[1]李梓成,代永强.一种改进的鲸鱼优化算法[J].计算机技术与发展,2023,33(02):173-180.[doi:10. 3969 / j. issn. 1673-629X. 2023. 02. 026]
　LI Zi-cheng,DAI Yong-qiang.An Improved Whale Optimization Algorithm[J].,2023,33(02):173-180.[doi:10. 3969 / j. issn. 1673-629X. 2023. 02. 026]
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一种改进的鲸鱼优化算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年02期
页码:
173-180
栏目:
人工智能
出版日期:
2023-02-10

文章信息/Info

Title:
An Improved Whale Optimization Algorithm
文章编号:
1673-629X(2023)02-0173-08
作者:
李梓成1 ; 代永强2
1. 甘肃农业大学 理学院,甘肃 兰州 730070;
2. 甘肃农业大学 信息科学技术学院,甘肃 兰州 730070
Author(s):
LI Zi-cheng1 ; DAI Yong-qiang2
1. School of Science,Gansu Agricultural University,Lanzhou 730070,China;
2. School of Information Science & Technology,Gansu Agricultural University,Lanzhou 730070,China
关键词:
鲸鱼优化算法; 群体智能优化算法; 反向学习; 随机游走; 假设检验
Keywords:
whale optimization algorithm; swarm intelligence algorithm; reverse learning; random walk; hypothetical test
分类号:
TP18
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 02. 026
摘要:
鲸鱼优化算法(WOA) 是一种模拟鲸鱼捕食猎物而提出的元启发式算法,具有操作简单、调节参数少等优点。 但由于 WOA 在迭代中后期探索和开发能力不足,导致算法求解精度低,易于陷入局部最优。 针对 WOA 收敛速度慢、精度低、全局探索能力不足且易于陷入局部最优等缺点,提出了一种改进的鲸鱼优化算法( GFWOA) 。 通过引入反向学习策略,对初始种群生成反向解,提高了初始种群质量,进一步加快了算法的收敛速度。 通过引入高斯随机游走策略到鲸鱼优化算法局部寻优阶段,提高了算法的开发能力,增强了算法全局探索的能力和跳出局部最优的能力。 并分别在单峰测试函数、多峰测试函数、低维多峰测试函数上进行仿真实验,通过平均值、标准差与最优值作为衡量算法性能的标准。 结果表明,GFWOA 在收敛精度、收敛速度和稳定性方面均较对比算法有明显提升。 最后通过假设检验方法将 GFWOA 与其他算法进行比较,得出 GFWOA 在性能方面更具优势。
Abstract:
Whale Optimization Algorithm ( WOA) is a meta - heuristic algorithm which simulates whale preying prey. It has simpleoperation and few adjustment parameters. However,due to the lack of exploration and development ability of WOA in the middle and lateiteration,the algorithm is easy to fall into local optimal solution with 　low accuracy. An improved whale optimization algorithm( GFWOA) is proposed to solve the shortcomings of WOA,such as slow convergence speed,low precision,　insufficient global explorationability and easy to fall into local optimum. By introducing the reverse learning strategy,the reverse solution is generated for the initial　population,the quality of the initial population is improved, and the convergence rate of the algorithm is further accelerated. Byintroducing the Gaussian random walk strategy to the local optimization stage of WOA, the development ability is improved, and theability of global exploration and the ability to jump out of the local optimal is enhanced. Simulation experiments are carried out on single-peak test function,multi - peak test function and low - dimensional multi - peak test function respectively. The average value, standarddeviation and optimal value are used as the standard to measure the performance of the algorithm. It is showed that the convergenceaccuracy,convergence speed and stability of GFWOA are significantly improved compared with the comparison algorithm. Finally,GFWOA is compared with other algorithms by the hypothesis testing method,and it is concluded that GFWOA has more advantages inperformance.

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更新日期/Last Update: 2023-02-10