非线性权重和收敛因子的鲸鱼算法

王涛; RyadChellali

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非线性权重和收敛因子的鲸鱼算法

更新时间：2026-09-04

- 非线性权重和收敛因子的鲸鱼算法
- Whale Optimization Algorithm with Nonlinear Weight and Convergence Factor
- 微电子学与计算机 2019年36卷第1期页码：11-15
- 作者机构：
  
  南京工业大学机构电气工程与控制科学学院,江苏,南京,211816
- 作者简介：
- 基金信息：
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- 纸质出版：2019
- 稿件说明：
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王涛, RyadChellali. 非线性权重和收敛因子的鲸鱼算法[J]. 微电子学与计算机, 2019, 36(1): 11-15.

WANG Tao, Ryad CHELLALI. Whale Optimization Algorithm with Nonlinear Weight and Convergence Factor[J]. 2019, 36(1): 11-15.
王涛, RyadChellali. 非线性权重和收敛因子的鲸鱼算法[J]. 微电子学与计算机, 2019, 36(1): 11-15. DOI：

WANG Tao, Ryad CHELLALI. Whale Optimization Algorithm with Nonlinear Weight and Convergence Factor[J]. 2019, 36(1): 11-15. DOI：

摘要

针对鲸鱼优化算法收敛速度慢

收敛精度低等问题

提出一种基于非线性收敛因子和惯性权重的鲸鱼优化算法.首先使用改进后的Logistic混沌映射来初始化种群

增加种群多样性.然后将线性变化的收敛因子改进为一种分段式非线性收敛因子

同时增加了非线性惯性权重来增强算法的勘探和开发能力.最后选取7个基准函数进行测试

实验表明改进后算法收敛速度快、精度高.

Abstract

To overcome the problems of slow convergence

slow accuracy of the standard whale optimization algorithm

a nonlinear weight and a nonlinear convergence factor in whale optimization algorithm is proposed. Firstly

the improved Logistic chaotic mapping is applied to initial population. Then the linearly variable convergence factor is improved to a piecewise nonlinear convergence factor. At the same time

nonlinear inertia weights are added to enhance the exploration and exploitation capabilities of the whale optimization algorithm. Finally

seven benchmark functions are selected for testing. Experiments show that the improved algorithm has fast convergence and high precision.

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references

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Prakash D B, Lakshminarayana C. Optimal siting of capacitors in radial distribution network using whale optimization algorithm[J]. Alexandria Engineering Journal, 2017, 56(4): 499-509. DOI: 499

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Oliva D, El Aziz M A, Hassanien A E. Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm[J]. Applied Energy, 2017(200): 141-154. DOI: 141

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刘竹松, 李生.正余混沌双弦鲸鱼优化算法[J].计算机工程与应用, 2018, 54(7):159-163. DOI: 159

El Aziz M A, Ewees A A, Hassanien A E. Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation[J]. Expert Systems with Applications, 2017(83): 242-256. DOI: 242

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