混合策略改进的正弦余弦算法

郭园园; 袁杰; 杨炳媛

doi:10.13568/j.cnki.651094.651316.2022.04.27.0001

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混合策略改进的正弦余弦算法

工程技术 | 更新时间：2026-01-21

- 混合策略改进的正弦余弦算法
- 混合策略改进的正弦余弦算法
- 新疆大学学报（自然科学版中英文） 2023年40卷第1期页码：114-121
- 作者机构：
  
  新疆大学电气工程学院
- 作者简介：
- 基金信息：
  
  国家自然科学基金“非结构环境下机器人羽流寻源自主演进策略研究”（62263031）,“机器人化单分子病毒可控侵染细胞及原位定量表征方法研究”（62073227）;新疆维吾尔自治区自然科学基金“非结构环境下机器人建图与主动安全方法研究”（2022D01C53）
- DOI：10.13568/j.cnki.651094.651316.2022.04.27.0001
  中图分类号： TP18
- 纸质出版：2023
- 稿件说明：
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[1]郭园园,袁杰,杨炳媛.混合策略改进的正弦余弦算法[J].新疆大学学报(自然科学版)(中英文),2023,40(01):114-121.
[1]郭园园,袁杰,杨炳媛.混合策略改进的正弦余弦算法[J].新疆大学学报(自然科学版)(中英文),2023,40(01):114-121. DOI： 10.13568/j.cnki.651094.651316.2022.04.27.0001.

DOI：10.13568/j.cnki.651094.651316.2022.04.27.0001.

摘要

针对正弦余弦算法在求解函数优化问题时存在的求解速度慢和收敛精度低等缺陷，提出了一种混合策略改进的正弦余弦算法．首先，采用余弦拟合度策略，通过旋转变换算子与轴向变换算子来更新种群位置信息，进而能够有效保持种群的多样性和提高求解精度．其次，利用单纯形法对种群中适应度较差的个体更新位置信息，增强算法的勘探能力和开采能力．最后，采用自适应参数调整策略，来均衡全局的搜索性能和局部的开采性能．增加动态惯性权重策略，使寻优过程充分利用个体的位置信息，提高局部的开采性能和加快算法的收敛速度．采用8个基准测试函数来评估改进算法的性能，并与其它改进算法用于压力容器设计中．实验结果表明：改进算法在求解速度和收敛精度方面均具有实质性的优势．

Abstract

Aiming at the slow solution speed and low convergence accuracy of the sine-cosine algorithm in solving function optimization problems

a hybrid strategy-improved sine-cosine algorithm is proposed. Firstly

the cosine fit strategy is adopted to update the population position information through the rotation transformation operator and the axial transformation operator

which can effectively maintain the diversity of the population and improve the accuracy of the solution. Secondly

the simplex method is used to update the position information of the individuals with poor fitness in the population to enhance the exploration and mining capabilities of the algorithm.Finally

an adaptive parameter adjustment strategy is adopted to balance the global search performance and the local mining performance. The dynamic inertia weight strategy is taken

and the individual location information is used in the optimization process

improving local mining performance and speeding up the convergence speed of the algorithm. Eight benchmark test functions are used to evaluate the performance of the improved algorithm

and used with other improved algorithms in the design of pressure vessels. Experimental results show that the improved algorithm has substantial advantages in aspect of solution speed and convergence accuracy.

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references

MIRJALILI S. SCA:a sine cosine algorithm for solving optimization problems[J]. Knowledge-Based Systems, 2016, 96:120-133.

DHIMAN G, KUMAR V. Seagull optimization algorithm:theory and its applications for large-scale industrial engineering problems[J]. Knowledge-Based Systems, 2018, 165:169-196.

ASKARZADEH A. A novel metaheuristic method for solving constrained engineering optimization problems:crow search algorithm[J]. Computers and Structures, 2016, 169:1-12.

MIRJALILI S, GANDOMI A H, MIRJALILI S Z, et al. Salp swarm algorithm:a bio-inspired optimizer for engineering design problems[J]. Advances in Engineering Software, 2017, 114:163-191.

KAUR S, AWASTHI L K, SANGAL A L, et al. Tunicate swarm algorithm:a new bio-inspired based metaheuristic paradigm for global optimization[J]. Engineering Applications of Artificial Intelligence, 2020, 90:103541.

林杰,何庆.混合策略改进正弦余弦算法[J].计算机应用研究, 2020, 37(12):3612-3617.

徐明,焦建军,龙文.基于Logistic模型和随机差分变异的正弦余弦算法[J].计算机科学, 2020, 47(2):206-212.

王联国,刘小娟.基于采蜜机制的正弦余弦算法及其在机械优化设计中的应用[J].中国机械工程, 2021, 32(21):2577-2589.

何庆,徐钦帅,魏康园.基于改进正弦余弦算法的无线传感器节点部署优化[J].计算机应用, 2019, 39(7):2035-2043.

杨晓倩,李琦,李豪欣,等.基于改进正弦余弦算法的MIMO雷达相位编码信号集设计[J].电光与控制, 2021, 28(6):90-94.

郭文艳,王远,戴芳,等.基于精英混沌搜索策略的交替正余弦算法[J].控制与决策, 2019, 34(8):1654-1662.

方旭阳,武相军,游大涛.具有学习机制的正弦余弦算法[J].计算机应用研究, 2020, 37(3):809-813.

周晓君,阳春华,桂卫华.状态转移算法原理与应用[J].自动化学报, 2020, 46(11):2260-2274.

MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95:51-67.

RAO R V, SAVSANI V J, VAKHARIA D P. Teaching-learning-based optimization:an optimization method for continuous non-linear large scale problems[J]. Information Sciences, 2012, 183(1):1-15.

RASHEDI E, NEZAMABADI-POUR H, SARYAZDI S. GSA:a gravitational search algorithm[J]. Information Sciences, 2009,179(13):2232-2248.

MIRJALILI S, MIRJALILI S M, HATAMLOU A. Multi-verse optimizer:a nature-inspired algorithm for global optimization[J].Neural Computing and Applications, 2016, 27(2):495-513.

李银通,韩统,赵辉,等.自学习策略和Levy飞行的正弦余弦优化算法[J].重庆大学学报, 2019, 42(9):56-66.

牛培峰,吴志良,马云鹏,等.基于正弦余弦算法的汽轮机热耗率预测[J].动力工程学报, 2018, 38(2):85-91.

肖子雅,刘升,韩斐斐,等.正弦余弦指引的乌鸦搜索算法研究[J].计算机工程与应用, 2019, 55(21):52-59.

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