摘要
【目的】社会蜘蛛群优化算法(SSO)是一种新颖的元启发式优化算法,自从它被提出之后就受到该领域学者的广泛关注,并且也被成功应用到许多领域。但是由于社会蜘蛛群优化算法还处在算法的研究初期,该算法的收敛速度与收敛精度还需要进一步提高。【方法】将差分进化算子引入到社会蜘蛛群优化算法(SSO-DM)中,并将改进的算法应用于函数优化问题中,通过5个标准测试函数来验证基于差分进化算子的社会蜘蛛群优化算法(SSO-DM)的优化性能。【结果】差分进化算子增强了社会蜘蛛群优化算法的收敛速度与收敛精度。【结论】本研究中所提出的算法能够获得精确解,并且它也具有较快的收敛速度和较高的算法稳定性。
【Objective】A social-spider optimization algorithm(SSO)is a novel meta-heuristic op-timization algorithm,it has been widely concerned by scholars in this field since it was put for-ward?and it had been successfully applied in many fields?but the algorithm is still in the earlystages of the study?the convergence speed and computational accuracy of the algorithm need tobe improved.[Methodslln order to enhance the convergence speed and computational accuracyof the algorithm,in this paper,a social-spider optimization algorithm with differential mutationoperator(SSO-DM)had been proposed?and was applied to the function optimization problem.The improvement involved differential mutation operator.A social-spider optimization algo-rithm with differential mutation operator(SSO-DM)was validated by five benchmark func-tions.[Resultsl Differential mutation operator enhanced the convergence speed and computa-tional accuracy of the algorithm.【Conclusion】The results showed that the proposed algorithmwas able to obtain accurate solution,and it also Had a fast convergence speed and a high degreeof stability.
作者
赵汝鑫
罗淇芳
周永权
ZHAO Ruxin;LUO Qifang;ZHOU Yongquan(College of Information Science and Engineering, Guangxi University for Nationalities ,Nanning, Guangxi, 530006 , China;Guangxi Higher School Key Laboratories of Complex Systems and Intelligent Computing,Nanning,Guangxi,530006,China)
出处
《广西科学》
CAS
2017年第3期247-257,共11页
Guangxi Sciences
基金
国家自然科学基金项目(61463007,61563008)
广西自然科学基金项目(2016GXNSFAA380264)资助
关键词
社会蜘蛛群优化算法
差分进化算子
元启发式优化算法
函数优化
social-spider optimization algorithm
differential mutation operator
meta-heuristic optimization algorithm
functions optimization
