| 基于模拟退火思想改进的粒子群算法求解背包问题 |
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| 引用本文： | 张其亮,陈永生.基于模拟退火思想改进的粒子群算法求解背包问题[J].现代电子技术,2010,33(12):85-86,89. |
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| 作者姓名： | 张其亮 陈永生 |
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| 作者单位： | 1. 同济大学,上海,201804;江苏科技大学,计算机科学与工程学院,江苏,镇江,212003
2. 同济大学,上海,201804 |
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| 摘 要： | 针对典型的背包问题，给出了一种基于粒子群算法的求解方法。考虑到粒子群算法在解决问题时容易陷入局部最优的缺点，将模拟退火（SA）思想引入到了粒子群算法中，得到了粒子群——模拟退火算法。该算法保持了粒子群算法原有的简单易实现特点，同时改善了粒子群算法易陷入局部最优的缺点。实验结果表明，该算法具有较好的求解质量。
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| 关 键 词： | 模拟退火 粒子群 背包问题 遗传算法 |
Particle Swarm Algorithm Modified Based on Ideal of Simulated Annealing for Knapsack Problem |
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| ZHANG Qi-liang,CHEN Yong-sheng.Particle Swarm Algorithm Modified Based on Ideal of Simulated Annealing for Knapsack Problem[J].Modern Electronic Technique,2010,33(12):85-86,89. |
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| Authors: | ZHANG Qi-liang CHEN Yong-sheng |
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| Affiliation: | 1. Tongji University, Shanghai 201804, China; 2. School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China) |
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| Abstract: | A solving process based on the particle swarm algorithm is presented to resolve the knapsack problem. Considering the shortcomings of the particle swarm algorithm that is easy to trap into local minimum, the ideal of the simulated annealing （SA） was introduced into the particle swarm algorithm and the PSO-SA algorithm was obtained. The PSO-SA algorithm maintains the characteristics of particle swarm algorithm easy to realize and also overcomes its defect. The experimental results show that the PSO-SA algorithm can obtain higher quality of the solution. |
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| Keywords: | simulated annealing particle swarm knapsack problem genetic algorithm |
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