基于双向局部开发和黄金正弦的异构导向的鲸鱼优化算法
A heterogeneous guided whale optimization algorithm based on forward-reverse local exploitation and the golden sine algorithm
徐慧玲 1刘升 1李安东1
作者信息
- 1. 上海工程技术大学管理学院,上海 201620
- 折叠
摘要
为了解决鲸鱼优化算法 WOA准确率低和稳定性差的问题,提出了一种基于双向局部开发和黄金正弦算法的异构导向的鲸鱼优化算法LEDGWOA.在搜索猎物阶段嵌入黄金正弦算子,结合"更优更近"的原则,增强个体间信息交流的强度.此外,根据适应度值区分出统治鲸鱼群,用自适应惯性权重计算出一个虚拟领导者.在包围猎物阶段时,整合切比雪夫阈值的双向开发策略,从而加强了邻域的开发强度.随机螺旋式更新可以间接地增加种群在迭代后期的分散度.改进后的算法在CEC2017和CEC2019函数上进行仿真实验,并成功应用于压力容器的优化设计.LEDGWOA与17种算法进行对比,结果表明其具有优越的性能.
Abstract
The paper proposes a heterogeneous guided whale optimization algorithm(LEDGWOA)based on forward-reverse local exploitation and the golden sine algorithm to address the issues of low ac-curacy and poor stability in the Whale Optimization Algorithm(WOA).Firstly,the golden sine opera-tor is embedded during the prey searching phase,enhancing the intensity of information exchange among individuals based on the principle of"better and closer."Additionally,dominant whale groups are iden-tified based on fitness values,and an adaptive inertial weight is calculated to determine a virtual leader.During the prey encircling phase,a bidirectional exploitation strategy incorporating Chebyshev threshold is integrated to strengthen neighborhood development intensity.Random spiral updates indirectly in-crease population diversity in later iterations.The improved algorithm is evaluated through simulation experiments on CEC2017 and CEC2019 functions and successfully applied to optimize the design of pres-sure vessels.LEDGWOA is compared against 17 other algorithms,demonstrating superior perform-ance.
关键词
鲸鱼优化算法/统治鲸鱼群/黄金正弦/双向局部开发/切比雪夫映射Key words
whale optimization algorithm/dominant whale group/golden sine/bi-directional local ex-ploitation/chebyshev mapping引用本文复制引用
基金项目
国家自然科学基金(61673258)
国家自然科学基金(61075115)
上海市自然科学基金(19ZR1421600)
出版年
2024
国家科技期刊平台
NSTL
万方数据