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求助，幫忙檢索一下這個論文，不勝感激， A resilient particle swarm optimization algorithm with dynamically changing inertia weight [ 發(fā)自手機(jī)版 http://www.gaoyang168.com/3g ] |
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wzd123
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2樓2013-08-02 12:39:44
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Check record to add to Selected Records 1. Accession number: 20133016541197 Title: A resilient particle swarm optimization algorithm with dynamically changing inertia weight Authors: Wu, Zhi Dong1 ; Zhou, Sui Hua1; Feng, Shi Min1; Xiao, Zu Jing1 Author affiliation: 1 Dept. of Weaponry Eng, Naval Univ. of Engineering, Wuhan, 430033, China Source title: Advanced Materials Research Abbreviated source title: Adv. Mater. Res. Volume: 712-715 Monograph title: Advances in Manufacturing Science and Engineering Issue date: 2013 Publication year: 2013 Pages: 2423-2427 Language: English ISSN: 10226680 ISBN-13: 9783037857243 Document type: Conference article (CA) Conference name: 4th International Conference on Manufacturing Science and Engineering, ICMSE 2013 Conference date: March 30, 2013 - March 31, 2013 Conference location: Dalian, China Conference code: 97852 Sponsor: Northeastern University; Harbin Institute of Technology; Jilin University; Hong Kong Industrial Technology Research Centre Publisher: Trans Tech Publications Ltd, Kreuzstrasse 10, Zurich-Durnten, CH-8635, Switzerland Abstract: To overcome the shortage that the particle swarm optimization is prone to trap into local extremum searching for the lost in population diversity, a strategy in which the velocity is not dependent on the size of distance between the individual and the optimal particle but only dependent on its direction is proposed. The average similarity of particles in the population is seem as the measure of population diversity and it is used to balance the global and local searching of the algorithm. Based on establishing the relationship between inertia weight and the measure of population diversity which has been inserted into the algorithm, A resilient particle swarm optimization algorithm with dynamically changing inertia weight(ARPSO) was proposed. ARPSO was applied in simulation experiment. The results show that the algorithm has the ability to avoid being trapped in local extremum and advance the probability of finding global optimum. ? (2013) Trans Tech Publications, Switzerland. Number of references: 9 Main heading: Algorithms Controlled terms: Manufacture - Particle swarm optimization (PSO) Uncontrolled terms: Average similarities - Globaloptimum - Inertia weight - Local extremum - Local searching - Particle swarm optimization algorithm - Population diversity - Resilient adjustment Classification code: 537.1 Heat Treatment Processes - 723 Computer Software, Data Handling and Applications - 921 Mathematics DOI: 10.4028/www.scientific.net/AMR.712-715.2423 Database: Compendex Compilation and indexing terms, ? 2012 Elsevier Inc. |
3樓2013-08-02 14:34:35
wzd123
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4樓2013-08-03 06:51:54
wzd123
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5樓2013-08-03 12:24:19
【答案】應(yīng)助回帖
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Accession number: 20132616450074 Title: Reliability allocation of underwater experiment system based on particle swarm optimization Authors: Xiong, Lu1 ; Gong, Shen-Guang1 Author affiliation: 1 Naval University of Engineering, Wuhan, China Source title: Journal of Networks Abbreviated source title: J. Netw. Volume: 8 Issue: 6 Issue date: 2013 Publication year: 2013 Pages: 1292-1299 Language: English ISSN: 17962056 Document type: Journal article (JA) Publisher: Academy Publisher, P.O.Box 40,, OULU, 90571, Finland Abstract: The problem of system reliability allocation is often solved by direct search method. The shortage, which affects the application of this method, is the large calculation amount of complex system architecture. Particle Swarm Optimization (PSO) is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. The particle swarm optimization, which attracted the interest of researchers. In this paper, a kind of PSO algorithm is proposed to solve underwater experimental system reliability problems. In addition, the reliability of the system model is established as well, the model is numerically simulated by PSO algorithm and examples are provided. The results show that compared to other algorithms, PSO has a better adaptability and can solve the optimal solution more stably without the precocious weakness, which is more suitable for reliability optimization of a system underwater with a more complex structure. © 2013 ACADEMY PUBLISHER. Number of references: 23 Main heading: Particle swarm optimization (PSO) Controlled terms: Algorithms - Computer simulation - Reliability Uncontrolled terms: Direct search methods - Objective functions - Optimization problems - PSO algorithms - Reliability allocation - Reliability optimization - Series and Parallel systems - Underwater experiments Classification code: 421 Strength of Building Materials; Mechanical Properties - 723 Computer Software, Data Handling and Applications - 723.5 Computer Applications - 921 Mathematics DOI: 10.4304/jnw.8.6.1292-1299 Database: Compendex Compilation and indexing terms, © 2012 Elsevier Inc. |
6樓2013-08-03 17:57:54
wzd1237樓2013-08-03 17:58:50
wzd123
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8樓2013-09-09 10:46:08
zhaoyang1989
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9樓2013-09-10 09:48:41
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