分形变异因子修正的差分进化算法  

Fractal Mutation Factor Correcting Differential Evolution Algorithm

作　　者：邱晓红[1] 江阳[1] 李渤[1] 

机构地区：[1]江西理工大学软件学院,南昌330013

出　　处：《模式识别与人工智能》2015年第2期132-138,共7页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.60674054);江西省自然科学基金项目(No.20132BAB201044);江西省高等学校科技落地计划项目(No.KJLD12071)资助

摘　　要：为提高差分进化算法的求解精度,其变异策略应适应目标函数整体变化趋势和随机变化部分.文中提出利用不同的Hurst指数的分形布朗运动改进差分进化算法变异策略,进而构建分形变异因子修正的差分进化算法.针对该算法应用CEC2005进化计算国际会议提出的25个标准测试函数进行测试,至少有10个测试函数的计算结果优于其他差分进化算法,其余测试结果大部分相近,因此文中算法能提高优化问题的求解精度和适应性.To get better solution of the differential evolution （ DE） algorithm, the mutation strategy of DE is proposed and divided into two parts to reflect the changes of the target population trends and their random variation. Fractal mutation factor differential evolution （ FMDE） algorithm is put forward and it consists of an additional mutation factor simulated by a different Hurst index fractal Brownian motion. FMDE is tested on 25 benchmark functions presented at 2005 IEEE congress on evolutionary computation. The optimization results of at least 10 benchmark functions are better than the results obtained by other differential evolution algorithms, and the rest of the test results are approximate. Experimental results show that FMDE significantly improves the accuracy and adaptability of the optimization.

关 键 词：HURST指数 分形 差分进化 分形布朗运动 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]