| 背包问题的混合粒子群优化算法 |
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| 引用本文： | 高 尚,杨静宇.背包问题的混合粒子群优化算法[J].中国工程科学,2006,8(11):94-98. |
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| 作者姓名： | 高 尚 杨静宇 |
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| 作者单位： | 1. 江苏科技大学电子信息学院,江苏,镇江,212003;苏州大学江苏省计算机信息处理技术重点实验室,江苏,苏州,215006
2. 南京理工大学计算机科学与技术系,南京,210094 |
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| 摘 要： | 经典的粒子群是一个有效的寻找连续函数极值的方法，结合遗传算法的思想提出的混合粒子群算法来解决背包问题，经过比较测试，6种混合粒子群算法的效果都比较好，特别交叉策略A和变异策略C的混合粒子群算法是最好的且简单有效的算法，并成功地运用在投资问题中。对于目前还没有好的解法的组合优化问题，很容易地修改此算法就可解决
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| 关 键 词： | 粒子群算法 背包问题 遗传算法 变异 |
| 文章编号： | 1009-1742（2006）11-0094-05 |
| 收稿时间： | 2005-06-14 |
| 修稿时间： | 2005-07-19 |
Solving Knapsack Problem by Hybrid Particle Swarm Optimization Algorithm |
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| gaoshang and yangjingyu.Solving Knapsack Problem by Hybrid Particle Swarm Optimization Algorithm[J].Engineering Science,2006,8(11):94-98. |
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| Authors: | gaoshang and yangjingyu |
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| Abstract: | The classical particle swarm optimization is a powerful method to find the minimum of a numerical function, on a continuous definition domain. The particle swarm optimization algorithm combining with the idea of the genetic algorithm is recommended to solve knapsack problem. All the 6 hybrid particle swarm optimization algorithms are proved effective. Especially the hybrid particle swarm optimization algorithm derived from across strategy A and mutation strategy C is a simple yet effective algorithm and it has been applied successfully to investment problem. It can easily be modified for any combinatorial problem for which there has been no good specialized algorithm. |
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| Keywords: | particle swarm algorithm knapsack problem genetic algorithm mutation |
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