混合式區間搜索粒子群演算法

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馬冠群(Guan-Chun Ma) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

混合式區間搜索粒子群演算法
(A Particle Swarm Optimization With Hybrid Interval Search)

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摘要(中)

傳統的粒子群演算法的學習策略常被認為是讓粒子失去多樣性的原因，也因為多樣性的喪失而引發過早的收斂。因此本論文中提出了兩個方法，來改善上述提到的問題。第一個方法是「單維度模仿」(Single Dimension Imitation, SDI)，此方法是用來提高粒子的收斂速度以及結果的準確度，並且將它運用在傳統的粒子群演算法上。第二個方法是「區間搜索」(Interval Search, IS)，此方法是用來提高粒子群的多樣性，接著再提出「混合經驗解」(Hybrid Experience,H_(i,best) )來增強IS結果的準確性。接著我們做一些實驗，來證明這兩個方法的可行性。然後我們發現了在處理比較簡單的問題時第一個方法比第二個方法優秀，而在處理比較複雜的問題時第二個方法比第一個方法優秀，因此我們決定將這兩個方法結合在一起，形成一個新的演算法。最後我們將使用一些測試函數與其它已提出的演算法進行比較。從實驗結果得知，本論文提出的新的改良方法，不論在結果還是在速度上面，都比其他演算法還要優秀許多。

摘要(英)

The learning strategy in the canonical particle swarm optimization (PSO) algorithm is generally considered to be the reasons for the loss of diversity, and this leads to premature convergence. In this thesis, two methods are proposed to improve the above mentioned problems. The first method is called single dimension imitation. This method is used to improve the convergence speed of the particles and the accuracy of the results, and it is used in the canonical particle swarm optimization (PSO) algorithm. The second method is the Interval Search, IS, which is utilized to improve the diversity of the particle swarm. In order to enhance the accuracy of the results of IS, we propose a new strategy which called Hybrid Experience,H_(i,best), and add it to the IS. Since we found that the first method was better at dealing with simpler problems than the second, while the second method was superior to the first in dealing with more complex problems, so we decided to combine the two methods to form a new algorithm. Finally, we will use some test functions to compare with other proposed algorithms. According to the experimental results, the new improved method proposed in this thesis is much better than other algorithms in both the results and the speed.

關鍵字(中)

★ 粒子群演算法
★ 區間搜索

關鍵字(英)

論文目次

摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VI
表目錄 X
第一章緒論 1
1.1研究動機 1
1.2論文架構 3
第二章粒子群演算法 4
2.1 人工智能演化最佳化方法 4
2.2 原始粒子群演算法介紹 5
2.3 原始粒子群演算法的基本模式與公式 5
2.4 加入慣性權重的標準粒子群演算法(SPSO) 6
第三章單維度模仿暨區間搜索法 10
3.1 引言 10
3.2單維度模仿策略 10
3.3 新粒子移動策略 21
3.3.1 粒子群移動搜索策略 21
3.3.2 混合經驗解 HI,BEST 26
3.4 結果比較及分析 31
3.5 混合式區間搜索粒子群演算法 39
第四章實驗結果 44
4.1 模擬實驗結果 44
4.1.1 測試函數在10維下的結果 44
4.1.2 測試函數在30維下的結果 61
4.1.3 演算法時間比較 78
4.2 ISH-PSO-SDI與其它知名的演算法比較 81
第五章總結與未來展望 96
5.1 總結 96
5.2 未來展望 96
參考文獻 97

