页

作者： de Oliveira, Marcelo Elias Hasler, Carol-Claudius Zheng, Guoyan Studer, Daniel Schneider, Jacques Buechler, Philippe Univ Bern Inst Surg Technol & Biomech CH-3014 Bern Switzerland UKBB Univ Childrens Hosp Beider Basel CH-4056 Basel Switzerland

In adolescent idiopathic scoliosis, the selection of an optimal instrumentation configuration for correcting a specific spinal deformity is a challenging combinatorial problem. Current methods mostly rely on surgeons' expertise, which has been shown to lead to different treatment strategies for the same patients. In this work, a mathematical model of the human spine derived from in-vitro experimentally-obtained data was used to simulate the biomechanical behavior of the spine under the application of corrective forces and torques. The corrective forces and torques were optimized based on the particle swarm optimization algorithm for each combinatorially possible instrumentation strategy. Finally, a multi-criteria decision support for optimal instrumentation in scoliosis spine surgery has been proposed and applied to five patient data sets exhibiting similar spinal deformities according to two commonly used classification systems. Results indicated that the classification of the spinal deformities based on the current standardized clinical classifications systems is not a sufficient condition for recommending selective fusion of spinal motion segments. In addition, the particle swarm optimization algorithm was successfully applied to solve a realistic interdisciplinary clinical problem in a patient-specific fashion. The proposed method enables a better understanding of the biomechanical behavior of spinal structures and has the potential to become a standard tool in preoperative planning.

关键词： Adolescent idiopathic scoliosis Optimal instrumentation configuration Interdisciplinary clinical problem particle swarm optimization algorithm

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A simulation-optimization approach for energy efficiency of chilled water system

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ENERGY AND BUILDINGS 2012年第Nov.期54卷 290-296页

作者： Lee, Kuei-Peng Cheng, Te-Ang Natl Taipei Univ Technol Dept Energy & Refrigerating Airconditioning Engn Taipei Taiwan Natl Taipei Univ Technol Grad Inst Mech & Elect Engn Taipei Taiwan

This study combines an energy simulation program with a hybrid optimization algorithm to identify the optimal settings and minimize the energy consumption of chilled water system. This study employed EnergyPlus, a flexible and highly accurate energy simulation program, to construct the model. To determine the optimal temperature settings for chilled and cooled water for chilled water system, this model used a hybrid optimization algorithm that combines the particle swarm optimization algorithm and the Hooke-Jeeves algorithm. We selected four days in summer and four days in winter to conduct the optimization. The results indicated that the optimized settings reduced the total energy consumed by the chilled water system by 9.4% in summer and 11.1% in winter compared to conventional settings. (C) 2012 Elsevier B.V. All rights reserved.

关键词： EnergyPlus particle swarm optimization algorithm Hooke-Jeeves algorithm Chilled water system optimization Simulation Energy saving

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CALYPSO: A method for crystal structure prediction

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COMPUTER PHYSICS COMMUNICATIONS 2012年第10期183卷 2063-2070页

作者： Wang, Yanchao Lv, Jian Zhu, Li Ma, Yanming Jilin Univ State Key Lab Superhard Mat Changchun 130012 Peoples R China

We have developed a software package CALYPSO (Crystal structure AnaLYsis by particle swarm optimization) to predict the energetically stable/metastable crystal structures of materials at given chemical compositions and external conditions (e.g., pressure). The CALYPSO method is based on several major techniques (e.g. particle-swarm optimization algorithm, symmetry constraints on structural generation, bond characterization matrix on elimination of similar structures, partial random structures per generation on enhancing structural diversity, and penalty function, etc.) for global structural minimization from scratch. All of these techniques have been demonstrated to be critical to the prediction of global stable structure. We have implemented these techniques into the CALYPSO code. Testing of the code on many known and unknown systems shows high efficiency and the highly successful rate of this CALYPSO method [Y. Wang, J. Lv, L. Zhu, Y. Ma, Phys. Rev. B 82 (2010) 094116] [29]. In this paper, we focus on descriptions of the implementation of CALYPSO code and why it works. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Structure prediction particle swarm optimization algorithm Crystal structure

