It is difficult to have good performance to control large delay time system. A neural network identification method for nonlinear system's delay time was discussed. Using the abrupt mutation resulted from the trai...
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ISBN:
(纸本)9781424473281
It is difficult to have good performance to control large delay time system. A neural network identification method for nonlinear system's delay time was discussed. Using the abrupt mutation resulted from the training error sum square of the real output and the expected output of the network, this method changed the input sample period of the neural network so that it could discriminate the delay time of the nonlinear model. Combining the discrimination of neural network system with long time delay and the control method based on model prediction, searching PID controller parameters based on ant colony optimization algorithm, it was applied to control boiler combustion system. The simulation results show that this scheme has much better advantage of celerity and robustness.
The past two decades have witnessed tremendous research activities in optimization methods for communication networks. One important problem in communication networks is the Weighted Ring Arc-Loading Problem (combinat...
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ISBN:
(纸本)9783642128417
The past two decades have witnessed tremendous research activities in optimization methods for communication networks. One important problem in communication networks is the Weighted Ring Arc-Loading Problem (combinatorial optimization NP-complete problem). This problem arises in engineering and planning of the Resilient Packet Ring (RPR) systems. Specifically, for a given set of non-split and uni-directional point-to-point demands (weights), the objective is to find the routing for each demand (i.e., assignment of the demand to either clockwise or counter-clockwise ring) so that the maximum arc load is minimised. In this paper, we propose a Hybrid ant colony optimization algorithm to solve this problem. We compare our results with the results obtained by the standard Genetic algorithm and Particle Swarm optimization, used in literature.
This paper studies container loading optimization *** problem is a subset of rectangular boxes loaded into a rectangular container with fixed dimensions such that maximize container's utilization ratio.A mathemati...
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This paper studies container loading optimization *** problem is a subset of rectangular boxes loaded into a rectangular container with fixed dimensions such that maximize container's utilization ratio.A mathematical model is *** principles which include space division,space merger,residual subspace omitted and loading rule are presented.A hybrid algorithm which integrate ant colony optimization algorithm with above principles is used to solve the container loading *** simulation results show that the model and the algorithm are effective.
This paper studies container loading optimization problem. This problem is a subset of rectangular boxes loaded into a rectangular container with fixed dimensions such that maximize container's utilization ratio. ...
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This paper studies container loading optimization problem. This problem is a subset of rectangular boxes loaded into a rectangular container with fixed dimensions such that maximize container's utilization ratio. A mathematical model is given. Some principles which include space division, space merger, residual subspace omitted and loading rule are presented. A hybrid algorithm which integrate ant colony optimization algorithm with above principles is used to solve the container loading problem. The simulation results show that the model and the algorithm are effective.
A new method is developed to design a multi-objective and multi-pollutant sensitive air quality monitoring network (AQMN) for an industrial district. A dispersion model is employed to estimate the ground level concent...
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A new method is developed to design a multi-objective and multi-pollutant sensitive air quality monitoring network (AQMN) for an industrial district. A dispersion model is employed to estimate the ground level concentration of the air pollutants emitted from different emission sources. The primary objective of AQMN is providing the maximum information about the pollutant with respect to (1) maximum coverage area, (2) maximum detection of violations over ambient air standards and (3) sensitivity of monitoring stations to emission sources. ant colony optimization algorithm (ACO) and Genetic algorithm (GA) are adopted as the optimization tools to identify the optimal configuration of the monitoring network. The comparison between the results of ACO and GA shows that the performance of both algorithms is acceptable in finding the optimal configuration of AQMN. The application of the method to a network of existing refinery stacks indicates that three stations are suitable to cover the study area. The sensitivity of the three optimal station locations to emission sources is investigated and a database including the sensitivity of stations to each source is created.
Based on particle swarm optimizationalgorithm, this paper presents a grid scheduling optimizationalgorithm combing the advantages of ant colony optimization algorithm. The algorithm processes task scheduling through...
