Time to time, many researchers have suggested modifications to the standard particleswarmoptimization to find good solutions faster than the evolutionary algorithms, but they could be possibly stuck in poor region o...
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Time to time, many researchers have suggested modifications to the standard particleswarmoptimization to find good solutions faster than the evolutionary algorithms, but they could be possibly stuck in poor region or diverge to unstable situations. For overcoming such problems, this paper proposes new Fast Convergence particleswarmoptimization (FCPSO) approach based on balancing the diversity of location of individual particle by introducing a new parameter, particle mean dimension (Pmd) of all particles to improve the performance of PSO. The FCPSO method is tested with five benchmark functions by variable dimensions and fixed size population and compared with PSO & Constriction factor approach of PSO (CPSO). Finally, search performances of these methods on the benchmark functions are tested.
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 improvemen...
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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 particleswarmoptimization 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.
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&...
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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.
This study combines an energy simulation program with a hybrid optimizationalgorithm to identify the optimal settings and minimize the energy consumption of chilled water system. This study employed EnergyPlus, a fle...
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This study combines an energy simulation program with a hybrid optimizationalgorithm 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 optimizationalgorithm 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.
We have developed a software package CALYPSO (Crystal structure AnaLYsis by particleswarmoptimization) to predict the energetically stable/metastable crystal structures of materials at given chemical compositions an...
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We have developed a software package CALYPSO (Crystal structure AnaLYsis by particleswarmoptimization) 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-swarmoptimizationalgorithm, 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.
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 relationsh...
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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.
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 ...
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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 optimizationalgorithm (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.
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 othe...
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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.
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 particleswarmoptimization (PSO...
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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 particleswarmoptimization (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.
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 ...
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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 particleswarmoptimization 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.
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