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Reactive Power optimization for Voltage Stability Limit Improvement Incorporating TCSC Device through DE/PSO under Contingency Condition

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ISTANBUL UNIVERSITY-JOURNAL OF ELECTRICAL AND ELECTRONICS ENGINEERING 2012年第1期12卷 1419-1430页

作者： Sakthivel, S. Mary, D. VRS Coll Engn & Technol Dept Elect & Elect Engn Villupuram TN India Govt Coll Engn Dept Elect & Elect Engn Bargur TN India

Modern power systems are at risks of voltage instability problems due to highly stressed operating conditions caused by increased load demand. This paper proposes a hybrid Differential Evolution (DE)/particle swarm optimization (PSO) algorithm based optimal reactive power flow control task incorporating only one type of FACTS device. DE is efficient in exploration through the search space of the problem but not so good in exploitation while PSO is highly efficient in exploitation of the solutions. The hybrid algorithm combines the features of both DE and PSO and is capable of finding the global best solution without easily trapping in to the local minima. Optimal settings of control variables of generator voltages, transformer tap settings and location and parameter setting of TCSC is considered for optimal solution for reactive power flow control and the resultant reactive power reserves. The effectiveness of the proposed work is tested on IEEE-30 Bus test system.

关键词： FACTS devices TCSC Reactive Power Flow Control Differential Evolution particle swarm optimization algorithm DE/PSO algorithm

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Knowledge Extraction algorithm for Variances Handling of CP Using Integrated Hybrid Genetic Double Multi-group Cooperative PSO and DPSO

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JOURNAL OF MEDICAL SYSTEMS 2012年第2期36卷 979-994页

作者： Du, Gang Jiang, Zhibin Diao, Xiaodi Yao, Yang Shanghai Jiao Tong Univ Dept Ind Engn & Logist Management Sch Mech Engn Shanghai 200240 Peoples R China Shanghai Putuo Dist Cent Hosp Shanghai 200062 Peoples R China Shanghai 6 Peoples Hosp Shanghai 200233 Peoples R China

Although the clinical pathway (CP) predefines predictable standardized care process for a particular diagnosis or procedure, many variances may still unavoidably occur. Some key index parameters have strong relationship with variances handling measures of CP. In real world, these problems are highly nonlinear in nature so that it's hard to develop a comprehensive mathematic model. In this paper, a rule extraction approach based on combing hybrid genetic double multi-group cooperative particle swarm optimization algorithm (PSO) and discrete PSO algorithm (named HGDMCPSO/DPSO) is developed to discovery the previously unknown and potentially complicated nonlinear relationship between key parameters and variances handling measures of CP. Then these extracted rules can provide abnormal variances handling warning for medical professionals. Three numerical experiments on Iris of UCI data sets, Wisconsin breast cancer data sets and CP variances data sets of osteosarcoma preoperative chemotherapy are used to validate the proposed method. When compared with the previous researches, the proposed rule extraction algorithm can obtain the high prediction accuracy, less computing time, more stability and easily comprehended by users, thus it is an effective knowledge extraction tool for CP variances handling.

关键词： Clinical pathway (CP) Rule extraction particle swarm optimization algorithm Data mining Variances handling Osteosarcoma

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A novel fractional-order hyperchaotic system with a quadratic exponential nonlinear term and its synchronization

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ADVANCES IN DIFFERENCE EQUATIONS 2012年第1期2012卷 1-21页

作者： Sahab, Ali Reza Ziabari, Masoud Taleb Modabbernia, Mohammad Reza Islamic Azad Univ Fac Engn Elect Engn Grp Lahijan Branch Lahijan Iran Ahrar Univ Fac Engn Comp Engn Grp Rasht Iran Tech & Vocat Univ Elect Engn Dept Rasht Branch Rasht Iran

