(3.230.154.160) 您好!臺灣時間:2021/05/07 18:34
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:李政哲
研究生(外文):Cheng-Che Lee
論文名稱:基於閃避策略的分群式粒子群最佳化演算法
論文名稱(外文):A Multi-Group Particle Swarm Optimization Algorithm Using a Dodge Strategy
指導教授:阮議聰
指導教授(外文):Yee-Jsong Juan
學位類別:碩士
校院名稱:國立臺灣海洋大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:64
中文關鍵詞:演化計算粒子群最佳化演算法分群式粒子群最佳化演算法閃避策略
外文關鍵詞:Evolutionary Algorithm(EA)Particle Swarm Optimization(PSO)Multi-Group Particle Swarm Optimization(MG-PSO)Dodge Strategy
相關次數:
  • 被引用被引用:0
  • 點閱點閱:134
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
本篇論文提出基於閃避策略的分群式粒子群最佳化演算法(MG-PSO)來改進PSO以處裡不同類型的最佳化問題。粒子群最佳化演算法(PSO)由Kennedy 和 Eberhart在1995年提出,是藉由模擬鳥類群體的行為來解決最佳化問題。
PSO在單峰問題(unimodal)上已經有很好的表現,但面對多峰的(multimodal)問題,卻有可能因為過早收斂導致落入區域最佳解。閃避策略的分群式粒子群最佳化演算法能保持粒子多樣性,防止落入區域最佳解。在分群架構與閃避策略下,能夠避免粒子群搜尋同一區域。我們使用數個測試函數來評估MG-PSO的效能,並與不同的類型的PSO比較。
In this paper, we propose a multi-group Particle Swarm Optimization (MG-PSO) with dodge strategy to improve the Particle Swarm Optimization for dealing with different types of optimization problems.
Particle Swarm Optimization (PSO) is an evolutionary algorithm introduced by Kennedy and Eberhart in 1995, which simulates social behavior of birds to solve optimization problems. The particle swarm optimizer (PSO) has exhibited good performance on unimodal problem. But on multimodal function it tends to suffer from premature convergence and fall into local optimal. Dodge strategy can maintain diversity and avoid convergence to local optimal. The MG-PSO with multi-group framework and dodge strategy is to prevent particle groups from searching the same region. In the experiments, we used some benchmarks functions to evaluate the performance of MG-PSO and compared with different PSO.
目錄
摘要_______________________________________________________I
ABSTRACT__________________________________________________II
誌謝_____________________________________________________III
目錄______________________________________________________IV
表目錄____________________________________________________VI
圖目錄___________________________________________________VII
第一章 導論_________________________________________________1
1.1研究背景______________________________________________1
1.2研究動機______________________________________________2
1.3論文架構______________________________________________3
第二章 相關研究_____________________________________________4
2.1粒子群最佳化演算法(PSO)______________________________4
2.2粒子速度更新法則_______________________________________8
2.3粒子群最佳化演算法流程圖_______________________________10
2.4基因演算法___________________________________________11
2.5粒子群最佳化演算法與基因演算法的比較____________________13
第三章 分群與閃避策略_______________________________________14
3.1分群概念_____________________________________________14
3.2分群式粒子群最佳化演算法流程圖_________________________15
3.3分群模式_____________________________________________16
3.4閃避方法概念_________________________________________17
3.5閃避方法公式_________________________________________18
3.6閃避方法模擬_________________________________________20
第四章 閃避策略的分群式粒子群最佳化演算法架構__________________31
4.1分群架構_____________________________________________31
4.2閃避策略____________________________________________33
4.3程式流程圖___________________________________________35
4.4閃避策略的分群式粒子群最佳化演算法______________________36
第五章 實驗結果____________________________________________40
第六章 結論________________________________________________57
6.1結論________________________________________________60
第七章 參考文獻____________________________________________62
參考文獻
[1] H.-M. Chen, B.-F. Liu, H.-L. Huang, S.-F. Hwang, and S.-Y. Ho*, "SODOCK: Swarm Optimization for Highly Flexible Protein-Ligand Docking," Journal of Computational Chemistry, vol. 28, pp. 612-623, 2007.

[2] Dong-Wook Lee, Chang-Bong Ban, Kwee-Bo Sim, Ho-Sik Seok, Kwang-Ju Lee, Byoung-Tak Zhang, "Behavior evolution of autonomous mobile robot using genetic programming based on evolvable hardware, " Systems, Man, and Cybernetics, 2000 IEEE International Conference on , Volume 5, 8-11 Oct. 2000 Page(s):3835 - 3840 vol.5

[3] S.-Y. Ho*, S.-J. Ho, Y.-K. Lin, and C.-C. Chu, "An Orthogonal Simulated Annealing Algorithm for Large Floorplanning Problems," IEEE Trans. VLSI systems, vol. 12, no. 8, pp. 874-876, Aug. 2004.