參考文獻

J. Kennedy and R. C. Eberhart, “Particle Swarm Optimization,” In Proceedings of IEEE International Conference on Neural Networks,” Vol. IV, pp. 1942−1948, 1995.
W. D. Chang and S. P. Shih, “PID controller design nonlinear systems using an improved particle swarm optimization approach,” Communication Nonlinear Science and Numerical Simulation, Vol. 15, pp. 3632-3639, 2010.
R. A. Krohling and L. S. Coelho, “Coevolutionary Particle Swarm Optimization Using Gaussian Distribution for Solving Constrained Optimization Problem,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics , Vol. 36, No. 6, pp. 1407-1416, 2006.
G. Zeng and Y. Jiang, “A Modified PSO Algorithm with Line Search,” In Proceedings of 2010 International Conference on Computational Intelligence and Software Engineering, pp. 1-4, 2010.
Y. Shi and R. C. Eberhart, “Particle Swarm Optimization：Development, Applications and Resource,” In Proceedings of the 2001 Congress on Evolutionary Computation, Vol. 1, pp. 81-86, 2001.
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,” IEEE Transactions on Man and Cybernetics, Part A: Systems and Humans, Vol. 38, No. 2, pp. 288-298, 2008.
H. Babaee and A. Khosravi, “An Improve PSO Based Hybrid Algorithms,” In Proceedings of 2011 International Conference on Management and Service Science, pp. 1-5, 2011.
Y. Shi and R. C. Eberhart, “Empirical Study of Particle Swarm Optimization,” In Proceedings of the 1999 Congress on Evolutionary Computation, Vol. 3, pp. 1945-1950, 1999.
M. Clerc and J. Kennedy, “The Particle Swarm—Explosion, Stability, and Convergence in a Multidimensional Complex Space,” IEEE Transactions on Evolutionary Computation, Vol. 6, No. 1, pp. 58-73, 2002.
M. Clerc, “The Swarm and the Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization,” In Proceedings of the Congress on Evolutionary Computation, Vol. 3, pp. 1951−1957, 1999.
N. M. Kwok, D. K. Liu, K. C. Tan, and Q. P. Ha, “An Empirical Study on the Settings of Control Coefficients in Particle Swarm Optimization,” In Proceedings of IEEE Congress on Evolutionary Computation, pp. 823-830, 2006.
Y. T. Juang, S. L. Tung and H. C. Chiu, “Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions,” International Journal of Information Sciences, Vol. 181, pp. 4539-4549, 2011.
A. M. Elbar, S. Abdelshahid, and D. C. Wunsch, “Fuzzy PSO: a generalization of particle swarm optimization,” 2005 IEEE International Neural Networks, Vol. 2, pp.1086–1091, 2005.
J. Wei, L. Guangbin and K. Dong, “Elite Particle Swarm Optimization with Mutation,” 2008 Asia Simulation Conference-7^th International Conference on System Simulation and Scientific Computing, pp.800-803,2008.
W. －P. Lee, W. －Y. Xian and C. －C. Wen, ‘Research on a Modified Particle Swarm Optimization Algorithm,” Journal of Engineering Technology, vol.4,no.2, pp.51-62,2008.
林柏勳,胡光復,沈哲緯,鄭錦桐,「最佳化方法於工程上之應用」，中興工程季刊第103期，2009年4月。
陳鍾誠 (2010年08月23日)，(網頁標題) 人工智慧 — 最佳化方法，(網站標題) 陳鍾誠的網站，取自 http://ccckmit.wikidot.com/so:introduction ，網頁修改第 1 版。
吳讚展，「自調整非線性慣性權重粒子群演算法」，國立中央大學，碩士論文，民國101年。
Y. Shi, and R. C. Eberhart, “Evolutionary Programming VII, Parameter Selection in Particle Swarm Optimization,” Springer Berlin Heidelberg, Vol. 1447, pp. 591–600, 1998.
Y. Shi and R. C. Eberhart, “Empirical Study of Particle Swarm Optimization,” Proceedings of the 1999 Congress on Evolutionary Computation, Vol. 3, pp. 1945-1950, 1999.
