A novel particle swarm optimization algorithm-random perturbation particle swarm optimization algorithm(RP-PSO) based on independence of population structure is proposed. To retain diversity of population and avoid be...
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A novel particle swarm optimization algorithm-random perturbation particle swarm optimization algorithm(RP-PSO) based on independence of population structure is proposed. To retain diversity of population and avoid being plunged to local optimum, it initializes the worst individual in population over again, at the same time, the best previous particle of each individual is randomly perturbed after evolutionary computation every time to improve its running efficiency and precision of over all optimization searching. Test results of complex functions demonstrate RAPSO is superior to basic particleswarmoptimization in quality and efficiency.
An effective method of making tradeoff between the optimize precision and optimize speed for load frequency control in the automatic generation control, which can improve the calculating process of particleswarm algo...
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ISBN:
(纸本)1424403316
An effective method of making tradeoff between the optimize precision and optimize speed for load frequency control in the automatic generation control, which can improve the calculating process of particleswarmalgorithm is presented in this paper. This method which is suit for the case that the object to be optimized is complicate can be used to accelerate optimizing process and save calculate time but not influence precision due to the fact that particle swarm optimization algorithm is not sensitive to the number of particles. The method of optimizing PI controller coefficient using promoted particleswarmalgorithm which is used to meet the different performance need in single-area and two-area interconnected power system is proposed respectively. The simulation result shows that the performance is better than the PI controller optimized by genetic algorithm.
This study intends to propose a two-stage clustering algorithm which consists of adaptive resonance theory 2 (ART2) neural network and binary particleswarm K-means optimization (BPSKO) algorithm for grouping the orde...
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This study intends to propose a two-stage clustering algorithm which consists of adaptive resonance theory 2 (ART2) neural network and binary particleswarm K-means optimization (BPSKO) algorithm for grouping the orders together in order to reduce the SMT setup time. The BPSKO algorithm integrates both the particleswarm. optimizationalgorithm and K-means algorithm. Besides, roulette selection operator is applied for avoiding premature convergence. Simulation results using four data sets, Iris, Wine, Vowel, and Glass are very promising. The results for an international industrial personal computer (PG) manufacturer show that the proposed algorithm, ART2+BPSKO, is superior to continuous particle swarm optimization algorithm. Through order clustering, scheduling orders belonging to the same cluster together can, reduce the production time as well as the machine idle time.
particle swarm optimization algorithm is a newly proposed population -based *** efficient in many optimization problems,it may encounter the problem of premature convergence and computational time *** this paper,we at...
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particle swarm optimization algorithm is a newly proposed population -based *** efficient in many optimization problems,it may encounter the problem of premature convergence and computational time *** this paper,we attempt to introduce parallel mechanism into PSO and proposes PPSO(Parallel PSO) *** test the PPSO on four widely known benchmark functions and the experiment results show the efficiency and efficacy of PPSO.
The Improved Vehicle Routing Problem(IVRP) and Location Allocation Problems(LAP) were considered synthetically. And a mathematic model about IVRP has been built *** purpose of this model was to minimize the total cost...
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The Improved Vehicle Routing Problem(IVRP) and Location Allocation Problems(LAP) were considered synthetically. And a mathematic model about IVRP has been built *** purpose of this model was to minimize the total cost by determining the location of the warehouse,allocating appropriate nurnber of vehicles for the selected warehouse and finding the optimal routing for each *** the same time,Improved particle swarm optimization algorithm with Genetic algorithm and Simulated Annealing were applied to solve an example of the *** good convergence state of the global best solution has proved the IVRP model was correct and the algorithm applied to solve IVRP was effective.
The purpose of this paper is to present and evaluate an improved Naive Bayes algorithm for clustering. Many researchers search for parameter values using EM *** is well-known that EM approach has a drawback-local opti...
