The particleswarmoptimization (PSO) algorithm is vulnerable to reach local optimal value. So, this paper presents an adaptive hybrid particles swarmoptimization. During the solving process, both crossover operator ...
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ISBN:
(纸本)9780769538655
The particleswarmoptimization (PSO) algorithm is vulnerable to reach local optimal value. So, this paper presents an adaptive hybrid particles swarmoptimization. During the solving process, both crossover operator in genetic algorithm and hyper-mutation are introduced. Referring to the selection mechanism of immune algorithm based on information entropy, the adaptive selections mechanism is proposed. Experiments show that the algorithm effectively improves global search capability.
On the basis of analyzing the particleswarmoptimization (PSO) algorithm, a cooperative evolutionary algorithm (SAPSO) based on PSO and simulated annealing (SA) algorithm is proposed. It can validly overcome the prem...
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ISBN:
(纸本)9781424451821
On the basis of analyzing the particleswarmoptimization (PSO) algorithm, a cooperative evolutionary algorithm (SAPSO) based on PSO and simulated annealing (SA) algorithm is proposed. It can validly overcome the premature problem in PSO through cooperative search between PSO and SA. Then, SAPSO is employed to train artificial neural network and applied to soft-sensing of melt-index of High Pressure Low-Density Polyethylene yield. The simulation results demonstrate that the model has effective generalization performance, higher precision and engineering practicability.
We use an effective global harmony search algorithm(EGHS)to solve two kinds of pressure vessel design *** general,the two problems are formulated as mixed-integer non-linear programming problems with several *** EGHS ...
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We use an effective global harmony search algorithm(EGHS)to solve two kinds of pressure vessel design *** general,the two problems are formulated as mixed-integer non-linear programming problems with several *** EGHS combines harmony search algorithm(HS)with concepts from the swarm intelligence of particle swarm optimization algorithm(PSO)to solve the two optimization *** EGHS algorithm has been applied to two typical problems with results better than previously *** results have demonstrated that the EGHS has strong convergence and capacity of space exploration on solving pressure vessel design problems.
Grid task scheduling(GTS)is a NP-hard *** paper proposes an optimized GTS algorithm which combines with the advantages of cloud model based on the particleswarmoptimization *** algorithm iterates tasks utilizing the...
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Grid task scheduling(GTS)is a NP-hard *** paper proposes an optimized GTS algorithm which combines with the advantages of cloud model based on the particleswarmoptimization *** algorithm iterates tasks utilizing the advantages of particle swarm optimization algorithm and then gets a set of candidate solutions *** addition,this algorithm modifies the value of entropy and excess entropy using the characteristics of cloud model and implements the transformation between qualitative variables and quantity of uncertain *** this algorithm makes particles fly to the global optimal solutions by exact searching in local areas. Theoretical analysis and simulation results show that this algorithm makes load balance of resource *** also avoids the problems of genetic algorithm and basic particle swarm optimization algorithm,which would easily fall into local optimal solutions and premature convergence caused by too much selected *** algorithm has the advantages of high precision and faster convergence and can be applied in task scheduling on computing grid.
Accurate cancer diagnosis can be achieved by performing microarray expression data classification. Various technology, including Support Vector Machines (SVMs), K Nearest Neighbor method (KNN), Neuro-Fuzzy models(NF),...
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Accurate cancer diagnosis can be achieved by performing microarray expression data classification. Various technology, including Support Vector Machines (SVMs), K Nearest Neighbor method (KNN), Neuro-Fuzzy models(NF), Neural Network (NN), etc. have been applied to analyze microarray expression data. In this paper, Complex Network based on genetic programming and particleswarmoptimization is proposed for analyzing microarray expression data. We propose an automatic method for constructing and evolving our complex network model. The structure of complex network is evolved using genetic programming, and the fine tuning of the parameters encoded in the structure is accomplished using particle swarm optimization algorithm. The relative performances of our model are reported. The results are comparable to those previously obtain.
Dissolved gas analysis is an effective method for the early detection of incipient fault in power *** improve the capability of interpreting the result of dissolved gas analysis,a technology is proposed in this *** Pa...
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Dissolved gas analysis is an effective method for the early detection of incipient fault in power *** improve the capability of interpreting the result of dissolved gas analysis,a technology is proposed in this *** particleswarmoptimization(PSO) technique is used to integrate with Back Propagation(BP) neural networks,and using particleswarm to optimize the network's weights and biases,the fault of transformers is simulated and *** results show that the accuracy of PSO-BP method is significantly higher than that of the conventional three-ratio *** the algorithm based on PSO-BP network model provides a more accurate,safe and reliable result for the fault diagnosis of transformers.
Evolutionary algorithms combined with artificial neural network (ANN) have been applied in RFID tag antenna optimization platform. An effective method for RFID tag antenna optimization by particleswarmoptimization (...
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Evolutionary algorithms combined with artificial neural network (ANN) have been applied in RFID tag antenna optimization platform. An effective method for RFID tag antenna optimization by particleswarmoptimization (PSO) algorithm or genetic algorithm (GA) combined with ANN is presented in this paper. ANN is used to establish the non-linear model of tag antenna which is shown to be as accurate as an electromagnetic simulator and can be used for constructing the fitness function of PSO and GA. The PSO and GA optimizers are developed and executed in C++. Finally, this optimization method is turned out to be much more efficient than any electromagnetic simulator optimization. In addition, the PSO optimization results show that it is faster than GA.
Dissolved gas analysis is an effective method for the early detection of incipient fault in power transformers. To improve the capability of interpreting the result of dissolved gas analysis, a technology is propo...
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ISBN:
(纸本)9781424479573
Dissolved gas analysis is an effective method for the early detection of incipient fault in power transformers. To improve the capability of interpreting the result of dissolved gas analysis, a technology is proposed in this paper. The particleswarmoptimization (PSO) technique is used to integrate with Back Propagation (BP) neural networks, and using particleswarm to optimize the network's weights and biases, the fault of transformers is simulated and discussed. The results show that the accuracy of PSO-BP method is significantly higher than that of the conventional three-ratio method. So the algorithm based on PSO-BP network model provides a more accurate, safe and reliable result for the fault diagnosis of transformers.
In order to solve the problem of linearization,complexity and poor accuracy for parameter estimate of Muskingum Routing Model at present,this paper introduces three modern intelligent algorithms-Genetic algorithm(GA),...
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In order to solve the problem of linearization,complexity and poor accuracy for parameter estimate of Muskingum Routing Model at present,this paper introduces three modern intelligent algorithms-Genetic algorithm(GA),Simulated Annealing algorithm(SA) and particle swarm optimization algorithm(PSO) for the parameter calibration of Muskingum *** specific simulation,the results of five methods are *** according to the calculation,comparison and analysis of five methods comprehensively,it is found that the results of three modern intelligent algorithms are fit significantly and better than traditional methods.
The flow shop scheduling problem (FSSP) is a NPHARD combinatorial problem with strong industrial background. Among the meta-heuristics, genetic algorithms attracted a lot of attention. However, lacking the m...
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The flow shop scheduling problem (FSSP) is a NPHARD combinatorial problem with strong industrial background. Among the meta-heuristics, genetic algorithms attracted a lot of attention. However, lacking the major evolution direction, the effectiveness of regular genetic algorithm is restricted. In this paper, the particleswarmoptimizationalgorithm (PSO) is introduced for better initial group. By combining PSO with GA, a hybrid optimizationalgorithm for FSSP is proposed. This method is validated on a series of benchmark datasets. Experimental results indicate that this method is efficient and competitive compared to some existing methods.
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