Cloud computing provides the computational machines as a support of the clients utilizing cloud organize. In cloud computing, the user inputs are executed with required machines to convey the administrations. Numerous...
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ISBN:
(纸本)9781538677995
Cloud computing provides the computational machines as a support of the clients utilizing cloud organize. In cloud computing, the user inputs are executed with required machines to convey the administrations. Numerous task scheduling methods are utilized to plan the client tasks to the machines. In this paper, another successful hybrid task scheduling is proposed to minimize the total execution time using Genetic algorithm (GA) and particleswarmoptimization (PSO) algorithms. In hybrid Genetic algorithm - particleswarmoptimization (GA-PSO) algorithm, PSO helped GA to obtain better results compare to a standard genetic algorithm, Min-Min, and Max-Min algorithms 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.
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.
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.
Population growth increased the need for energy, which led us to use new and clean energy sources. The microgrid is considered one of the best solutions for energy supply, which is one of the main challenges. Here, vo...
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