| 基于结晶过程的分子动理论优化算法 |
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| 引用本文： | 易灵芝,朱彪明,范朝冬,任柯,李杰,肖乐意. 基于结晶过程的分子动理论优化算法[J]. 计算机工程与科学, 2017, 39(9): 1774-1780 |
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| 作者姓名： | 易灵芝 朱彪明 范朝冬 任柯 李杰 肖乐意 |
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| 作者单位： | (1.智能计算与信息处理教育部重点实验室（湘潭大学）,湖南 湘潭 411105；2.湖南省“风电装备与电能变化”2011.协同创新中心,湖南 湘潭 411105；3.湘潭大学艺术学院,湖南 湘潭 411105) |
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| 基金项目： | 国家自然科学基金(61572416,61573299)；湖南省自然科学基金（2016JJ3125）;湖南省研究生科研创新项目（CX2017B339）;湖南省教育厅科学研究项目(15C1327)；湘潭大学科研项目(11KZ|KZ03045)；湘潭大学博士科研项目(11KZ|KZ08062) |
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| 摘 要： | 针对分子动理论优化算法(KMTOA)存在易陷入局部最优、寻优精度低等问题,提出一种基于结晶过程的分子动理论优化算法(C-KMTOA)。该算法通过模拟结晶过程设计了一种分离算子,该算子将种群分为最优个体、优秀个体、较差个体三个子群,并通过引导操作使较差个体向优秀个体附近移动、优秀个体向最优个体附近移动,从而使搜索范围快速缩小到最优解附近。实验结果表明,该算法在优化精度、动态性能等方面均优于GA、DE、QPSO和KMTOA。
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| 关 键 词： | 函数优化 分子动理论优化算法 最优解 分离算子 |
| 收稿时间： | 2016-01-28 |
| 修稿时间： | 2017-09-25 |
A kinetic-molecular theory optimization algorithmbased on crystallization process |
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| YI Ling-zhi,ZHU Biao-ming,FAN Chao-dong,REN Ke,LI Jie,XIAO Le-yi. A kinetic-molecular theory optimization algorithmbased on crystallization process[J]. Computer Engineering & Science, 2017, 39(9): 1774-1780 |
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| Authors: | YI Ling-zhi ZHU Biao-ming FAN Chao-dong REN Ke LI Jie XIAO Le-yi |
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| Affiliation: | (1.Key Laboratory of Intelligent Computing & Information Processing (Xiangtan University),Ministry of Education,Xiangtan 411105；2.Wind Power Equipment and Power Conversion 2011 Collaborative Innovation Center,Xiangtan 411105;3.School of Art,Xiangtan University,Xiangtan 411105,China） |
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| Abstract: | We propose a kinetic-molecular theory optimization algorithm based on crystallization process (C-KMTOA) to solve the problem that the KMTOA is easily stuck into local optimal and low accuracy. We also design a separation operator by simulating the crystallization process, which divides the population into three subgroups: the best individuals, the excellent individuals and the worst individuals. In addition, with the help of guiding operation, the worst individuals can move toward the excellent individuals and the excellent individuals move toward the best individuals, so that the search range is narrowed down to the optimal solution quickly. Experimental results show that the proposed algorithm is superior to the GA, DE, QPSO, and KMTOA algorithms in terms of optimization precision and dynamic performance. |
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