An Enhanced Whale Optimization Algorithm for the Problems of Function Optimization

HE Qing; WEI Kang-yuan; XU Qin-shuai

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An Enhanced Whale Optimization Algorithm for the Problems of Function Optimization

更新时间：2026-09-04

- An Enhanced Whale Optimization Algorithm for the Problems of Function Optimization
- Vol. 36, Issue 4, Pages: 72-77(2019)
- 作者机构：
  
  1. 贵州大学大数据与信息工程学院,贵州,贵阳,550025
  2. 贵州大学贵州省公共大数据重点实验室,贵州,贵阳,550025
- 作者简介：
- 基金信息：
- DOI：
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- Published：2019
- 稿件说明：
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何庆, 魏康园, 徐钦帅. 求解函数优化问题的改进鲸鱼优化算法[J]. 微电子学与计算机, 2019, 36(4): 72-77, 83.

HE Qing, WEI Kang-yuan, XU Qin-shuai. An Enhanced Whale Optimization Algorithm for the Problems of Function Optimization[J]. 2019, 36(4): 72-77.
何庆, 魏康园, 徐钦帅. 求解函数优化问题的改进鲸鱼优化算法[J]. 微电子学与计算机, 2019, 36(4): 72-77, 83. DOI：

HE Qing, WEI Kang-yuan, XU Qin-shuai. An Enhanced Whale Optimization Algorithm for the Problems of Function Optimization[J]. 2019, 36(4): 72-77. DOI：

摘要

针对鲸鱼优化算法(WOA)易陷入局部最优、寻优精度低等问题

提出一种改进的鲸鱼优化算法(EWOA).首先

将自适应策略引入鲸鱼位置更新公式中

以便平衡算法全局探索和局部开发能力的同时

加快算法收敛速度、提高算法的寻优精度; 然后

引入差分变异思想

对较优的鲸鱼位置进行变异操作以避免算法陷入局部最优

防止早熟收敛现象; 最后

通过对9个基准测试函数在固定参数和不同维度的实验表明

改进算法在寻优精度和收敛速度比传统算法均有显著提高

尤其在高维函数的优化问题中表现出更好的收敛性能.

Abstract

To resolve the problem that the whale optimization algorithm (WOA) is easy to fall into local optimum and low precision

an enhanced whale optimization algorithm (EWOA) is proposed. Firstly

the adaptive strategy was introduced into the whales position to balance the global exploration and local exploitation capabilities of the algorithm

speed up the convergence of the algorithm

and improve the optimization accuracy of the algorithm. Then

to avoid the algorithm falling into local optimum and prevent premature convergence

the idea of differential mutation was introduced to mutate the better whales position. Finally

the experimental results on nine test functions under fixed parameters and different dimensions show that the improved algorithm has significantly improved search precision and convergence speed compared with the traditional WOA. Especially in the optimization problem of high-dimensional functions

the improved algorithm shows better convergence performance than the traditional WOA and its variants.

关键词

Keywords

references

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STRON R, PRICE K. Differential evolution―A simple and efficient heuristic for global optimization over continuous spaces[J]. J of Global Optimization, 1997, 11(4): 341-359. DOI: 341

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郭振洲, 王平, 马云峰, 等.基于自适应权重和柯西变异的鲸鱼优化算法[J].微电子学与计算机, 2017, 34(9): 20-25. DOI: 20

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龙文, 蔡绍洪, 焦建军, 等.求解大规模优化问题的改进鲸鱼优化算法[J].系统工程理论与实践, 2017, 37(11): 2983-2994. DOI: 2983

陈占伟, 李骞.一种自适应惯性权重的粒子群优化算法[J].微电子学与计算机, 2011, 28(3): 27-30. DOI: 27

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