《计算机应用研究》|Application Research of Computers

基于差分进化的改进狼群算法研究

Research of improved wolf pack algorithm based on differential evolution

免费全文下载 (已被下载 次)  
获取PDF全文
作者 王盈祥,陈民铀,程庭莉,盛琪,董龙昌,李哲
机构 1.重庆大学 电气工程学院 输配电装备及系统安全与新技术国家重点实验室,重庆 400044;2.国网重庆市电力公司电力科学研究院,重庆 400044
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2019)08-014-2305-06
DOI 10.19734/j.issn.1001-3695.2018.02.0083
摘要 针对传统狼群算法(WPA)存在易陷入局部最优解、计算资源耗费大、鲁棒性低等问题,提出一种基于差分进化的改进狼群算法(DWPA)。首先,通过引入探狼搜索因子、猛狼最大奔袭次数、自适应围攻步长、差分进化策略等对传统狼群算法进行了改进,在降低算法计算耗费的同时提高了算法的全局搜索能力;然后,运用马尔可夫链理论证明了DWPA的收敛性;最后,对13个测试函数进行寻优测试,并与WPA等四种算法进行对比分析。测试结果表明,DWPA具有良好的鲁棒性和全局搜索能力,在求解多峰、高维、不可分函数方面的寻优能力尤为突出。
关键词 狼群算法; 局部最优解; 鲁棒性; 差分进化; 马尔可夫链
基金项目 国家自然科学基金资助项目(51177177,61105125,51477019)
国家电网公司科技项目(5220001600V6)
本文URL http://www.arocmag.com/article/01-2019-08-014.html
英文标题 Research of improved wolf pack algorithm based on differential evolution
作者英文名 Wang Yingxiang, Chen Minyou, Cheng Tingli, Sheng Qi, Dong Longchang, Li Zhe
机构英文名 1.State Key Laboratory of Power Transmission Equipment & System Security & New Technology,School of Electrical Engineering,Chongqing University,Chongqing 400044,China;2.Chongqing Electric Power Company Electric Power Research Institute of State Grid,Chongqing 400044,China
英文摘要 Aiming at the problems of traditional wolf pack algorithm(WPA), such as easy to fall in to local optimal, large computational resource cost and low robustness, this paper proposed an improved wolf pack algorithm based on differential evolution(DWPA). First of all, it proposed search wolf search factor, maximum number of raid wolves, adaptive siege step size and differential evolution strategy to improve the traditional wolf pack algorithm, which could not only reduce the computational cost of the algorithm but also improved the global search ability. Then, it proved the convergence of DWPA applying the Markov process. Finally, it conducted optimization test on 13 functions and then compared it with WPA and other 4 algorithms. The test results show that DWPA has great robustness and global search ability, especially has an excellent optimizing ability in multi-peak, high-dimension, indivisible functions.
英文关键词 wolf pack algorithm; local optimal; robustness; differential evolution; Markov process
参考文献 查看稿件参考文献
 
收稿日期 2018/2/5
修回日期 2018/3/26
页码 2305-2310
中图分类号 TP301.6
文献标志码 A