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.
Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimiza- tion problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. ...
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Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimiza- tion problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. particleswarmoptimization (PSO) algorithm is developed for nonlinear optimization problems with both contin- uous and discrete variables. In order to obtain a global optimum solution quickly, PSO algorithm is applied to solve the problem of blending scheduling under uncertainty. The calculation results based on an example of gasoline blending agree satisfactory with the ideal values, which illustrates that the PSO algorithm is valid and effective in solving the blending scheduling problem.
The performance of a fragile watermarking method based on discrete cosine transform (DCT) has been improved in this paper by using intelligent optimizationalgorithms (IOA). namely genetic algorithm, differential evol...
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The performance of a fragile watermarking method based on discrete cosine transform (DCT) has been improved in this paper by using intelligent optimizationalgorithms (IOA). namely genetic algorithm, differential evolution algorithm, clonal selection algorithm and particle swarm optimization algorithm. In DCT based fragile watermarking techniques, watermark embedding can usually be achieved by modifying the least significant bits of the transformation coefficients. After the embedding process is completed, transforming the modified coefficients from the frequency domain to the spatial domain produces some rounding errors due to the conversion of real numbers to integers. The rounding errors caused by this transformation process were corrected by the use of intelligent optimizationalgorithms mentioned above. This paper gives experimental results which show the feasibility of using these optimizationalgorithms for the fragile watermarking and demonstrate the accuracy of these methods. The performance comparison of the algorithms was also realized. (C) 2009 Elsevier B.V. All rights reserved.
Magnetic flux leakage (MFL) testing is widely used to examine ferromagnetic materials. For the reason of estimating the sizes of cracks in metals is important in piping industries, a fast method based on particle swar...
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Magnetic flux leakage (MFL) testing is widely used to examine ferromagnetic materials. For the reason of estimating the sizes of cracks in metals is important in piping industries, a fast method based on particle swarm optimization algorithm is proposed for reconstructing the sizes of rectangular crack in this article. Considering the magnetic leakage field intensity is related to the air gap between the inspection specimen and the sensor, we give the reconstruction results in different lift-off values. Besides, the influence of different magnetic conditions to the reconstruction effectiveness has been investigated. The simulation results have shown the rapidity and accuracy of the proposed method. (C) 2009 Elsevier Ltd. All rights reserved.
Aiming at the demerits of extremum random disturbed arithmetic operator of a particle swarm optimization algorithm, the reasonable amelioration is put forward based on the design idea of extremum random disturbed arit...
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ISBN:
(纸本)9780769537450
Aiming at the demerits of extremum random disturbed arithmetic operator of a particle swarm optimization algorithm, the reasonable amelioration is put forward based on the design idea of extremum random disturbed arithmetic operator. An improved particle swarm optimization algorithm is put forward and applied to parameter selection of support vector machine. The regress modeling of two common functions based on least square support vector machine is to be as examples and the simulation experiment is done. The results show that the amelioration of arithmetic operator is necessary and feasible. The convergence velocity and precision of algorithm are enhanced.
Generally, Hardware/Software (HW/SW) partitioning can be approximately resolved through some kinds of optimal algorithms. Based oil both characteristics of HW/SW partitioning and particleswarmoptimization (PSO) algo...
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ISBN:
(纸本)9783642030949
Generally, Hardware/Software (HW/SW) partitioning can be approximately resolved through some kinds of optimal algorithms. Based oil both characteristics of HW/SW partitioning and particleswarmoptimization (PSO) algorithm, a novel parallel FlW/SW partitioning method is proposed in this paper. A model of parallel HW/SW partitioning on the basis of PSO algorithm is established after analyzing the particularity of HW/SW partitioning. A hybrid strategy of PSO and Tabu Search (TS) is proposed in this paper, which uses the intrinsic parallelism of PSO and the memory function of TS to speed tip and improve the performance of PSO. To settle the problem of premature convergence, the reproduction and crossover operation of genetic algorithm (GA) is also introduced into procedure of PSO. Experimental results indicate that the parallel PSO algorithm can efficiently reduce the running time even for large task graphs.
Several improvements about basic particleswarmoptimization (PSO) algorithm has been presented. In the improved particleswarmoptimization (IPSO) algorithm, the particles are initialized with chaos
Several improvements about basic particleswarmoptimization (PSO) algorithm has been presented. In the improved particleswarmoptimization (IPSO) algorithm, the particles are initialized with chaos
Bio-inspired evolutionary algorithms are probabilistic search methods that simulate the natural biological evolution or the behaviour of biological entities. Such algorithms can be used to obtain near optimal solution...
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ISBN:
(纸本)9781424450534
Bio-inspired evolutionary algorithms are probabilistic search methods that simulate the natural biological evolution or the behaviour of biological entities. Such algorithms can be used to obtain near optimal solutions in optimization problems, for which traditional mathematical techniques may fail. This paper does a comparative study of results of five evolutionary algorithms: Genetic algorithm (GA), particleswarmoptimization (PSO) algorithm, Artificial Bee Colony (ABC) algorithm, Invasive Weed optimization (IWO) algorithm and Artificial Immune (AI) algorithm when applied to some standard benchmark multivariable functions.
This paper presents a comprehensive study of forecasting a day-ahead of load and locational marginal pricing (LMP) using artificial intelligent systems. An artificial neural network (ANN) is trained with a stochastic ...
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ISBN:
(纸本)9781424435098
This paper presents a comprehensive study of forecasting a day-ahead of load and locational marginal pricing (LMP) using artificial intelligent systems. An artificial neural network (ANN) is trained with a stochastic optimization technique called particleswarmoptimization (PSO). This training algorithm works to adjust the network weights and biases as to minimize the error function. Wavelet transformed data is fed into neural network as preprocessing stage in order to get a better price pattern that will be reliable for forecasting. The proposed models were trained and tested using real data consists of historical load and LMP and corresponding influence variables such as weather information and marginal losses cost (MLC). The data used is from NYISO and Weather Source Stations, Buffalo, New York over a period of three years (2001-2003). Simulation results are compared with that of conventional back-propagation (BP) neural network and radial basis function network (RBFN) and provided highly accurate generalization capability.
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