| 动态改变惯性权重的新模式粒子群算法 |
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| 引用本文： | 杜江,袁中华,王景芹.动态改变惯性权重的新模式粒子群算法[J].安徽大学学报(自然科学版),2018,42(2):60-66. |
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| 作者姓名： | 杜江 袁中华 王景芹 |
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| 作者单位： | 河北工业大学 电磁场与电器可靠性省部共建重点实验室,天津,300130;河北工业大学 电磁场与电器可靠性省部共建重点实验室,天津,300130;河北工业大学 电磁场与电器可靠性省部共建重点实验室,天津,300130 |
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| 基金项目： | 国家自然科学基金资助项目，河北省自然科学基金资助项目，河北省人社厅科研基金资助项目，河北省科学技术研究，发展科研基金资助项目，河北省高等学校创新团队领军人才培育计划资助项目 |
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| 摘 要： | 针对标准粒子群算法在求解复杂优化问题时易陷入局部最优、收敛精度不高和收敛成功率低的不足,提出了一种改进的粒子群算法.通过算法所处的迭代阶段和粒子的分布情况动态改变惯性权重的值,并根据每个粒子的更新情况调整其飞行的起点.最后4个测试函数仿真结果表明,在求解复杂优化问题时,改进后算法的收敛精度和收敛成功率均有明显提高.
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| 关 键 词： | 群体智能 粒子群算法 惯性权重 动态调整 新模式 |
New model of particle swarm optimization algorithm with dynamically changing inertia weight |
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| DU Jiang,YUAN Zhonghua,WANG Jingqin.New model of particle swarm optimization algorithm with dynamically changing inertia weight[J].Journal of Anhui University(Natural Sciences),2018,42(2):60-66. |
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| Authors: | DU Jiang YUAN Zhonghua WANG Jingqin |
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| Abstract: | An improved particle swarm optimization was presented to solve the problem that the standard particle swarm optimization could not complete the high accuracy and the high success rate of convergence and the standard particle swarm optimization was very easy to fall into local optimum when it solved the complex optimization problems.This paper changed inertia weight value dynamically by the iterative phase and the distribution of particles.Then it adj usted the starting point of the particle flight according to updating situation of each particle.Finally,the improved algorithm and standard algorithm were tested with four well-known benchmark functions.The experiments showed that convergence accuracy and the convergence precision were increased in solving complex optimization problems. |
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