particleswarmoptimization (PSO) algorithm is a heuristic global optimization technology based on colony intelligence. For improving the searching ability of this algorithm, a search factor is added into the movement...
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ISBN:
(纸本)9781424467129
particleswarmoptimization (PSO) algorithm is a heuristic global optimization technology based on colony intelligence. For improving the searching ability of this algorithm, a search factor is added into the movement of the particle to develop it, and the developed PSO algorithm is used for optimal design of water-supply pipe network. Reliability constraint, which is based on the principle of average distribution flux, is added into the process of optimal design to avoid producing tree pipe network for the reason of economic flux distribution. The algorithm is applied to a simple test network. Comparison with the results of basic PSO algorithm shows that the developed algorithm has stronger global optimizing ability and better search accuracy for optimal design of water-supply pipe network.
Because the network intrusion behaviors are characterized with uncertainty, complexity and diversity, an intrusion detection method based on neural network and particle swarm optimization algorithm (PSOA) is presented...
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Because the network intrusion behaviors are characterized with uncertainty, complexity and diversity, an intrusion detection method based on neural network and particle swarm optimization algorithm (PSOA) is presented in this paper. The novel structure model has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong self-learning and faster convergence, this intrusion detection method can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. Utilizing the character that rough set can keep the discern ability of original dataset after reduction, the reduces of the original dataset are calculated and used to train neural network, which increase the detection accuracy. We apply this technique on KDD99 data set and get satisfactory results. The experimental result shows that this intrusion detection method is feasible and effective.
particleswarmoptimization (PSO) is a new stochastic optimization technique based on swarm intelligence. In this paper, we introduce the basic principles of PSO firstly. Then, the research progress on PSO algorithm i...
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particleswarmoptimization (PSO) is a new stochastic optimization technique based on swarm intelligence. In this paper, we introduce the basic principles of PSO firstly. Then, the research progress on PSO algorithm is summarized in several fields, such as parameter selection and design, population topology, hybrid PSO algorithm etc. Finally, some vital applications and aspects that may be conducted in the future investigations are discussed.
The efficiency of utilizing the satellite communications resource and system can be improved by optimizing the satellite broadcasting scheduling with genetic *** drawbacks such as complicated genetic operation, tardy ...
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The efficiency of utilizing the satellite communications resource and system can be improved by optimizing the satellite broadcasting scheduling with genetic *** drawbacks such as complicated genetic operation, tardy convergent speed and the aptness to sink into local minimum within the Genetic algorithm(GA) have encouraged a satellite broadcasting scheduling approach for resolving the scheduling *** approach was based on the particleswarmoptimization(PSO) algorithm which involved in processes such as constructing the model of satellite broadcasting scheduling,initialization of the particles and particle *** has been shown by simulation analysis that satellite broadcasting scheduling based on the PSO algorithm was feasible and its optimization result was significant.
particleswarmoptimization is a new heuristic global optimizationalgorithm based on swarm intelligence. The algorithm is simple, easy to implement and has good performance of optimization. Now it has been applied in...
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ISBN:
(纸本)9787811240559
particleswarmoptimization is a new heuristic global optimizationalgorithm based on swarm intelligence. The algorithm is simple, easy to implement and has good performance of optimization. Now it has been applied in many fields. However, when optimizing multidimensional and multimodal functions, the basic particleswarmoptimization is apt to be trapped in local optima, which is called premature. This paper proposes a modified optimization method (MPSO), which considers for convergence speed and search capacity. This modified algorithm has stronger exploitation ability, so it can prevent premature well. Simulation results show that this modified algorithm performs better performance. It is used in segmentation of infrared image. The experimental results show that the modified PSO not only realizes the image segmentation well;but also improves the speed greatly.
This paper presents a higher-order multivari-ate Markov chain model combined with particleswarmoptimization *** to some deciencies,such as only considering the maximum probability while ignoring the effect of the ot...
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This paper presents a higher-order multivari-ate Markov chain model combined with particleswarmoptimization *** to some deciencies,such as only considering the maximum probability while ignoring the effect of the other probabilities,the traditional method of probability distribution has been replaced by the level characteristics value of fuzzy set theory;further more particle swarm optimization algorithm has been employed to optimize the coefcient of level characteristics *** recent years,air pollution acutely aggravates chronic diseases in mankind,such as sulfur dioxide pollution which plays a most important role in acid *** order to confront air pollution problems and to plan abatement strategies,both the scientic community and the relevant authorities have focused on monitoring and analyzing the atmospheric pollutants *** the forecast of air pollutants as a case,we illustrate the improvement of accuracy and efciency of the new method and the result shows the new method is predominant in forecasting of multivariate and non-linear data.
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
In this paper the numerical computation theory of internal trajectory was researched. Based on this, a multi-parameter fitting calculation model was established in order to get higher precise computation results. Thro...
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In this paper the numerical computation theory of internal trajectory was researched. Based on this, a multi-parameter fitting calculation model was established in order to get higher precise computation results. Through analysing the model, this paper selected the burning rate exponent n and the specific heat ratio γ as the sensitive internal trajectory parameters, meanwhile selected the in-bore maximum pressure m p and the muzzle velocity g v correspondingly as the fitting parameters to improve the fitting efficiency. To do the fitting work, an improved particleswarm optimisation (PSO) algorithm was presented in this paper. The calculation results indicated that the fitting process using improved PSO was obviously better than those using standard PSO and genetic algorithm (GA) in global searching ability, convergence rate and fitting precision. In this case, the method in this paper is more suitable to the multi-parameter fitting calculation of internal trajectory considering requirements of reliable design and on site testing.
In the analysis of electronic circuit fault diagnosis based on support vector regression (SVR), irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease o...
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In the analysis of electronic circuit fault diagnosis based on support vector regression (SVR), irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper used rough sets as a preprocessor of SVR to select a subset of input variables and employed the particle swarm optimization algorithm (PSOA) to optimize the parameters of SVR. Additionally, the proposed PSOA-SVR model that can automatically determine the optimal parameters was tested on the prediction of electronic circuit fault. Then, we compared the proposed PSOA-SVR model with other artificial intelligence models of (BPN and fix-SVR). The experiment indicates that the proposed method is quite effective and ubiquitous.
A new algorithm for timetabling based on particle swarm optimization algorithm was proposed, and the key problems such as particle coding, fitness function fabricating, particleswarm initialization and crossover oper...
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A new algorithm for timetabling based on particle swarm optimization algorithm was proposed, and the key problems such as particle coding, fitness function fabricating, particleswarm initialization and crossover operation were settled. The fitness value declines when the evolution generation increases. The results showed that it was a good solution for course timetabling problem in the educational system.
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