| 求解非线性约束优化问题改进的粒子群算法 |
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| 引用本文： | 李从信,马良乾,俞莲莲.求解非线性约束优化问题改进的粒子群算法[J].佳木斯大学学报,2006,24(3):340-342. |
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| 作者姓名： | 李从信 马良乾 俞莲莲 |
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| 作者单位： | 大庆石油学院计算机科学与信息技术学院 黑龙江大庆163318 |
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| 摘 要： | 采用粒子群算法处理约束优化问题时,由于约束条件使得解空间成为非凸集合,粒子容易陷入局部最优,因此在搜索过程的不同阶段,提出变步长因子的粒子群算法,实验证明改进的算法是可行的,且在精度与稳定性上明显优于采用罚函数的粒子群算法和遗传算法等其它一些算法.
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| 关 键 词： | 粒子群算法 动态罚函数 变步长因子 |
| 文章编号： | 1008-1402（2006）03-0340-03 |
| 收稿时间： | 2006-04-27 |
| 修稿时间： | 2006年4月27日 |
An Improved Particle Swarm Optimization to Settle Constrained Optimal Nonlinear Problem |
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| LI Gong-xin,MA Liang-qian,YU Lian-lian.An Improved Particle Swarm Optimization to Settle Constrained Optimal Nonlinear Problem[J].Journal of Jiamusi University(Natural Science Edition),2006,24(3):340-342. |
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| Authors: | LI Gong-xin MA Liang-qian YU Lian-lian |
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| Institution: | Computer and Information, Technology College,Daqing Petroleum Institute,Daqing 163318, China |
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| Abstract: | In this paper,the particle swarm optimaziton handles are used to deal with constraint optimal problems.Owing to constraint conditions,searching space is not bulgy and particles are easy to be limited to local optimal.Therefore,we advance to the PSO of searching different scale gene in different phases during the searching process.Numerical results show that the improved PSO is feasible and can get more precise results than particle swarm optimization by using penalty functions and genetic algorithm and other optimization algorithms. |
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| Keywords: | particle swarm algorithm dynamic penalty function scale gene |
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