Threshold extraction is the fundamental step in multi-threshold image segmentation. This paper has introduced particle swarm optimization algorithm (PSO) for threshold extraction. But when dealing with the peaky high ...
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ISBN:
(纸本)9781424409723
Threshold extraction is the fundamental step in multi-threshold image segmentation. This paper has introduced particle swarm optimization algorithm (PSO) for threshold extraction. But when dealing with the peaky high dimension function of maximum entropy for multi-threshold image segmentation, the conventional PSO is apt to be trapped in local optima called premature. This can cause image segmentation failure. This paper proposes a modified particleswarmoptimization method (MPSO), which improves convergence speed and search capacity and avoid the premature phenomena when used in threshold extraction. Simulation results show that the MPSO has better performance and quicker speed. The experimental results also show that with the modified PSO as a threshold extraction method, the image is segmented fairly well and the segmentation speed improves greatly.
This paper presents an ARIMA model which uses particle swarm optimization algorithm (PSO) for model estimation. Because the traditional estimation method is complex and may obtain very bad results, PSO which can be im...
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ISBN:
(纸本)9783642052521
This paper presents an ARIMA model which uses particle swarm optimization algorithm (PSO) for model estimation. Because the traditional estimation method is complex and may obtain very bad results, PSO which can be implemented with ease and has a powerful optimizing performance is employed to optimize the coefficients of AMNIA. In recent years, inflation and deflation plague the world moreover the consumer price index (CPI) which is a measure of the average price of consumer goods and services purchased by households is usually observed as an important indicator of the level of inflation, so the forecast of CPI has been focused on by both scientific community and relevant authorities. Furthermore, taking the forecast of CPI as a case, we illustrate the improvement of accuracy and efficiency of the new method and the result shows it is predominant in forecasting.
In this paperan algorithm based on particle swarm optimization algorithm for RBF neural network is propose. With particle swarm optimization algorithm, neural network weights are optimized. Also through the dynamic re...
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ISBN:
(纸本)9781424446421
In this paperan algorithm based on particle swarm optimization algorithm for RBF neural network is propose. With particle swarm optimization algorithm, neural network weights are optimized. Also through the dynamic regulation of the number of radial basis function in neural network hidden layer, neural network structure is optimized. The algorithm is applied to gearbox fault diagnosis. Experimental results show the effectiveness and great performance. Classification effect of neural network based on particle swarm optimization algorithm is better than that of the RBF neural network for identifying effectively the different status of gearbox and monitoring timely the status changes of gearbox. Also it can reduce the time for fault diagnosis and improve accuracy of fault diagnosis.
A novel Quantum-behaved particle swarm optimization algorithm with probability (P-QPSO) is introduced to improve the global convergence property of QPSO. In the proposed algorithm, all the particles keep the original ...
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ISBN:
(纸本)9781467365932
A novel Quantum-behaved particle swarm optimization algorithm with probability (P-QPSO) is introduced to improve the global convergence property of QPSO. In the proposed algorithm, all the particles keep the original evolution with large probability, and do not update the position of particles with small probability, and re-initialize the position of particles with small probability. Seven benchmark functions are used to test the performance of P-QPSO. The results of experiment show that the proposed technique can increase diversity of population and converge more rapidly than other evolutionary computation methods.
作者：
Liu, YiHangzhou Dianzi Univ
Inst Management Sci & Informat Engn Hangzhou 310018 Zhejiang Peoples R China
Logistics distribution locating problem is an important area in Logistics, which select the most reasonable location of distribution centers from many places. This paper establish the Cellular PSO algorithm, which com...
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ISBN:
(纸本)9780769535616
Logistics distribution locating problem is an important area in Logistics, which select the most reasonable location of distribution centers from many places. This paper establish the Cellular PSO algorithm, which combine the particle swarm optimization algorithm and cellular automata. This algorithm was tested in the simulation experiment, and the result indicate that the Cellular PSO algorithm is a effective method of solving the problem of choosing the distribution centers location which can overcome the low precision of the basic particle swarm optimization algorithm. In additional, the Cellular PSO algorithm has high quality and efficiency of searching.
