摘要
针对基本海豚群算法易陷入局部最优的缺陷,提出了基于信息熵的改进海豚群算法,引入信息熵来度量海豚群搜索阶段的不确定性,控制搜索阶段的选择概率,降低盲目搜索,克服了基本海豚群算法搜索阶段易陷入局部最优和早熟收敛的缺陷。将改进后的算法应用到桁架结构的优化中,并与其他算法优化结果进行了比较,证明了改进的算法在收敛速度和寻优精度方面有更好的表现,将其应用到桁架结构优化设计中,为结构优化设计提供了一种有效的方法。
Aiming at the disadvantage that basic dolphin swarm algorithm is easy to fall into local optimum,an improved dolphin swarm algorithm based on information entropy is proposed.The algorithm measures the uncertainty of dolphin swarm search phase by information entropy,controls the selection probability of search phase,reduces blind search,and overcomes the shortcomings of local optimum and premature convergence in the search phase of basic dolphin swarm algorithm.The improved algorithm is applied to the optimization of truss structure and compared with other algorithms.It is proved that the improved algorithm has better performance in convergence speed and optimization accuracy.It is applied to optimum design of truss structure,and provides an effective method for optimum design of structure.
作者
李彦苍
王旭
LI Yancang;WANG Xu(Hebei University of Engineering and Water Resources and Hydropower,Hebei University of Engineering and Hydropower,056038;School of Civil Engineering,Hebei University of Engineering,Handan,Hebei 056038)
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2019年第5期76-85,共10页
Journal of Chongqing University
基金
河北省自然科学基金资助项目(E2012402030)
河北省高校百名优秀创新人才支持计划项目(BR2-206)~~
关键词
海豚群算法
改进
信息熵
收敛
自适应
组合优化
dolphin swarm algorithm
improvement
information entropy
convergence
self-adaption
combinatorial optimization
