| PSO和GA的对比及其混合算法的研究进展 |
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| 引用本文： | 封磊,蔡创,齐春,乔锃.PSO和GA的对比及其混合算法的研究进展[J].控制工程,2005(Z2). |
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| 作者姓名： | 封磊 蔡创 齐春 乔锃 |
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| 作者单位： | 西安交通大学电子信息工程学院，西安电子科技大学计算机学院，西安交通大学电子信息工程学院，西安交通大学电气工程学院 陕西西安710049，陕西工业职业技术学院信息工程系，陕西咸阳712000，陕西西安710071，陕西工业职业技术学院信息工程系，陕西咸阳712000，陕西西安710049，陕西西安710049 |
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| 摘 要： | 系统地介绍了微粒群优化算法(PSO)和遗传算法(GA)的基本原理、发展和应用的状况,比较了两者的原理特点,列举了各种微粒群优化算法和遗传算法的改进算法。介绍和总结目前出现的两种算法思想结合的局部混合与全局混合两种方式,并用图表给出了说明。分析了两种混合方式的局限性,提出对具体问题找出计算速度和计算精度的平衡点来改进算法。最后做了总结和展望,指出微粒群算法的应用需进一步拓展,和其他算法结合是提高其性能的主要方向。
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| 关 键 词： | 微粒群优化算法 遗传算法 进化算法 混合 群智能 |
Comparison Between Particle Swarm Optimization and Genetic Algorithm and Development of the Hybrid Approach |
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| FENG Lei,CAI Chuang,QI Chun,QIAO Zeng School of Electronic and Information Engineering,Xi'an Jiaotong University,Xi'an ,China, School of Computer Science and Engineering,XiDian University,Xi'an ,China, School of Electrical Engineering,Xi'anJiantong University,Xi'an ,China.Comparison Between Particle Swarm Optimization and Genetic Algorithm and Development of the Hybrid Approach[J].Control Engineering of China,2005(Z2). |
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| Authors: | FENG Lei CAI Chuang QI Chun QIAO Zeng School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China School of Computer Science and Engineering XiDian University Xi'an China School of Electrical Engineering Xi'anJiantong University Xi'an China |
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| Affiliation: | FENG Lei,CAI Chuang,QI Chun,QIAO Zeng School of Electronic and Information Engineering,Xi'an Jiaotong University,Xi'an 710049,China, School of Computer Science and Engineering,XiDian University,Xi'an 710071,China, School of Electrical Engineering,Xi'anJiantong University,Xi'an 710049,China, Department of Information Engeering,Shaanxi Polytechnic Instetute,Xian yang 712000,China |
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| Abstract: | The basic theories,development and applications of particle swarm optimization and genetic algorithm are introduced. Some models of improved PSO algorithms are outlined. Characteristics of PSO and GA are compared. Two methods of hybrid of PSO and GA at present are summarized: global combination of the two algorithms or partial combination are illustrated with flowchart. Limitation of the two hybrid methods is analysed. It is pointed out that hybrid algorithms can be improved with a balance between speed and accuracy of computation. Finally,it is pointed out application of PSO needs to be extended, and hybrid methods with other algorithms is seen as a good way to improve PSO algorithm. |
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| Keywords: | particle swarm optimization genetic algorithm evolutionary algorithm hybrid swarm intelligence |
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