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.
An improved particleswarmoptimization (IPSO) algorithm is proposed to solve a typical combinatorial optimization problem: traveling salesman problem, which is a well-known NP-complete problem. In the improved algori...
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ISBN:
(纸本)9812565329
An improved particleswarmoptimization (IPSO) algorithm is proposed to solve a typical combinatorial optimization problem: traveling salesman problem, which is a well-known NP-complete problem. In the improved algorithm, particles not only adjust its own flying speed according to itself and the best individual of the swarm but also learn from other individuals according to certain probability. This kind of study behavior accords with the biological natural law even more, and furthermore helps to find the global optimum solution. At the same time, this paper proposes the concepts of Adjustment Operator and Adjustment Sequence based on which particleswarmoptimization (PSO) and IPSO algorithm were successfully rebuilt, according to the ideas of single node regulating algorithm. For solving traveling salesman problem, numerical simulation results show the effectiveness and efficiency of the proposed method.
Two off-line neural networks were trained by applying particle swarm optimization algorithm to create object model and object inverse model of model reference adaptive inverse control system. The method and procedure ...
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ISBN:
(纸本)078039044X
Two off-line neural networks were trained by applying particle swarm optimization algorithm to create object model and object inverse model of model reference adaptive inverse control system. The method and procedure in training the network of control system was given by using particleswarm. Double inverted pendulum system was used for research object in simulation. The result of experiment proved that this algorithm can obtain more stability performance, and easy to achieve.
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