摘要
针对鲸鱼优化算法(WOA)在求解高维复杂问题时存在收敛精度低,难以解决离散优化问题等的不足,提出了一种离散鲸鱼算法(DWOA)。该算法引入收敛因子调控个体距离最优鲸鱼位置的远近程度,利用惯性权值平衡算法的全局搜索和局部开发能力,通过改进的Sigmoid函数对WOA进行离散化处理。9个基准函数和油田措施规划方案的测试结果表明,DWOA在收敛速度和寻优精度等方面均有较大的提升。
A discrete whale optimization algorithm(DWOA)is proposed to overcome the defects of low convergence and the lack of ability to solve discrete optimization problems when solving high-dimensional complex problems.In DWOA,the convergence factor is introduced to adjust the distance of the individual from the optimal whale position,the adaptive inertia weight is designed to balance the global exploration and local exploitation ability,the whale optimization algorithm(WOA)is discretized by the improved Sigmoid function.The optimization experiments are conducted on the 9 benchmark functions and oilfield measures planning.Simulation results show that the proposed DWOA has a great improvement in convergence speed and convergence precision.
作者
张强
郭玉洁
王颖
刘馨
ZHANG Qiang;GUO Yu-jie;WANG Ying;LIU Xin(School of Computer and Information Technology,Northeast Petroleum University Daqing Heilongjiang 163318)
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2020年第4期622-630,共9页
Journal of University of Electronic Science and Technology of China
基金
国家自然科学基金(61702093)
黑龙江省自然科学基金(F2018003)。
关键词
自适应惯性权值
收敛因子
离散鲸鱼算法
油田措施规划
SIGMOID
adaptive inertia weight
convergence factor
discrete whale optimization algorithm(DWOA)
oilfield measures planning
Sigmoid
