This study intends to present a dynamic clustering (DC) approach based on particleswarmoptimization (PSO) and immune genetic (IG) (DCPIG) algorithm, which is able to cluster the data into adequate clusters through d...
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This study intends to present a dynamic clustering (DC) approach based on particleswarmoptimization (PSO) and immune genetic (IG) (DCPIG) algorithm, which is able to cluster the data into adequate clusters through data characteristics with pre-specified numbers of clusters. The proposed DCPIG algorithm is compared with three DC algorithms in the literature using Iris, Wine, Glass and Vowel benchmark data sets. The experiment results show that the DCPIG algorithm can achieve higher stability and accuracy than the other algorithms. In addition, the DCPIG algorithm is also applied to a real-world problem considering the customer clustering for a cyber flower shop. Lastly, we recommend different products and services to customers based on the clustering results.
Based on particle swarm optimization algorithm, this paper presents a grid scheduling optimizationalgorithm combing the advantages of Ant Colony optimizationalgorithm. The algorithm processes task scheduling through...
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ISBN:
(纸本)9780878492695
Based on particle swarm optimization algorithm, this paper presents a grid scheduling optimizationalgorithm combing the advantages of Ant Colony optimizationalgorithm. The algorithm processes task scheduling through particle swarm optimization algorithm to get a group of relatively optimal solutions, and then conducts small-area local search with Ant Colony optimizationalgorithm. Theoretical analysis and results of the simulation experiments show that this scheduling algorithm effectively achieves load balancing of resources with comprehensive advantages in time efficiency and solution accuracy compared to the traditional Ant Colony optimizationalgorithm and particleswarmoptimizationalgorithm, and can be applied to task scheduling in grid computing.
particle swarm optimization algorithm has the defects of easy to fall into local optimum and low convergence accuracy used in reactive power optimization. To solve the problems, this paper proposed an improved particl...
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ISBN:
(纸本)9783038351153
particle swarm optimization algorithm has the defects of easy to fall into local optimum and low convergence accuracy used in reactive power optimization. To solve the problems, this paper proposed an improved particle swarm optimization algorithm based on dynamic learning factors. The two accelerations are changed with searching stage, so as to enhance the early globle search ability and the late local search ability, then to avoid local optimum;minimum particle angle method and crowded distance method are uesd to determine the global extremum in instalments, so as to improve the convergence speed and accuracy of multi-objective pareto solutions. Take the IEEE 30 bus system IEEE 118 bus system as example, the proposed method is compared with adaptive chaos particleswarmoptimization (ACPSO) and NSGA-II, simulation results show that the method put forward in this paper has better convergence accuracy.
The present paper considers convergence characteristics of the particleswarmalgorithm and its modification - the hybrid PSO-GS algorithm obtained under combination of the PSO algorithm and Grid Search algorithm. Com...
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ISBN:
(纸本)9781509022212
The present paper considers convergence characteristics of the particleswarmalgorithm and its modification - the hybrid PSO-GS algorithm obtained under combination of the PSO algorithm and Grid Search algorithm. Comparison of quality indices of the particleswarmalgorithm and the steepest descent algorithm has been carried out for evaluation of advantages of the PSO algorithm in comparison with classical optimizationalgorithms.
In order to solve the problem of insufficient adaptive ability of the network intrusion detection model, the large-scale fast search capability of the particleswarmoptimization (PSO) algorithm is introduced into the...
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ISBN:
(纸本)9798331532109;9798331532093
In order to solve the problem of insufficient adaptive ability of the network intrusion detection model, the large-scale fast search capability of the particleswarmoptimization (PSO) algorithm is introduced into the intrusion detection model. In order to solve the problem that PSO is easy to fall into local optimality, the genetic algorithm (GA) is introduced. An improved particleswarmoptimization (GAPSO) algorithm based on genetic algorithm is proposed. This algorithm optimizes the parameters that are difficult to adjust in the lightweight gradient boosting machine (LightGBM) algorithm, so that the PSO algorithm can quickly converge while ensuring the optimization accuracy, and obtain the optimal network intrusion detection model. Experimental results show that GAPSO is more effective than the basic PSO algorithm when dealing with high-dimensional, complex structure optimization problems.
A novel multi-agent particle swarm optimization algorithm (MAI'SO) is proposed for optimal reactive power dispatch and voltage control of power system. The method integrates multi-agent system (MAS) and particle s...
