融合黏菌算法与切线飞行的克隆选择算法
Clone Selection Algorithm Combining Slime Mold Algorithm and Tangent Flight
王道维 1杨超 2彭旭 1张文豪 1蒋碧波3
作者信息
- 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;湖北大学 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
- 3. 湖北大学 计算机与信息工程学院,湖北 武汉 430062
- 折叠
摘要
针对传统克隆选择算法可能存在收敛速度慢、容易陷入局部最优的问题,提出一种融合黏菌算法与切线飞行的克隆选择算法.首先,结合黏菌算法的位置更新策略对克隆后的种群进行变异,以提高克隆选择算法在迭代过程中的全局搜索能力;其次,提出一种曲线收敛策略来调节算法的搜索能力,从而提高算法的收敛速度;最后,利用切线飞行策略增强算法跳出局部最优的能力.在14个测试函数上进行测试,通过与其他智能算法、改进的克隆选择算法进行对比,并进行Wilcoxon秩和检验.实验结果表明,改进算法在收敛速度和求解精度方面均有较大提升,验证了改进算法具有良好的优化能力.
Abstract
In view of the fact that the traditional clone selection algorithm may have slow convergence speed and easily fall into the local opti-mal problem,a clone selection algorithm that combines the slime mold algorithm and tangent flight is proposed.Firstly,incorporate the posi-tion updating strategy of the slime mold algorithm to mutate the cloned population,enhancing the global search capability of the clone selec-tion algorithm during the iteration process.Secondly,a curve convergence strategy is proposed to adjust the search ability of the algorithm,thereby improving the convergence speed of the algorithm.Finally,the Tangent flight strategy is utilized to enhance the algorithm's ability to escape from local optima.The proposed strategy was validated by conducting tests on 14 test functions,compared with other intelligent algo-rithms and improved CSA algorithm,and conducted Wilcoxon rank-sum test.The experiments show that the convergence speed and solution accuracy of the algorithm,were improved and verifies the good optimization ability of the improved algorithm.
关键词
克隆选择算法/智能算法/黏菌算法/收敛策略/切线飞行Key words
clone selection algorithm/intelligent algorithm/slime mold algorithm/convergence strategy/tangent flight引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据