I. C. Trelea, “The particle swarm optimization algorithm: convergence analysis and parameter selection,” Elsevier Science B.V., Vol. 85, pp. 317-325, 2003.
J. Kennedy, R. C. Eberhart, and Y. Shi, “Swarm intelligence,” Morgan Kaufmann Publishers, San Francisco, 2001.
W. H. Ip, D. Wang, and V. Cho, “Aircraft ground service scheduling problems and their genetic algorithm with hybrid assignment and sequence encoding scheme,” IEEE Systems Journal, Vol. 7, No. 4, 2013.
C. Liu and C. Ouyang, “An adaptive fuzzy weight PSO algorithm,” 2010 Fourth International Conference on Genetic and Evolutionary Computing, pp. 8, 10, 13-15, 2010.
董聖龍，「粒子群演算法於二階時變系統穩定分析與穩定化設計」，國立中央大學，博士論文，民國100年。
M. Dorigo, V. Maniezzo and A. Colorni, “Ant system: Optimization by a colony of cooperating agents,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 26, No. 1, pp. 29, 41, 1996.
J. Kennedy and W. M. Spears, “Matching algorithms to problems: An experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator,” The 1998 IEEE International Conference on Evolutionary Computation Proceedings, pp. 78-83, 1988.
王文俊，「認識fuzzy」，全華出版，第三版，2007。
TRAN Dang Cong and WU Zhijian, “Adaptive Multi-layer Particle Swarm Optimization with Neighborhood Search,” Chinese Journal of Electronics, Vol.25, No.6, Nov. 2016
M. Pant, T. Radha, and V. P. Singh, “A new particle swarm optimization with quadratic interpolation,” Proceedings of IEEE International Conference on Computational Intelligence and Multimedia Applications, 13-15 December 2007, Sivakasi, Tamil Nadu, pp. 55-60.
J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 3, pp. 281-296, 2006.
N. Iwasaki, K. Yasuda, and G. Ueno, “Dynamic parameter tuning of particle swarm optimization,” IEEJ Transactions on Electrical and Electronic Engineering, vol. 1, no. 4, pp. 353-363, 2006.
M. A. Montes de Oca, J. Pena, T. Stutzle, C. Pinciroli, and M. Dorigo, “Heterogeneous particle swarm optimizers,” Proceedings of IEEE congress on Evolutionary Computation, 18-21 May 2009, Trondheim , pp. 698-705.
X. Yao, Y. Liu, and G. Lin, “Evolutionary programming made faster,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, pp. 82-102, 1999.
J. J. Liang, P. N. Suganthan, and K. Deb, “Novel composition test functions for numerical global optimization,” Proceedings of IEEE on Swarm Intelligence Symposium, 8-10 June 2005, pp. 68-75.
P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y.-P. Chen, A. Auger, and S. Tiwari, “Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization,” Technical report, Nanyang Technological University, Singapore, 2005.
R. Salomon, “Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions,” BioSystems, vol. 39, no. 3, pp. 263-278, 1996.
M. Pant, T. Radha and V. P. Singh, “A New Particle Swarm Optimization with Quadratic Interpolation,” International Conference on Computational Intelligence and Multimedia Applications, pp. 55-60, 2007.
K. E. Parsopoulos and M. N. Vrahatis, “UPSO: a unified particle swarm optimization scheme,” In Lecture series on Computer and Computational Sciences, Vol. 1, pp. 868-873, 2004.
R. Mendes, J. Kennedy and J. Neves, “The fully informed particle swarm：simpler, maybe better,” IEEE Transactions on Evolutionary Computation, Vol. 8, pp. 204-210, 2004.
