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| PSO的向量整体修订策略和局部跳出策略 | |
| 引用本文： | 窦全胜,潘冠宇,刘岩,周春光,史忠植. PSO的向量整体修订策略和局部跳出策略[J]. 吉林大学学报(工学版), 2012, 42(2): 429-433 |
| 作者姓名： | 窦全胜 潘冠宇 刘岩 周春光 史忠植 |
| 作者单位： | 1. 山东工商学院计算机科学与技术学院,山东烟台264005／中国科学院计算技术研究所,北京100080 2. 吉林大学数学学院,长春,130012 3. 山东工商学院计算机科学与技术学院,山东烟台,264005 4. 吉林大学计算机科学与技术学院,长春,130012 5. 中国科学院计算技术研究所,北京,100080 |
| 基金项目： | 国家自然科学基金项目,“973”国家发展规划项目,山东省中青年科学家奖励基金 |
| 摘 要： | 针对传统PSO方法对CEC2005(The 2005 IEEE Congress on evolutionary computation)中的25个benchmark函数搜索效果较差的问题,提出了"向量整体修订"和"局部跳出"两种改进策略。改变PSO方法中粒子在每一维上的修订相互独立的传统机制,按某一概率将粒子作为整体进行修正,当群体最优长时间不变或变化值小于一定阈值时,为跳出局部最优,按某一概率重新定义群体最优或初始化群体。通过实验证明了改进后的PSO方法对CEC2005中的测试问题的有效性。 |
| 关 键 词： | 计算机应用 粒子群优化 收敛 向量修订 局部跳出 |
Vector correction and jump out of local optimum strategy for PSO |
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| DOU Quan-sheng,PAN Guan-yu,LIU Yan,ZHOU Chun-guang,SHI Zhong-zhi. Vector correction and jump out of local optimum strategy for PSO[J]. Journal of Jilin University:Eng and Technol Ed, 2012, 42(2): 429-433 | |
| Authors: | DOU Quan-sheng PAN Guan-yu LIU Yan ZHOU Chun-guang SHI Zhong-zhi |
| Affiliation: | 1.School of Computer Science and Technology,Shandong Institute of Business and Technology,Yantai 264005,China;2.Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100080,China;3.College of Mathematics,Jilin University,Changchun 130012,China;4.College of Computer Science and Technology,Jilin University,Changchun 130012,China) |
| Abstract: | Using traditional Particle Swarm Optimization(PSO) the searching results for some new benchmark functions,e.g.the 25 benchmark functions in CEC2005,are not satisfactory.An improved version of PSO was designed to suit for new benchmark functions in CEC2005.Two improvement strategies,named Vector correction strategy and Jump out of local optimum strategy,were employed in this improved PSO.When the swarm optimum remains invariable for a long time,The improve PSO can revises the whole particle vector and re-initialize the swarm or generate a new swarm optimum according to certain probability.The improved PSO was tested by the 25 benchmark functions in CEC2005,and the experimental results show that the search efficiency and the ability to jump out of the local optimum of the improved PSO are significantly improved. |
| Keywords: | computer application partical swarm optimization convergence vector correction jumpout of local optimum |
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