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ISBN:
(纸本)9780878492138
A novel multi-agent particle swarm optimization algorithm (MAI'SO) is proposed for optimal reactive power dispatch and voltage control of power system. The method integrates multi-agent system (MAS) and particle swarm optimization algorithm (PSO). An agent in *** represents a particle to PSO and a candidate solution to the optimization problem. All agents live in a lattice-like environment, with each agent fixed on a lattice-point. In order to decrease fitness value, quickly, agents compete and cooperate with their neighbors. and they can also use knowledge. Making use of these agent interactions and evolution mechanism of ***. MAPSO realizes the purpose of minimizing the value of objective function. MAPSO applied for optimal reactive power is evaluated on an IEEE 30-bus power system. It is shown that the proposed approach converges to better solutions much faster than the earlier reported approaches
In wireless sensor network for nuclear power plant's peripheral environmental radiation monitoring the gamma dose rate data may be missed affected by various factors, which will influence the validity of environme...
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ISBN:
(纸本)9783037855447
In wireless sensor network for nuclear power plant's peripheral environmental radiation monitoring the gamma dose rate data may be missed affected by various factors, which will influence the validity of environmental radiation monitoring. To solve the problem, a missing data imputation algorithm is proposed based on particleswarm optimized least squares support vector machine. This algorithm imputes missing data utilizing node's previous monitoring data and neighbor node's current monitoring data jointly. Experimental results using the real radiation monitoring data around a nuclear power plant show that the proposed algorithm can impute the missing gamma dose rate data accurately.
Small world network is a type of mathematical graph, in which most nodes are not neighbors of one another, but can be reached from every other by a small number of hops. P2P network is a typical small world network, i...
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ISBN:
(纸本)9780769548647;9781467330275
Small world network is a type of mathematical graph, in which most nodes are not neighbors of one another, but can be reached from every other by a small number of hops. P2P network is a typical small world network, it has a large scale and high risks, so trust model building and Trust Path Selecting (TPS) become big challenges. To solve TPS problem, we first reduce the scale of the complex P2P network by identifying and deleting the equivalent nodes. Then we provide a Trust Path Selection algorithm based on particleswarmoptimization (PSO). In the algorithm, after initializing the particleswarm, each particle can update the speed and location according to its information, and then produce a new particle with better value. Repeating that process continually to implement the global search of the space, we can get the better trust path in the networks. The experimental results show that this algorithm is effective and efficient in finding the suboptimal solution of trust path, hence it can be applied in trust path searching in such small-world networks as P2P network.
particleswarmoptimization (PSO) algorithm is a well-known optimization approach to deal with discrete problems. There are two models proposed for the operators of PSO algorithm, one is based on value exchange and th...
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particleswarmoptimization (PSO) algorithm is a well-known optimization approach to deal with discrete problems. There are two models proposed for the operators of PSO algorithm, one is based on value exchange and the other on order exchange, accordingly two versions of PSO algorithms are formed. A new version of PSO algorithm based on order exchange has been presented in our studies, which is capable of converging on the global optimization solution, with the method of generating the stop evolution particle over again. In this paper, we propose another version of PSO algorithm based on value exchange with the same method. There exist, thus, totally four versions of PSO algorithms, which is given a brief introduction individually and the performance of which are compared in solving sequence optimization problems through fifty runs. The performance comparison show that the PSO algorithm with global convergence characteristics based on order exchange outperforms the other versions of PSO in solving sequence optimization problem. (C) 2011 Elsevier Ltd. All rights reserved.
We propose a novel iterative thresholding approach based on firefly and particleswarmoptimization to be used for the detection of hemorrhages, one of the signs of diabetic retinopathy disease. This approach consists...
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We propose a novel iterative thresholding approach based on firefly and particleswarmoptimization to be used for the detection of hemorrhages, one of the signs of diabetic retinopathy disease. This approach consists of the enhancement of the image using basic preprocessing methods, the segmentation of vessels with the help of Gabor and Top-hat transformation for the removal of the vessels from the image, the determination of the number of regions with hemorrhages and pixel counts in these regions using firefly algorithm (FFA) and particle swarm optimization algorithm (PSOA)-based iterative thresholding, and the detection of hemorrhages with the help of a support vector machine (SVM) and linear regression (LR)-based classifier. In the preprocessing step, color space selection, brightness and contrast adjustment, and adaptive histogram equalization are applied to enhance retinal images, respectively. In the step of segmentation, blood vessels are detected by using Gabor and Top-hat transformations and are removed from the image to avoid confusion with hemorrhagic regions in the retinal image. In the iterative thresholding step, the number of hemorrhagic regions and pixel counts in these regions are determined by using an iterative thresholding approach that generates different thresholding values with the FFA/PSOA. In the classification step, the hemorrhagic regions and pixel counts obtained by the iterative thresholding are used as inputs in the LR/SVM-based classifier. PSOA-based iterative thresholding and the SVM classifier achieved 96.7% sensitivity, 91.4% specificity, and 94.1% accuracy for hemorrhage detection. Finally, the experiments show that the correct classification rates and time performances of the PSOA-based iterative thresholding algorithm are better than those of the FFA in hemorrhage detection. In addition, the proposed approach can be used as a diagnostic decision support system for detecting hemorrhages with high success rate.
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