摘要
正弦余弦算法是一种新的群体智能优化算法。针对其易陷入局部最优而导致寻优精度较低的问题,提出一种改进的正弦余弦算法。嵌入动态变异的差分进化算法,提高正弦余弦算法的全局搜索能力。此外,引入自适应惯性权重平衡正弦余弦算法的全局搜索与局部开发能力。采用常用的9个标准测试函数进行实验,结果表明:改进的算法相较于原始的正弦余弦算法、差分进化以及粒子群优化算法,在寻优精度和稳定性方面存在显著的性能优势。
The sine cosine algorithm is a new swarm intelligence optimization algorithm.Aiming at the problem that it is easy to fall into local optimum and lead to low precision of optimization,an improved sine cosine algorithm is proposed.Embedded differential evolution algorithm with dynamic mutation to improve the global search ability of sine cosine algorithm.In addition,an adaptive inertia weight is introduced to balance the global search and local development capabilities of the sine cosine algorithm.Experiment with 9 standard test functions,the results show that the improved algorithm has significant performance advantages in optimization accuracy and stability compared with the original sine cosine algorithm,differential evolution algorithm and particle swarm optimization algorithm.
作者
宁中正
和煦
杨小勇
赵小强
NING Zhongzheng;HE Xu;YANG Xiaoyong;ZHAO Xiaoqiang(School of Communication and Information Engineering,Xi’an University of Posts and Telecommunications,Xi’an 710121,China;National Institute of Radio Spectrum Management,Xi’an 710061,China)
出处
《传感器与微系统》
CSCD
北大核心
2022年第8期131-135,共5页
Transducer and Microsystem Technologies
基金
国家自然科学基金委员会—雅砻江联合基金资助项目(U1965102)
陕西省创新人才推进计划资助项目(2019TD—28)
西安市科技计划资助项目(201806117YF05NC13—2)
陕西省教育厅产业化培育项目(18JF029)
陕西省国际合作计划资助项目(2018KW—025)。
关键词
群体智能优化算法
正弦余弦算法
差分进化算法
惯性权重
swarm intelligence optimization algorithm
sine cosine algorithm
differential evolution algorithm
inertia weight
