Discrete particle swarm optimization based on estimation of distribution for polygonal approximation problems
The polygonal approximation is an important topic in the area of pattern recognition, computer graphics and computer vision. This paper presents a novel discrete particle swarm optimization algorithm based on estimation of distribution (DPSO-EDA), for two types of polygonal approximation problems. E...
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| Published in | Expert systems with applications Vol. 36; no. 5; pp. 9398 - 9408 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Ltd
01.07.2009
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| Abstract | The polygonal approximation is an important topic in the area of pattern recognition, computer graphics and computer vision. This paper presents a novel discrete particle swarm optimization algorithm based on estimation of distribution (DPSO-EDA), for two types of polygonal approximation problems. Estimation of distribution algorithms sample new solutions from a probability model which characterizes the distribution of promising solutions in the search space at each generation. The DPSO-EDA incorporates the global statistical information collected from local best solution of all particles into the particle swarm optimization and therefore each particle has comprehensive learning and search ability. Further, constraint handling methods based on the split-and-merge local search is introduced to satisfy the constraints of the two types of problems. Simulation results on several benchmark problems show that the DPSO-EDA is better than previous methods such as genetic algorithm, tabu search, particle swarm optimization, and ant colony optimization. |
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| AbstractList | The polygonal approximation is an important topic in the area of pattern recognition, computer graphics and computer vision. This paper presents a novel discrete particle swarm optimization algorithm based on estimation of distribution (DPSO-EDA), for two types of polygonal approximation problems. Estimation of distribution algorithms sample new solutions from a probability model which characterizes the distribution of promising solutions in the search space at each generation. The DPSO-EDA incorporates the global statistical information collected from local best solution of all particles into the particle swarm optimization and therefore each particle has comprehensive learning and search ability. Further, constraint handling methods based on the split-and-merge local search is introduced to satisfy the constraints of the two types of problems. Simulation results on several benchmark problems show that the DPSO-EDA is better than previous methods such as genetic algorithm, tabu search, particle swarm optimization, and ant colony optimization. |
| Author | Wang, Jiahai Zhou, Yalan Kuang, Zhanghui Xu, Xinshun |
| Author_xml | – sequence: 1 givenname: Jiahai surname: Wang fullname: Wang, Jiahai email: wjiahai@hotmail.com organization: Department of Computer Science, Sun Yat-sen University, No. 135, Xingang West Road, Guangzhou 510275, China – sequence: 2 givenname: Zhanghui surname: Kuang fullname: Kuang, Zhanghui organization: Department of Computer Science, Sun Yat-sen University, No. 135, Xingang West Road, Guangzhou 510275, China – sequence: 3 givenname: Xinshun surname: Xu fullname: Xu, Xinshun organization: School of Computer Science and Technology, Shandong University Middle of Shunhua Road, Jinan, Shandong 250101, China – sequence: 4 givenname: Yalan surname: Zhou fullname: Zhou, Yalan organization: Information Science School, Guangdong University of Business Studies, No. 21, Chisha Road, Guangzhou 510320, China |
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| CitedBy_id | crossref_primary_10_1016_j_eswa_2010_12_124 crossref_primary_10_1007_s00500_011_0780_6 crossref_primary_10_1109_TCYB_2015_2424836 crossref_primary_10_1016_j_jvcir_2014_09_012 crossref_primary_10_1016_j_ins_2013_05_031 crossref_primary_10_1016_j_gmod_2016_01_004 crossref_primary_10_1016_j_eswa_2011_07_039 crossref_primary_10_1016_j_patrec_2012_02_015 |
| Cites_doi | 10.1016/0167-8655(94)00081-D 10.1109/21.293514 10.1109/CEC.2004.1330874 10.1016/S0031-3203(98)00173-3 10.1134/S105466180602009X 10.1016/j.jvcir.2003.12.001 10.1142/S0218001499000598 10.1016/S0167-8655(98)00082-8 10.1007/s11047-007-9050-z 10.1109/ICNN.1995.488968 10.1109/4235.797971 10.1142/S0219467804001385 10.1109/34.31447 10.1007/s11047-007-9049-5 10.1016/S0031-3203(00)00159-X 10.1142/S0218001400000209 10.1142/S0218001400000167 10.1016/0031-3203(92)90126-4 10.1109/ICNC.2007.18 10.1023/A:1013500812258 10.1016/S0734-189X(84)80023-7 10.1016/S0031-3203(02)00321-7 10.1016/0031-3203(93)90106-7 |
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