[1]张超,杨忆.求解大规模优化问题的改进正弦余弦算法[J].深圳大学学报理工版,2022,39(6):684-692.[doi:10.3724/SP.J.1249.2022.06684]
 ZHANG Chao,YANG Yi.Improved sine cosine algorithm for large scale optimization problems[J].Journal of Shenzhen University Science and Engineering,2022,39(6):684-692.[doi:10.3724/SP.J.1249.2022.06684]
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求解大规模优化问题的改进正弦余弦算法()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第39卷
期数:
2022年第6期
页码:
684-692
栏目:
电子与信息科学
出版日期:
2022-11-15

文章信息/Info

Title:
Improved sine cosine algorithm for large scale optimization problems
文章编号:
202206010
作者:
张超杨忆
1)宿州职业技术学院计算机信息系,安徽宿州 234101;2)淮北师范大学计算机科学与技术学院,安徽淮北 235000; 3)安徽省认知行为智能计算与应用工程研究中心 安徽淮北 235000
Author(s):
ZHANG Chao YANG Yi
1) Department of Computer Information, Suzhou Vocational and Technological College, Suzhou 234000, Anhui Province, P.R.China 2) College of Computer Science and Technology, Huaibei Normal University, Huaibei 235000, Anhui Province, P.R.China 3) Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB), Huaibei 235000, Anhui Province, P.R.China
关键词:
人工智能正弦余弦算法大规模优化问题Lévy飞行基于距离的非线性参数调整收敛速度收敛精度
Keywords:
artificial intelligence sine cosine algorithm large-scale optimization problems Lévy flight nonlinear parameter adjustment based on distance convergence speed convergence accuracy
分类号:
TP301.6
DOI:
10.3724/SP.J.1249.2022.06684
文献标志码:
A
摘要:
针对正弦余弦算法(sine cosine algorithm, SCA)在求解大规模优化问题时收敛精度低、收敛速度慢和易陷入“维数灾难”的不足,提出一种带Lévy飞行的改进正弦余弦算法(sine cosine algorithm with Lévy flight, SCAL).SCAL算法通过将Lévy飞行分布与正弦余弦种群个体位置向量进行对应元素相乘运算,使Lévy飞行分布的特征和信息融入正弦余弦种群个体信息中,使其拥有Lévy飞行随机游走的特性,增强了个体局部开发和逃离局部极值的能力;采用基于空间距离的非线性参数调整方法,平衡算法的局部开发和全局搜索,提高了算法的收敛速度.在14个经典测试函数上,维度分别为100、1 000和 5 000维时,与SCA、花授粉算法(flower pollination algorithm, FPA)、粒子群优化(particle swarm optimization, PSO)算法、麻雀搜索算法(sparrow search algorithm, SSA)、鲸鱼优化算法(whale optimization algorithm, WOA)5种群体智能算法进行仿真对比实验.结果表明,SCAL算法在收敛精度、收敛速度和鲁棒性上较5种群体智能算法优势明显.与解决大规模优化问题的改进狼群算法(improved wolf pack algorithm, IWPA)、改进花授粉算法(improved flower pollination algorithm, IFPA)、鲸鱼算法的两种改进版本(improved whale optimization algorithm, IWOA)和(modified whale optimization algorithm, MWOA)进行比较,发现SCAL的整体寻优结果优于对比算法,在求解大规模优化问题上具有显著优势和竞争力.
Abstract:
An improved sine cosine algorithm with Lévy flight, named SCAL, is proposed aiming at addressing the issues of the sine cosine algorithm (SCA) in solving large scale optimization problems with low accuracy, slow convergence speed and easy to fall into “dimension disaster”. By element-by-element multiplication of the Lévy flight distribution with the individual position vector of sine and cosine population, the characteristics and information of Lévy flight distribution are integrated into individual information, so that it possesses the characteristics of random walk of Lévy flight and enhances the ability of local exploitation and escape from local extremum. A novel nonlinear parameter adjustment method based on spatial distance is devised to balance the local exploitation and global exploration of the algorithm, and thus improve the convergence speed. Comparative simulation experiments were conducted with five swarm intelligence algorithms including SCA, flower pollination algorithm (FPA), particle swarm optimization (PSO) algorithm, sparrow search algorithm (SSA) and whale optimization algorithm (WOA) on 14 classical test functions with dimensions of 100, 1 000 and 5 000, and the results indicate that SCAL has a significant advantage over the comparison algorithms in terms of convergence accuracy, convergence speed and robustness. Comparative experiments with four algorithms, which including of improved wolf pack algorithm (IWPA), improved flower pollination algorithm (IFPA), improved whale optimization algorithm (IWOA), and modified whale optimization algorithm (MWOA), for solving large scale optimization problems were conducted, and the results show that SCAL has better finding performance than them, and has obvious advantages and competitiveness for solving large scale optimization problems.

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更新日期/Last Update: 2022-11-30