| 基于MQPSO优化的属性约简方法 |
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| 引用本文： | 谷晓博,张凌波,顾幸生,徐震浩.基于MQPSO优化的属性约简方法[J].华东理工大学学报(自然科学版),2012,38(1):84-88. |
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| 作者姓名： | 谷晓博 张凌波 顾幸生 徐震浩 |
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| 作者单位： | 华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海,200237 |
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| 基金项目： | 国家863高技术研究发展计划项目，中央高校基本科研业务费专项资金，教育部博士点基金项目，上海市重点学科项目，上海市自然科学基金项目 |
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| 摘 要： | 变异量子粒子群算法(MQPSO)通过在量子粒子群算法(QPSO)中引入变异机制,增加了全局搜索能力,避免陷入局部最优。在粗糙集理论和MQPSO算法基础上,提出了基于MQPSO优化的决策表属性约简方法,并在算法实现中提出了迭代记录策略,改进了算法中的耗时计算部分,降低了算法的时间复杂度。
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| 关 键 词： | 粗糙集 属性约简 MQPSO |
Attribute Reduction Algorithm Based on MQPSO Optimization |
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| GU Xiao-bo,ZHANG Ling-bo,GU Xing-sheng,XU Zhen-hao.Attribute Reduction Algorithm Based on MQPSO Optimization[J].Journal of East China University of Science and Technology,2012,38(1):84-88. |
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| Authors: | GU Xiao-bo ZHANG Ling-bo GU Xing-sheng XU Zhen-hao |
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| Institution: | (Key Laboratory of Advanced Control and Optimization for Chemical Processes,Ministry of Education,East China University of Science and Technology,Shanghai 200237,China) |
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| Abstract: | A quantum particle swarm optimization algorithm with mutation operator(MQPSO) can improve the global search capability to avoid local optimum by introducing the mutation mechanism into the quantum particle swarm optimization(QPSO).In this paper,an attribute reduction method based on MQPSO optimization is proposed,and a strategy of iterative record is given to modify the time-consuming part of the algorithm such that the time complexity can be reduced. |
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| Keywords: | rough set attribute reduction MQPSO |
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