Whale Optimization Algorithm Based on Adaptive Weight and Simulated Annealing

CHU Ding-li; CHEN Hong; WANG Xu-guang

doi:10.3969/j.issn.0372-2112.2019.05.003

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Whale Optimization Algorithm Based on Adaptive Weight and Simulated Annealing

更新时间：2025-07-16

- Whale Optimization Algorithm Based on Adaptive Weight and Simulated Annealing
- Acta Electronica Sinica Vol. 47, Issue 5, Pages: 992-999(2019)
- 作者机构：
  
  国防科技大学电子对抗学院, 61477部队,安徽,合肥,230037
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61571446);Natural Science Foundation of Anhui Province (No.KY13C152)
- DOI：10.3969/j.issn.0372-2112.2019.05.003
  CLC： TP301.6
- Published Online：25 May 2019，
  
  Published：2019
- 稿件说明：
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CHU Ding-li, CHEN Hong, WANG Xu-guang. Whale Optimization Algorithm Based on Adaptive Weight and Simulated Annealing[J]. Acta Electronica Sinica, 2019, 47(5): 992-999.
DOI：

CHU Ding-li, CHEN Hong, WANG Xu-guang. Whale Optimization Algorithm Based on Adaptive Weight and Simulated Annealing[J]. Acta Electronica Sinica, 2019, 47(5): 992-999. DOI： 10.3969/j.issn.0372-2112.2019.05.003.

摘要

针对鲸鱼优化算法容易陷入局部极值和收敛速度慢的问题，提出了一种结合自适应权重和模拟退火的鲸鱼优化算法.通过改进的自适应权重策略来调整算法的收敛速度，通过模拟退火增强鲸鱼优化算法的全局寻优能力.仿真实验中计算了18个测试函数，对比了粒子群算法、海豚回声定位算法和标准鲸鱼算法并进行统计分析，同时比较了单独结合自适应权重和模拟退火对鲸鱼优化的影响，结果表明，改进的算法在测试函数的极值计算中，计算精度和收敛速度方面都有了明显提升，验证了改进算法的有效性.

Abstract

Aiming at the problem that whale optimization algorithm is easy to fall into local extreme value and slow convergence speed

this paper proposes a whale optimization algorithm based on adaptive weight and simulated annealing.The improved convergence weight strategy is used to adjust the convergence speed of the algorithm

and the global optimization ability of the whale optimization algorithm is enhanced by simulated annealing.In the simulation experiment

18 test functions were calculated and the genetic algorithm

the particle swarm optimization algorithm and the standard whale algorithm were compared and statistically analyzed.At the same time

the influence of the adaptive weight and simulated annealing on the whale optimization is compared.The results show that the improved algorithm has a significant improvement in the calculation of the extremum of the test function

and the effectiveness of the improved algorithm is verified.

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