[4] S.-Y. Ho, H.-S. Lin, W.-H. Liauh and S.-J. Ho, "OPSO: Orthogonal Particle Swarm Optimization and Its Application to Task Assignment Problems," accepted by IEEE Trans. Systems, Man, and Cybernetics -Part A, Systems and Humans, 38 (2), pp. 288-298, MAR 2008.

[5] Axelsson. J, Menth. S, Semmler.K., "Genetic algorithms in industrial design, " Tools with Artificial Intelligence, 1993. TAI '93. Proceedings., Fifth International Conference on, 8-11 Nov. 1993 Page(s):64 – 67

[6] J. Kennedy and R. Eberhart, "Particle swarm optimization," in 1995, pp. 1942-1948 vol.4.

[7] S. Kirkpatrick , C. D. Gelatt Jr. , and M. P. Vecchi , "Optimization by Simulated Annealing," Science 13 May 1983:, Vol. 220. no. 4598, pp. 671 – 680
[8] R. Eberhart and J. Kennedy, "A new optimizer using particle swarm theory," in 1995, pp. 39-43.

[9] Y.Shi and R.Eberhart, "A modified Particle swarm optimazation, " in
Proc. of IEEE International Conference in Evolutionary
Computation,pp.69-72,May 1998.

[10] R. C. Eberhart and Y. Shi, "Comparing inertia weights and constriction factors in particle swarm optimization, " in Proc. Congress on Evolutionary Computation , pp. 84–88, 2000.

[11]D. E. Goldberg, "Genetic Algorithm in Search Optimization and
Machine Learning. " MA: Addison-Wisley, 1989.

[12]R. C. Eberhart and Y. Shi "Comparison between genetic algorithms and particle swarm optimization, " Evolutionary Programming VII: The 7th Ann. Conf. on Evolutionary Programming, pp.181-184, 1998.

[13] J. J. Liang and P. N. Suganthan, "Dynamic multi-swarm particle swarm optimizer." in 2005, pp. 124-129.

[14] 李宗澤 (民96)。 往復搜尋式粒子群體最佳化演算法。 國
立海洋大學碩士論文,未出版,基隆市。

[15] K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, "A fast and
elitist multiobjective genetic algorithm: NSGA-II." Evolutionary
Computation, IEEE Transactions on, vol. 6, pp. 182-197, 2002.

[16]Chen, Y.-p., Peng, W.-C., & Jian, M.-c. "Particle swarm optimization with recombination and dynamic linkage discovery. "IEEE Transactions on Systems, Man, and Cybernetics–Part B: Cybernetics, 37(6), 1460–1470,2007.

[17]Ballester, P.J., Carter, J.N.: An effective real-parameter genetic algorithm with parent centric normal crossover for multimodal optimization. In Deb, K., et al., eds.: Lecture Notes in Computer Science 3102, Springer (2004)901 913

[18] K. V. Price, "Differential evolution vs. the functions of the 2nd ICEO", Proc. of 1997 IEEE International Conference on Evolutionary Computation, pp. 153-157, Indianapolis, IN, USA, April 1997.

[19]N. Hansen and S. Kern. Evaluating the CMA evolution strategy on multimodal test functions. In Xin Yao et al., editors, Parallel Problem Solving from Nature – PPSN VIII, LNCS 3242, pages 282-291. Springer, 2004.

[20] S.-Y. Ho, L.-S. Shu, and J.-H. Chen, "Intelligent Evolutionary Algorithms for Large Parameter Optimization Problems," IEEE Trans. Evolutionary Computation, vol. 8, no. 6, pp. 522-541, Dec. 2004.

[21] Y. W. Leung and Y. P. Wang, "An orthogonal genetic algorithm with quantization for global numerical optimization, " IEEE Trans. Evol. Comput., vol. 5, pp. 41–53, Feb. 2001.

[22] M. Srinivas and L. M. Patnaik, "Adaptive probabilities of crossover and mutation in genetic algorithms, " IEEE Trans. Systems, Man and Cybernetics, vol. 24, pp. 656-667, April 1994.

[23] M. Clerc and J. Kennedy, "The particle swarm: Explosion, stability, and convergence in a multi-dimensional complex space, " IEEE Trans. Evol. Comput., vol. 6, pp. 58–73, Feb. 2002.

[25] T. M. Blackwell and J. Branke, "Multi-swarm optimization in dynamic environments". LNCS No. 3005: Proceedings of Applications of Evolutionary Computing: EvoWorkshops 2004: EvoBIO, EvoCOMNET, EvoHOT, EvoISAP, EvoMUSART, and EvoSTOC, Coimbra, Portugal. pp. 489-500, 2004

[26]M. Lovbjerg, T. K. Rasmussen and T. Krink, "Hybrid
particle swarm optimizer with breeding and subpopulations ". Proc. of the Genetic and Evolutionary Computation Conference, (GECCO
2001), 2001
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