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ISBN:
(纸本)9780878492695
Based on particle swarm optimizationalgorithm, this paper presents a grid scheduling optimizationalgorithm combing the advantages of ant colony optimization algorithm. The algorithm processes task scheduling through particle swarm optimizationalgorithm to get a group of relatively optimal solutions, and then conducts small-area local search with ant colony optimization algorithm. 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 optimization algorithm and particle swarm optimizationalgorithm, and can be applied to task scheduling in grid computing.
Remote sensing images classification method can be divided into supervised classification and unsupervised classification according to whether there is prior knowledge. Supervised classification is a machine learning ...
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ISBN:
(纸本)9783642173158
Remote sensing images classification method can be divided into supervised classification and unsupervised classification according to whether there is prior knowledge. Supervised classification is a machine learning procedure for deducing a function from training data;unsupervised classification is a kind of classification which no training sample is available and subdivision of the feature space is achieved by identifying natural groupings present in the images values. As a branch of swarm intelligence, ant colony optimization algorithm has self-organization, adaptation, positive feedback and other intelligent advantages. In this paper, ant colony optimization algorithm is tentatively introduced into unsupervised classification of remote sensing images. A series of experiments are performed with remote sensing data. Comparing with the K-mean and the ISODATA clustering algorithm, the experiment result proves that artificial ant colony optimization algorithm provides a more effective approach to remote sensing images classification.
In the present study, ant colony optimization algorithm is used for an inverse heat transfer problem of parameter estimation. Temperature dependent thermal conductivity and specific heat are estimated simultaneously. ...
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In the present study, ant colony optimization algorithm is used for an inverse heat transfer problem of parameter estimation. Temperature dependent thermal conductivity and specific heat are estimated simultaneously. Performance of the present algorithm is first analyzed by using the temperature data obtained from the solution of direct problem. Effect of measurement error on the property estimation is then analyzed. Accuracy of estimated properties is found to be good in both the cases. Experiments are then conducted on a cotton duck fabric exposed to 40 kW/m(2) radiant heat flux using a bench top test according to ISO 6942. Temporal variation of sensor temperature is used to estimate the thermal conductivity and specific heat of the fabric sample. The estimated values are in agreement with the results available in the literature. Property values estimated in the study are included in a coupled conduction-radiation heat transfer model for fabrics. Performance of the fabric sample is further analyzed with the numerical model for high heat fluxes. The simulation results compare quite well with experimental data at 80 kW/m(2) radiant heat and 70 kW/m(2) flame exposures. Flame exposure experiment is conducted according to ISO 9151. Good agreement between the numerical and experimental results are found for both the exposures. (C) 2016 Elsevier Ltd. All rights reserved.
Optical components production belongs to the typical jobbing work, and its scheduling problem is a significant research in advanced optical manufacture technique. According to the manufacturing technology for high pre...
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ISBN:
(纸本)9780819480897
Optical components production belongs to the typical jobbing work, and its scheduling problem is a significant research in advanced optical manufacture technique. According to the manufacturing technology for high precision optical components, the production scheduling model which is combined workshop scheduling theory with improved antcolonyalgorithm (ACA) is introduced in this paper. In order to reduce the inherent deficiency of traditional antalgorithm for local optimal solution and stagnation, an improved algorithm which can simulate real antcolony sensation and consciousness may enhance the efficiency of basic ACA. The simulation experiment shows the robustness for this improved algorithm.
In view of solving multi-objective path planning in the static environment,there are some faults for antcolonyoptimization(ACO),such as the long computation and easy to fall into local *** solve these problems,the A...
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In view of solving multi-objective path planning in the static environment,there are some faults for antcolonyoptimization(ACO),such as the long computation and easy to fall into local *** solve these problems,the ACO based on cat swarm optimization(CSO) algorithm searching model(CSOACO) is *** this algorithm,the introduction of CSO algorithm search pattern realizes the local search in the current solution for antcolony individuals,which not only enrich the diversity of solution,but improve the accuracy of the ***,the new algorithm is simulated in MATLAB for picking robot multi-objective path planning *** the simulation analysis,not only set the parameters,but compare CSOACO with other *** results show that the algorithm can accelerate the convergence speed,search to the global optimal solution and realize the multiobjective path planning of picking robot.
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