| 动态环境中具有记忆和搜索能力的微粒群算法 |
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| 引用本文： | 胡静,谭瑛. 动态环境中具有记忆和搜索能力的微粒群算法[J]. 太原科技大学学报, 2010, 31(1): 29-33 |
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| 作者姓名： | 胡静 谭瑛 |
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| 作者单位： | 太原科技大学计算机科学与技术学院,太原,030024 |
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| 基金项目： | 国家自然科学基金，太原科技大学青年基金 |
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| 摘 要： | 提出了响应环境变化的充分条件——子种群多样性和环境变化前后全局最好解的距离,分析了提出响应依据的原因、必要性及响应依据之间、响应依据和重设之间的关系。使用"记忆"微粒保留环境变化后的有用信息,同时使用"搜索"微粒替换"过时"的微粒,替换的过程即增加种群多样性又充分地利用了之前进化的信息。最后,将其应用于线性变化和随机变化的抛物线函数中,并与Eberhart-PSO和APSO进行了对比,结果表明了该算法的有效性。
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| 关 键 词： | 微粒群算法 动态环境 检测 响应 记忆 搜索 |
Memory and Exploration for Particle Swarm Optimization in Dynamic Environments |
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| HU Jing,TAN Ying. Memory and Exploration for Particle Swarm Optimization in Dynamic Environments[J]. Journal of Taiyuan University of Science and Technology, 2010, 31(1): 29-33 |
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| Authors: | HU Jing TAN Ying |
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| Affiliation: | HU Jing,TAN Ying(School of Computer Science , Technology,Taiyuan University ofScience , Technology,Taiyuan 030024,China) |
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| Abstract: | The sufficient conditions of response including and are designed.The reasons as well as necessity of putting forward response conditions and relation with reset are analyzed.The memory particles are used to restore used information,and the exploration particles replace the past particles,which both increases the diversity of population and sufficiently utilizes the used information.Then,the parabolic function with random variation and linear variation is used to test,which is compared with Eberhart-PSO and ... |
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| Keywords: | particle swarm optimization dynamic environments detection response memory exploration |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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