| 权重自适应调整的混沌量子粒子群优化 |
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| 引用本文： | 程伟,陈森发.权重自适应调整的混沌量子粒子群优化[J].计算机工程与应用,2010,46(9):46-48. |
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| 作者姓名： | 程伟 陈森发 |
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| 作者单位： | 东南大学,经济与管理学院,系统工程研究所,南京,210096 |
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| 基金项目： | 教育部博士点基金资助项目(No.20060286005) |
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| 摘 要： | 针对量子粒子群优化算法在处理高维复杂函数收敛速度慢、易陷入局优的问题，利用混沌算子的遍历性提出了基于惯性权重自适应调整的混沌量子粒子群优化算法。该算法在运行过程中根据粒子适应值的优劣情况，相应采取不同的惯性权重策略，以调节粒子的全局搜索和局部搜索能力。对几个典型函数的测试结果表明，该算法在收敛速度和精度上有大幅度的提高，且有很强的避免陷入局优的能力，性能远远优于一般的粒子群算法和量子粒子群算法。
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| 关 键 词： | 群体智能 粒子群优化 量子粒子群优化 惯性权重自适应调整 |
| 收稿时间： | 2009-4-27 |
| 修稿时间： | 2009-8-17
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QPSO with self-adapting adjustment of inertia weight |
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| CHENG Wei,CHEN Sen-fa.QPSO with self-adapting adjustment of inertia weight[J].Computer Engineering and Applications,2010,46(9):46-48. |
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| Authors: | CHENG Wei CHEN Sen-fa |
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| Affiliation: | CHENG Wei,CHEN Sen-fa School of Economy , Management,Southeast University,Nanjing 210096,China |
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| Abstract: | A novel algorithm is presented on the base of quantum behaved particle swarm optimization,which is aimed at resolving the problem of slow convergence rate in optimizing higher dimensional sophisticated functions and being trapped into local minima easily.Chaos algorithm is incorporated to traverse the whole solution space;besides a new strategy of self adapting adjustment of inertia weight according to the current particles'fitness is combined also to balance the capability of local search and global search... |
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| Keywords: | swarm intelligence particle swarm optimization quantum-behaved particle swarm optimization self-adapting adjustment of inertia weight |
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