| 一种自适应交替的粒子群差分进化优化算法 |
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| 引用本文： | 翟金涛,高兴宝. 一种自适应交替的粒子群差分进化优化算法[J]. 纺织高校基础科学学报, 2012, 25(3): 379-383 |
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| 作者姓名： | 翟金涛 高兴宝 |
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| 作者单位： | 陕西师范大学 数学与信息科学学院,陕西 西安,710062 |
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| 基金项目： | 国家自然科学基金资助项目，中央高校基本科研业务费专项基金资助项目 |
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| 摘 要： | 为了克服粒子群算法易陷入早熟收敛的缺点及提高差分进化算法的搜索能力,提出了一种自适应交替的粒子群差分进化算法.该算法采用自适应的概率交替使用PSO和DE,通过对6个基准函数的测试,说明本文提出的算法是一种收敛速度快、求解精度高的全局优化算法.
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| 关 键 词： | 粒子群算法 差分进化算法 基准函数 全局优化 |
A self-adaptive alternating optimization algorithm based on particle swarm optimization and differential evolution |
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| ZHAI Jin-tao , GAO Xing-bao. A self-adaptive alternating optimization algorithm based on particle swarm optimization and differential evolution[J]. Basic Sciences Journal of Textile Universities, 2012, 25(3): 379-383 |
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| Authors: | ZHAI Jin-tao GAO Xing-bao |
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| Affiliation: | (College of Mathematics and Information Science,Shaanxi Normal University,Xi′an 710062,China) |
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| Abstract: | To overcome particle swarm optimization algorithm′s premature convergence and improve the search ability of differential evolution,a hybrid optimization algorithm PSO-DE was proposed.This algorithm alternately used PSO and DE by a self-adaptive probability.Six benchmark functions are used in the test,and the experimental results show that PSO-DE is a powerful global optimization algorithm with rapid converence speed and higher accuracy. |
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| Keywords: | particle swarm optimization differential evolution benchmark functions global optimization |
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