计算机应用研究2016，Vol.33Issue(12)：3634-3637,4.DOI:10.3969/j.issn.1001-3695.2016.12.026
改进自适应微分进化算法求解全局优化问题
Improved adaptive differential evolution algorithm for global optimization
摘要
Abstract
Differential evolution (DE)algorithm has some disadvantages,such as slow convergence speed,low convergence precision and easy to fall into local optimal solution in the early stages of the evolution.The paper proposed an improved adap-tive differential evolution (IADE)algorithm by improving the mutation equation of DE and introducing a new control parame-ters adaption strategy.In the process of the evolution,the control parameters will be dynamically adjusted by comparing individ-ual fitness with average fitness of the parent population.Meanwhile,the paper chose the ten standard functions commonly used for the comparison of optimization algorithm to perform the comparative test of IADE and the other improved DE algorithms,and the experimental results show that IADE algorithm not only can significantly improve the convergence speed and convergence precision,but also has very good robustness,so that IADE algorithm can meet the requirements for the real-time,accuracy and stability of process optimization.关键词
微分进化/全局优化/控制参数自适应/收敛速度/鲁棒性Key words
differential evolution/global optimization/control parameters adaption/convergence speed/robustness分类
信息技术与安全科学引用本文复制引用
王世豪,杨红雨,李玉贞,刘洪,杨波..改进自适应微分进化算法求解全局优化问题[J].计算机应用研究,2016,33(12):3634-3637,4.基金项目
国家“863”计划资助项目（2013AA013802）；国家空管科研资助项目 （）
