Whale optimization algorithmcombined with dynamic probability threshold anda adaptive mutation

BI Xiao-ru; MU Qi; GONG Shang-fu

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Whale optimization algorithmcombined with dynamic probability threshold anda adaptive mutation

更新时间：2026-09-04

- Whale optimization algorithmcombined with dynamic probability threshold anda adaptive mutation
- Vol. 36, Issue 12, Pages: 78-83(2019)
- 作者机构：
  
  1. 四川外国语大学重庆南方翻译学院,重庆,401120
  2. 西安科技大学计算机学院,陕西,西安,710054
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2019
- 稿件说明：
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毕孝儒, 牟琦, 龚尚福. 融合动态概率阈值和自适应变异的鲸鱼优化算法[J]. 微电子学与计算机, 2019, 36(12): 78-83, 88.

BI Xiao-ru, MU Qi, GONG Shang-fu. Whale optimization algorithmcombined with dynamic probability threshold anda adaptive mutation[J]. 2019, 36(12): 78-83.
毕孝儒, 牟琦, 龚尚福. 融合动态概率阈值和自适应变异的鲸鱼优化算法[J]. 微电子学与计算机, 2019, 36(12): 78-83, 88. DOI：

BI Xiao-ru, MU Qi, GONG Shang-fu. Whale optimization algorithmcombined with dynamic probability threshold anda adaptive mutation[J]. 2019, 36(12): 78-83. DOI：

摘要

针对基本鲸鱼优化算法在非线性优化问题中存在的收敛精度低、易陷入局部最优解的问题

提出一种融合动态概率阈值和自适应变异的鲸鱼优化算法.首先

利用Fuch混沌和反向学习生成均匀的初始种群; 其次

设计基于双曲余弦函数的动态调整概率阈值以协调算法全局搜索与局部开采能力

采用可变权重对鲸鱼位置更新公式修正

提高收敛速度和精度; 最后

对鲸鱼精英个体引入自适应变异策略

以避免算法陷入局部最优解而搜索停滞.对13个基准测试函数仿真实验

结果表明

与基本GWO算法、PSO算法以及鲸鱼优化算法相比

该算法具有更好的求解精度、收敛速度.

Abstract

To overcome the deficiencies in optimizing the nonlinear problem of traditional whale optimization algorithm(WOA)

including Low convergence accuracyand Easily falling into local optimum in late iteration.

an whale optimization algorithm combined with dynamic probability threshold and adaptive mutation (PTMWOA) is proposed. Fuch chaos and opposition-based learning are used to initialize the population which can generate a population of uniform distribution.Dynamic probability threshold of adaptive adjustment is designed to coordinate the exploration and exploitation ability

and variable weight is applied to revise the updating formula for more precise search. Adaptive mutation strategy based is introduced to the optimum whale location to avoid falling into local optimum. Simulation results on 13 benchmark functions show that the proposed algorithm has better performance on solution

and convergence rate than GWO

PSO and WOA algorithms

关键词

Keywords

references

MIRJALILI S, LEWIS A.The whale optimizationalgorithm[J].Advances in Engineering Software, 2016(95):51-67. DOI: 51

MOHAMED ABD EI AZIZ, AHMED A, et al.Multi-objective whale optimization algorithm for content-based image retrieval[J].Multimedia Tools and Applications, 2018, 77(19):26135-26172. DOI: 26135

ZHANG X, LIU Z W, MIAO Q.Bearing fault diagnosis using a whale optimization algorithm-optimized orthogonal matching pursuit with a combined time-frequency atom dictionary[J].Mechanical Systems and Signal Processing, 2018, 107(1):29-43. DOI: 29

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

张永, 陈锋.一种改进的鲸鱼优化算法[J].计算机工程, 2018, 44(03):208-213. DOI: 208

傅文渊, 凌朝东.自适应折叠混沌优化方法[J].西安交通大学学报, 2013, 47(2):33-38. DOI: 33

徐继亚, 王艳, 纪志成.基于鲸鱼算法优化WKELM的滚动轴承故障诊断[J].系统仿真学报, 2017, 29(9):2189-2197. DOI: 2189

龙文, 伍铁斌, 唐斌.收敛因子非线性变化的鲸鱼优化算法[J].兰州理工大学, 2017, 12(6):102-107. DOI: 102

ELAZIZ M A, OLIVA D.Parameter estimation of solar cells diode models by an improved opposition-based whale optimization algorithm[J].Energy Conversion % Management, 2018(171):1843-1859. DOI: 1843

MAFARJA M M, MIRJALILI S.Hybrid whale optimization algorithm with simulated annealing for feature selection [J].Neurocomputing, 2017(260):302-312. DOI: 302

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