计算机工程与应用2019，Vol.55Issue(13)：145-150,6.DOI:10.3778/j.issn.1002-8331.1803-0444
结合高斯分布的改进二进制灰狼优化算法
Improved Binary Grey Wolves Optimization Algorithm Combined with Gaussian Distribution
CHEN Changqian 1MU Xiaodong 1NIU Ben 1WANG Lizhi1
作者信息
- 1. College of War Support, Rocket Force University of Engineering, Xi’an 710025, China
- 折叠
摘要
Abstract
In order to solve the problem that the Gray Wolf Optimization(GWO)algorithm is less utilized and developes immature on discrete issues, a Binary Gary Wolf Optimization(BGWO)algorithm is proposed. Firstly, aiming at the problem of chaos search that the initial population is more concentrated in solving binary problems, the Gaussian distribution curve is introduced, which makes the spatial distribution of initial population more uniform. Secondly, a transfer function is proposed to binarize the GWO. Then the performance of the algorithm is tested by the typical test function. The simulation results show that the proposed BGWO algorithm has better performance in precision. Finally, the BGWO is used to solve the knapsack problem. The conclusion shows that the BGWO has fewer iterations and higher solution accuracy.关键词
二进制灰狼优化(BGWO)/高斯分布/背包问题/最优化选择Key words
Binary Gary Wolf Optimization(BGWO)algorithm/ Gaussian distribution/ knapsack problem/ optimization choice分类
信息技术与安全科学引用本文复制引用
CHEN Changqian,MU Xiaodong,NIU Ben,WANG Lizhi..结合高斯分布的改进二进制灰狼优化算法[J].计算机工程与应用,2019,55(13):145-150,6.