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The purpose of this paper is to present and evaluate an improved Naive Bayes algorithm for clustering. Many researchers search for parameter values using EM *** is well-known that EM approach has a drawback-local optimal solution, so we propose a novel hybrid algorithm of the Discrete particleswarmoptimization (DPSO) and the EM approach to improve the global search performance. We evaluate this hybrid approach on 4 real-world data sets from UCI repository. In a number of experiments and comparisons,the hybrid DPSO+EM algorithm exhibits a more effective and outperforms the EM approach.
For the main steam temperature system of pulverized coal fired boiler, the modeling precision is not quite satisfactory based on the traditional transfer function. Utilizing the nonlinearity of thermal process, the pa...
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ISBN:
(纸本)9781424467129
For the main steam temperature system of pulverized coal fired boiler, the modeling precision is not quite satisfactory based on the traditional transfer function. Utilizing the nonlinearity of thermal process, the paper proposes the methodology of T-S fuzzy neural network for data fitting. The antecedent parameters are determined by selected centers obtained from simplified subtractive clustering method, and the number of 'If-Then' rules is automatically generated. Afterwards, the improved particle swarm optimization algorithm is proposed to assign the initial consequent parameters of rules which are then fine-tuned by BP algorithm. The simulation results show that the algorithm not only achieves the goal of higher precision, but also exhibits higher generalization ability with respect to the problem of identification and optimization of the main steam temperature system.
Reservoirs usually have multipurpose, such as flood control, water supply, hydropower and recreation. Deriving reservoirs operation rules are very important because it could help guide operators determine the release....
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Reservoirs usually have multipurpose, such as flood control, water supply, hydropower and recreation. Deriving reservoirs operation rules are very important because it could help guide operators determine the release. For fulfilling such work, the use of neural network has presented to be a cost-effective technique superior to traditional statistical methods. But their training, usually with back-propagation (BP) algorithm or other gradient algorithms, is often with certain drawbacks. In this paper, a newly developed method, simulation with radial basis function neural network (RBFNN) model is adopted. Exemplars are obtained through a simulation model, and RBF neural network is trained to derive reservoirs operation rules by using particleswarmoptimization (PSO) algorithm. The Yellow River upstream multi-reservoir system is demonstrated for this study.
Reliability prediction has been widely studied in many research fields to improve product and system reliability in manufacturing systems. Traditionally, to establish the prediction model, modelers would use all train...
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Reliability prediction has been widely studied in many research fields to improve product and system reliability in manufacturing systems. Traditionally, to establish the prediction model, modelers would use all training data without preference. However, the prediction model based only on the most recent data may have better performance. In this paper, to realize an accurate prediction with the most recent data sets, we use the grey model to establish the reliability model. Then, the cubic spline function is integrated into the grey model to enhance the prediction capability of GM(1, 1), a single variable first order grey model. The newly generated model is defined as 3spGM(1, 1). To further improve the prediction accuracy, the particleswarmoptimization (PSO) algorithm is applied to 3spGM(1, 1). We call the improved version P-3spGM(1, 1). Finally, we validated the effectiveness of the proposed model using failure data sets of electric product manufacturing systems.
The LiNbO3-based polarization controller is widely used, but it needs to be calibrated in order to cancel the remaining birefringence. The calibration of the LiNbO3 polarization controller is untrivial because there a...
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The LiNbO3-based polarization controller is widely used, but it needs to be calibrated in order to cancel the remaining birefringence. The calibration of the LiNbO3 polarization controller is untrivial because there are several stages, and for each stage, at least four parameters, including V-A,V-Bias, V-C,V-Bias, V-0, and V-pi, need to be calibrated. A smart calibration approach is presented theoretically and experimentally. The particleswarmoptimization (PSO) algorithm is used as an adaptive searching algorithm. The experiment results show that the PSO algorithm is powerful to optimize the operation of LiNbO3-based multistage polarization controllers. It takes only less than 1 min for all the stages of the polarization controller to be thoroughly calibrated.
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