A particle swarm optimization algorithm (PSO) is presented for vehicle path planning in the paper. particleswarmoptimization proposed by Kennedy and Eberhart is derived from the social behavior of the birds foraging...
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ISBN:
(纸本)9783037853696
A particle swarm optimization algorithm (PSO) is presented for vehicle path planning in the paper. particleswarmoptimization proposed by Kennedy and Eberhart is derived from the social behavior of the birds foraging. particle swarm optimization algorithm a kind of swarm-based optimization *** simulation experiments performed in this study show the better vehicle path planning ability of PSO than that of adaptive genetic algorithm and genetic algorithm. The experimental results show that the vehicle path planning by using PSO algorithm has the least cost and it is indicated that PSO algorithm has more excellent vehicle path planning ability than adaptive genetic algorithm,genetic algorithm.
Coalition is an important way of cooperation for multi-Agent system. To maximize the summation of the coalition values, and to search for an optimized coalition structure in a minimal searching range, a coalition stru...
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ISBN:
(纸本)1424403316
Coalition is an important way of cooperation for multi-Agent system. To maximize the summation of the coalition values, and to search for an optimized coalition structure in a minimal searching range, a coalition structure optimizationalgorithm in multi-Agent systems based on particleswarmalgorithm (PSO) is proposed. A comparison is made between the operation performances of Generic algorithm (GA) and particleswarmoptimization in this matter through simulation experiment. The result of the simulation shows the effectiveness of the PSO algorithm.
Reasonable warehouse storage planning and assignment is the key to reduce the product storage and retrieve time and improve warehouse operation efficiency. Moreover, capturing and sharing the information of warehouse ...
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ISBN:
(纸本)9781538635735
Reasonable warehouse storage planning and assignment is the key to reduce the product storage and retrieve time and improve warehouse operation efficiency. Moreover, capturing and sharing the information of warehouse in real time is the premise of warehouse location assignment, and internet of tings has provided this information required by leveraging the growing ubiquity of radio-frequency identification (RFID). Firstly this paper describes the layout of intelligent warehouse, then a multi-objective intelligent warehouse location assignment model is proposed with many constrain rules. Finally we develop an improved particle swarm optimization algorithm to solve the model, and verify the effectiveness of the model.
In this paper, a space-time simulation model based on particle swarm optimization algorithm for stadium evacuation is presented. In this new model, the fast evacuation, going with the crowd and the panic behaviors are...
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ISBN:
(纸本)9781479914883
In this paper, a space-time simulation model based on particle swarm optimization algorithm for stadium evacuation is presented. In this new model, the fast evacuation, going with the crowd and the panic behaviors are considered and the corresponding moving rules are defined. The model is applied to a stadium and simulations are carried out to analyze the space-time evacuation efficiency by different behaviors. The simulation results show that the behaviors of going with the crowd and panic will slow down the evacuation process while quickest evacuation psychology can accelerate the process, and panic is helpful to some extent. The setting of parameters is discussed to obtain best performance. The simulation results can offer effective suggestions for evacuees under emergency situation.
Niche is an important technique for multi-peak function optimization. When the particleswarmoptimization (PSO) algorithm is used in multi-peak function optimization, there exist some problems, such as easily falling...
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ISBN:
(纸本)9783037859537
Niche is an important technique for multi-peak function optimization. When the particleswarmoptimization (PSO) algorithm is used in multi-peak function optimization, there exist some problems, such as easily falling into prematurely, having slow convergence rate and so on. To solve above problems, an improved PSO algorithm based on niche technique is brought forward. PSO algorithm utilizes properties of swarm behavior to solve optimization problems rapidly. Niche techniques have the ability to locate multiple solutions in multimodal domains. The improved PSO algorithm not only has the efficient parallelism but also increases the diversity of population because of the niche technique. The simulation result shows that the new algorithm is prior to traditional PSO algorithm, having stronger adaptability and convergence, solving better the question on multi-peak function optimization.
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