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| 基于改进PSO和DE的混合算法 | |
| 引用本文： | 易文周,张超英,王强,许亚梅,周金玲.基于改进PSO和DE的混合算法[J].计算机工程,2010,36(10):233-235. |
| 作者姓名： | 易文周 张超英 王强 许亚梅 周金玲 |
| 作者单位： | 1. 广东工程职业技术学院计算机信息系,广州,510520;广西师范大学计算机科学与信息工程学院,桂林,541004 2. 广西师范大学计算机科学与信息工程学院,桂林,541004 3. 广东工程职业技术学院计算机信息系,广州,510520 |
| 基金项目： | 广西自然科学基金资助项目(桂科自0640067) |
| 摘 要： | 研究粒子群优化(PSO)算法和差分进化(DE)算法的优缺点，通过改进PSO算法并与DE算法混合，得到一种双种群的新型混合全局优化算法。经过对5个标准测试函数的大量实验计算表明，该算法能有效克服PSO算法和DE算法的缺陷，使寻优精度有较大改进，在高维情况下表现更加突出。 |
| 关 键 词： | 粒子群优化算法 差分进化算法 混合算法 |
Hybrid Algorithm Based on Improved PSO and DE |
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| YI Wen-zhou,ZHANG Chao-ying,WANG Qiang,XU Ya-mei,ZHOU Jin-ling.Hybrid Algorithm Based on Improved PSO and DE[J].Computer Engineering,2010,36(10):233-235. | |
| Authors: | YI Wen-zhou ZHANG Chao-ying WANG Qiang XU Ya-mei ZHOU Jin-ling |
| Affiliation: | (1. Department of Computer and Information, Guangdong Vocational and Technical College, Guangzhou 510520; 2. College of Computer Science and Information Engineering, Guangxi Normal University, Guilin 541004) |
| Abstract: | In accordance with the respective advantages and disadvantages of Particle Swarm Optimization(PSO) algorithm and Differential Evolution(DE) algorithm, a novel hybrid algorithm is achieved through the improvement of Particle Swarm Optimization(PSO) algorithm and mixture with Differential Evolution(DE) algorithm. Massive experiments of five standard benchmark functions in five different dimensions suggest that this novel hybrid algorithm effectively overcomes the respective disadvantages of PSO algorithm and DE algorithm. It produces a conspicuous effect, which results in satisfactory outcome in experiments especially in high dimension. |
| Keywords: | Particle Swarm Optimization(PSO) algorithm Differential Evolution(DE) algorithm hybrid algorithm |
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