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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:

34

期数:

2024年06期

页码:

140-147

栏目:

人工智能

出版日期:

2024-06-10

文章信息/Info

Title:

Improved Clonal Selection Algorithm Fusing Multiple Strategies

文章编号:

1673-629X(2024)06-0140-08

作者:

张文豪1; 杨超2; 3; 彭旭1; 王道维1; 范波4

1. 湖北大学 网络空间安全学院,湖北 武汉 430062;2. 湖北大学 计算机与信息工程学院,湖北 武汉 430062;3. 智慧政务与人工智能应用湖北省工程研究中心,湖北 武汉 430062;4. 武汉大学 科学技术发展研究院,湖北 武汉 430072

Author(s):

ZHANG Wen-hao1; YANG Chao2; 3; PENG Xu1; WANG Dao-wei1; FAN Bo4

1. School of Cyber Science and Technology,Hubei University,Wuhan 430062,China;2. School of Computer and Information Engineering,Hubei University,Wuhan 430062,China;3. Engineering Research Center of Hubei Province in Intelligent Government Affairs and Application of Artificial Intelligence,Wuhan 430062,China;4. School of Science and Technology Development,Wuhan University,Wuhan 430072,China

关键词:

克隆选择算法; 正余弦优化策略; 浓度调节策略; Sobol序列; 抗体变异

Keywords:

clonal selection algorithm; sine cosine optimization strategy; concentration regulation strategy; Sobol sequence; antibody varia-tion

分类号:

TP18

DOI:

10.20165/j.cnki.ISSN1673-629X.2024.0080

摘要:

针对克隆选择算法(CSA)解决复杂优化问题时存在的效率低下、收敛速度慢以及容易陷入局部最优等不足,提出了一种融合多策略改进的克隆选择算法(MSICSA)。 首先,引入 Sobol 序列初始化种群,丰富种群多样性,并提高算法整体稳定性;其次,引入正余弦优化策略加强算法全局搜索能力,避免陷入局部最优而导致算法停滞;最后,引入动态浓度调节策略,调节算法在不同时期搜索空间内的抗体浓度,控制算法加强前期全局搜索以及后期局部寻优能力,并提高算法收敛速度。 文中利用 12 种 CEC 测试函数及 4 种算法对 MSICSA 进行测试及对比,消融实验证明了改进策略的有效性,扰动实验验证了文中算法的稳定性与鲁棒性,对比仿真以及几项实验均表明 MSICSA 能够有效提升收敛速度和寻优精度,并提高跳出局部最优的能力。

Abstract:

Aiming at the shortcomings of clonal selection algorithm ( CSA) in solving complex optimization problems, such as low efficiency,slow convergence speed and easy to fall into local optimum,an improved clonal selection algorithm based on multi-strategy (MSICSA) was proposed. Firstly,the Sobol sequence was introduced to initialize the population,which enriched the diversity of the population and improved the overall stability of the algorithm. Secondly,the sine cosine optimization strategy was introduced to enhance the global search ability of the algorithm to avoid falling into local optimum and causing the stagnation of the algorithm. Finally,a dynamic adaptive concentration adjustment strategy was introduced to adjust the antibody concentration in the search space at different periods of the algorithm,which strengthened the global search ability in the early stage and the local optimization ability in the later stage,and improved the convergence speed of the algorithm. The ablation experiment shows the effectiveness of the improved strategy,and the perturbation experiment verifies the stability and robustness of the proposed algorithm. The comparative simulation show that MSICSA can effectively improve the convergence speed and optimization accuracy,and improve the ability to jump out of local optimum.

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更新日期/Last Update: 2024-06-10