摘要
针对鲸鱼优化算法(WOA)收敛速度慢、收敛精度低、易陷入局部最优的问题,提出一种基于自适应调整权重和搜索策略的鲸鱼优化算法(AWOA).设计一种随着鲸鱼种群变化情况而自适应调整权重的方法,提高了算法的收敛速度;设计一种自适应调整搜索策略,提高了算法跳出局部最优的能力.利用23个标准测试函数,分别针对高维和低维问题进行测试,仿真结果表明,AWOA在收敛精度和收敛速度方面总体上明显优于其他多种改进的鲸鱼优化算法.
The whale optimization algorithm(WOA)has slow convergence speed and low convergence accuracy and tends to fall into local optimum.In order to solve these problems,a whale optimization algorithm(AWOA)based on adaptive adjustment of weight and search strategy was proposed.An adaptive adjustment of weight with the current distribution of whale population was designed to improve the convergence speed of the algorithm,and an adaptive adjustment of search strategy was designed to improve the ability of the algorithm to jump out of local optimum.Using 23 standard test functions,the algorithm was tested for high-dimensional and low-dimensional problems,respectively.The simulation results showed that the AWOA is generally superior to other improved whale optimization algorithms in terms of convergence accuracy and convergence speed.
作者
孔芝
杨青峰
赵杰
熊浚钧
KONG Zhi;YANG Qing-feng;ZHAO Jie;XIONG Jun-jun(School of Control Engineering,Northeastern University at Qinhuangdao,Qinhuangdao 066004,China)
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2020年第1期35-43,共9页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(61402088)
河北省自然科学基金资助项目(F2017501041)
中央高校基本科研业务费专项资金资助项目(N172304030)
关键词
鲸鱼优化算法
自适应调整权重
自适应调整搜索策略
函数优化
全局优化
interval multi-objective optimization
interval particle swarm optimization
interval whale optimization algorithm
adaptive adjustment of weight
adaptive adjustment of search strategy
function optimization
global optimization