The dynamics of fractional-order systems have attracted increasing attention in recent years. In this paper a novel fractional-order hyperchaotic system with a quadratic exponential nonlinear term is proposed and the synchronization of a new fractional-order hyperchaotic system is discussed. The proposed system is also shown to exhibit hyperchaos for orders 0.95. Based on the stability theory of fractional-order systems, the generalized backstepping method (GBM) is implemented to give the approximate solution for the fractional-order error system of the two new fractional-order hyperchaotic systems. This method is called GBM because of its similarity to backstepping method and more applications in systems than it. Generalized backstepping method approach consists of parameters which accept positive values. The system responses differently for each value. It is necessary to select proper parameters to obtain a good response because the improper selection of parameters leads to inappropriate responses or even may lead to instability of the system. Genetic algorithm (GA), cuckoo optimization algorithm (COA), particle swarm optimization algorithm (PSO) and imperialist competitive algorithm (ICA) are used to compute the optimal parameters for the generalized backstepping controller. These algorithms can select appropriate and optimal values for the parameters. These minimize the cost function, so the optimal values for the parameters will be found. The selected cost function is defined to minimize the least square errors. The cost function enforces the system errors to decay to zero rapidly. Numerical simulation results are presented to show the effectiveness of the proposed method.

关键词： novel fractional-order hyperchaotic system generalized backstepping method synchronization genetic algorithm cuckoo optimization algorithm particle swarm optimization algorithm imperialist competitive algorithm cost function

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Vertical particle swarm optimization algorithm and its application in soft-sensor modeling

Vertical particle swarm optimization algorithm and its appli...

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6th International Conference on Machine Learning and Cybernetics

作者： Yang, Wei-Ping Shanghai DianJi Univ Sch Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9781424409723

Vertical particle swarm optimization algorithm (VPSO) is proposed in this paper. The new algorithm assumes that the particles tend to fly towards two directions. One is flying toward the global best particle. The other is flying toward the vertical direction. And there is a random value produced in every iteration step to measure the probability of flying into two directions. Both VPSO and particle swarm optimization algorithm (PSO) are used to train neural network (NN) and applied in soft-sensor of acrylonitrile yield. Finally, simulation results show that the method proposed by this paper is feasible and effective in soft-sensor of acrylonitrile yield.

关键词： particle swarm optimization algorithm optimization acrylonitrile soft-sensing

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Short-term load forecasting using artificial neural network based on particle swarm optimization algorithm

Short-term load forecasting using artificial neural network ...

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20th Annual Canadian Conference on Electrical and Computer Engineering

作者： Bashir, Z. A. El-Hawary, M. E. Dalhousie Univ Dept Elect & Comp Engn Halifax NS B3J 2X4 Canada

ISBN: (纸本)9781424410200

The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. In this work, for determining the competitive learning model, the particle swarm optimization (PSO) technique is used as a training algorithm to adjust the weights of the artificial neural networks (ANNs) model to predict hourly loads. The feature of PSO is to fly potential solutions through hyperspace, accelerating toward better solutions. Thus the training phase should result in obtaining the weights configuration associated with the minimum output error. The historical load and weather information were trained and tested over a period of one season through two years. Generalized error estimation is done by using the reverse part of the data as a "test" set. The results were compared with conventional back-propagation algorithm and yielded encouraging results.

关键词： predicted hourly load neural networks particle swarm optimization algorithm

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Multi-threshold infrared image segmentation based on the modified particle swarm optimization algorithm

Multi-threshold infrared image segmentation based on the mod...

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6th International Conference on Machine Learning and Cybernetics

作者： Liu, Yi-Tong Fu, Ming-Yin Gao, Hong-Bin Beijing Inst Technol Sch Informat Sci & Technol Dept Automat Beijing 100081 Peoples R China

ISBN: (纸本)9781424409723

Threshold extraction is the fundamental step in multi-threshold image segmentation. This paper has introduced particle swarm optimization algorithm (PSO) for threshold extraction. But when dealing with the peaky high dimension function of maximum entropy for multi-threshold image segmentation, the conventional PSO is apt to be trapped in local optima called premature. This can cause image segmentation failure. This paper proposes a modified particle swarm optimization method (MPSO), which improves convergence speed and search capacity and avoid the premature phenomena when used in threshold extraction. Simulation results show that the MPSO has better performance and quicker speed. The experimental results also show that with the modified PSO as a threshold extraction method, the image is segmented fairly well and the segmentation speed improves greatly.

关键词： particle swarm optimization algorithm multi-threshold infrared image segmentation

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