J. J. Liang and P. N. Suganthan, “Dynamic multi-swarm particle swarm optimizer,” In Proceedings of IEEE on Swarm Intelligence Symposium, pp. 124-129, 2005.
J. Hu, J. Zeng, Y. Yang, “A two-order particle swarm optimization model and the selection of its parameters,” The Sixth World Congress on Intelligent Control and Automation, pp. 3440, 3445, 2006.
陳珈妤，「快速平衡粒子群最佳化方法」，國立中央大學，碩士論文，民國100年。
蔡憲文，「以時變學習因子策略改良粒子群演算法」，國立中央大學，碩士論文，民國99年。
A. Chatterjee and P. Siarry, “Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization,” Computers and Operations Research, Vol. 33, No. 3, pp. 859-871, 2004.
李憲昌，「維度經驗重心分享粒子群演算法」，國立中央大學，碩士論文，民國102年。
顏淯翔，「改良式粒子群方法之影像追蹤系統應用」，國立中央大學，碩士論文，民國103年。
王鈺潔，「自適應解分享粒子群演算法及其在螺旋電感最佳化設計之應用」，桃園市：國立中央大學，碩士論文，民國104年。
張伯墉，「適應性自我學習粒子群演算法」，桃園市：國立中央大學，碩士論文，民國105年。
曾柏憲，「切換式自我學習粒子群演算法」，桃園市：國立中央大學，碩士論文，民國106年。
J. Liang, A. Qin, P. Suganthan and S. Baskarr, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions”, IEEE Transactions on Evolutionary Computation, Vol.10, No.3, pp.281–295, 2006.
M. Nasir and S. Das, “A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization”, Information Sciences, Vol.209, pp.16–36, 2012
H. Wang, S. Sun, C. Li, S. Rahnamayan and J. Pan, “Diversity enhanced particle swarm optimization with neighborhood search”, Information Sciences, Vol.223, pp.119–135, 2013.
L. Wang, B. Yang and Y. Chen, “Improving particle swarm optimization using multi-layer searching strategy”, Information Sciences, Vol.274, pp.70–94, 2014.
T. D. Cong and WU Zhijian, ” Adaptive Multi-layer Particle Swarm Optimization with Neighborhood Search”, Chinese Journal of Electronics,Vol.25,pp. 1079 – 1088,2016
J. Brest, S. Greiner, B. Boskovic, M. Mernik and V. Zumer, “Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems”, IEEE Transactions on Evolutionary Computation, Vol.10, No.6, pp.646–657, 2006.
J. Kennedy, “Bare bones particle swarms,” in Proc. IEEE Swarm Intell. Symp., 2003, pp. 80–87.
T. Blackwell, “A study of collapse in bare bones particle swarm optimization,” IEEE Trans. Evol. Comput., vol. 16, no. 3, pp. 354–372, Jun. 2012
T. Richer and T. Blackwell, “The Levy particle swarm,” in ´ Proc. IEEE Congr. Evol. Comput., 2006, pp. 3150–3157
H.-G. Beyer and H.-P. Schwefel, “Evolution strategies: A comprehensive introduction,” Natural Comput., vol. 1, no. 1, pp. 3–52, May 2002
H.-G. Beyer and B. Sendhoff, “Covariance matrix adaptation revisited: The CMSA evolution strategy,” in Proc. PPSN X, 2008, pp. 123–132.
H.-G. Beyer and S. Finck, “On the design of constraint covariance matrix self-adaptation evolution strategies including a cardinality constraint,” IEEE Trans. Evol. Comput., vol. 16, no. 4, pp. 578–596, Aug. 2012.
M. Campos, R. A. Krohling, and I. Enriquez,” Bare Bones Particle Swarm Optimization With Scale Matrix Adaptation” IEEE TRANSACTIONS ON CYBERNETICS, VOL. 44, NO. 9, SEPTEMBER 2014
P. Suganthan, N. Hansen, J. Liang, K. Deb, Y.-P. Chen, A. Auger, et al., “Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization,” Nanyang Technological Univ., Singapore, and KanGAL IIT Kanpur, Kanpur, India, Tech. Rep. 2005005. [Online]. Available: http://www3.ntu.edu.sg/home/EPNSugan, May 2005, pp. 1–49.
H. Wang, S. Rahnamayan, H. Sun, and M. Omran, “Gaussian bare-bones differential evolution,” IEEE Trans. Cybern., vol. 43, no. 2, pp. 634–647, Apr. 2013

指導教授

莊堯棠

審核日期

2018-7-4

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