• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2024, Vol. 46 ›› Issue (06): 1128-1140.

• 人工智能与数据挖掘 • 上一篇    

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

徐慧玲,刘升,李安东   

  1. (上海工程技术大学管理学院,上海 201620)

  • 收稿日期:2023-02-27 修回日期:2023-05-24 接受日期:2024-06-25 出版日期:2024-06-25 发布日期:2024-06-19
  • 基金资助:
    国家自然科学基金(61673258,61075115);上海市自然科学基金(19ZR1421600)

A heterogeneous guided whale optimization algorithm based on forward-reverse local exploitation and the golden sine algorithm

XU Hui-ling,LIU Sheng,LI An-dong   

  1. (School of Management,Shanghai University of Engineering Science,Shanghai 201620,China)
  • Received:2023-02-27 Revised:2023-05-24 Accepted:2024-06-25 Online:2024-06-25 Published:2024-06-19

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。

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

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 accuracy and poor stability in the Whale Optimization Algorithm (WOA). Firstly, the golden sine operator 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 identified 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 increase 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 pressure vessels. LEDGWOA is compared against 17 other algorithms, demonstrating superior performance.

Key words: whale optimization algorithm, dominant whale group, golden sine, bi-directional local exploitation, chebyshev mapping