A Dynamic Vortex Search Algorithm of Flow Field Attraction for Large-Scale Complex Global Optimization

LIU Jing-sen; LI Hao-ran; LI Yu; ZHOU Huan

doi:10.12263/DZXB.20221108

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A Dynamic Vortex Search Algorithm of Flow Field Attraction for Large-Scale Complex Global Optimization

PAPERS | 更新时间：2025-12-08

- A Dynamic Vortex Search Algorithm of Flow Field Attraction for Large-Scale Complex Global Optimization
- ACTA ELECTRONICA SINICA Vol. 51, Issue 7, Pages: 1949-1955(2023)
- 作者机构：
  
  1.河南大学河南省智能网络理论与关键技术国际联合实验室,河南开封 475004
  2.河南大学软件学院,河南开封 475004
  3.河南大学管理科学与工程研究所,河南开封 475004
  4.河南大学商学院,河南开封 475004
- 作者简介：
- 基金信息：
  
  Foundation Item(s):　National Natural Science Foundation of China(72104069);the Key R&D and Promotion Projects of Henan Province, China(222102210065);the Major Science and Technology Project of Henan Province, China(201300210400);the Action Plan for Postgraduate Training Innovation and Quality Improvement of Henan University(SYLYC2022150)
- DOI：10.12263/DZXB.20221108
  CLC： TP301.6
- Received：30 September 2022，
  
  Revised：2023-01-09，
  
  Published：25 July 2023
- 稿件说明：
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刘景森,李浩然,李煜等.一种面向大规模复杂全局优化的流场吸引动态涡流搜索算法[J].电子学报,2023,51(07):1949-1955.

LIU Jing-sen,LI Hao-ran,LI Yu,et al.A Dynamic Vortex Search Algorithm of Flow Field Attraction for Large-Scale Complex Global Optimization[J].ACTA ELECTRONICA SINICA,2023,51(07):1949-1955.
刘景森,李浩然,李煜等.一种面向大规模复杂全局优化的流场吸引动态涡流搜索算法[J].电子学报,2023,51(07):1949-1955. DOI： 10.12263/DZXB.20221108.

LIU Jing-sen,LI Hao-ran,LI Yu,et al.A Dynamic Vortex Search Algorithm of Flow Field Attraction for Large-Scale Complex Global Optimization[J].ACTA ELECTRONICA SINICA,2023,51(07):1949-1955. DOI： 10.12263/DZXB.20221108.

摘要

为了拓展涡流搜索算法的应用能力，提升其求解复杂优化尤其是大规模复杂优化问题的性能，本文提出了一种基于流场吸引流动、逐维半径试探更新和领导层决策机制的动态涡流搜索算法.首先，本文在算法中引入压强差的概念，使候选解依据压强差进一步向着较优解移动，提高算法整体的搜索质量；然后，算法通过逐维半径更新策略，有效避免了在某一维陷入局部极值的情况；最后，本文在中心点的更新中引入领导层决策机制，提高算法快速确定最佳区域的能力.在计算机仿真部分，本文将该改进算法与多组具有不同代表性的对比算法分别在CEC2017套件的100维和CEC2010套件的1 000维上进行了极值优化分析，结果表明改进后的算法无论是在高维问题还是大规模复杂问题上的寻优结果都能领先其他代表性对比算法多个数量级，具有很好的收敛性能.

Abstract

In order to expand the application capabilities of the vortex search algorithm and improve its performance in solving complex optimization problems

especially large-scale complex optimization problems

a vortex search algorithm is proposed based on attractive flow field operation

dimension-by-dimension dynamic radius

and leadership decision-making mechanism. Firstly

this paper introduces the concept of pressure difference in the algorithm. Candidate solutions further move towards the optimal solution according to pressure difference

which improves the overall search quality of the algorithm. Then

a dimension-by-dimension radius updating strategy is used to avoid trapping into the local minima in a certain dimension.Finally

the leadership decision-making mechanism is introduced into updating the circle center

which improves the algorithm's ability and quickly determines the optimal region. In the simulation section

the improved algorithm and multiple sets of representative comparison algorithms are analyzed for extreme value optimization on the 100 dimensions of the CEC2017 suite and 1 000 dimensions of the CEC2010 suite

respectively. The results show that the improved algorithm can outperform other representative algorithms by multiple orders of magnitude in both high-dimensional and large-scale complex problems

and has good convergence performance.

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Keywords

references

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

HEIDARI A A , MIRJALILI S , FARIS H , et al . Harris hawks optimization: Algorithm and applications [J]. Future Generation Computer Systems , 2019 , 97 : 849 - 872 .

DOĞAN B , ÖLMEZ T . A new metaheuristic for numerical function optimization: Vortex Search algorithm [J]. Information Sciences , 2015 , 293 : 125 - 145 .

ALI W , QYYUM M A , QADEER K , et al . Energy optimization for single mixed refrigerant natural gas liquefaction process using the metaheuristic vortex search algorithm [J]. Applied Thermal Engineering , 2018 , 129 : 782 - 791 .

RAMLI M A M , BOUCHEKARA H R E H . Estimation of solar radiation on PV panel surface with optimum tilt angle using vortex search algorithm [J]. IET Renewable Power Generation , 2018 , 12 ( 10 ): 1138 - 1145 .

SAĞ T , JALIL Z A J . Vortex search optimization algorithm for training of feed-forward neural network [J]. International Journal of Machine Learning and Cybernetics , 2021 , 12 ( 5 ): 1517 - 1544 .

YADAV S , YADAV R , KUMAR A , et al . Design of optimal two-dimensional FIR filters with quadrantally symmetric properties using vortex search algorithm [J]. Journal of Circuits, Systems and Computers , 2020 , 29 ( 10 ): 2050155 .

ÖZKIŞ A , BABALIK . A novel metaheuristic for multi-objective optimization problems: The multi-objective vortex search algorithm [J]. Information Sciences , 2017 , 402 : 124 - 148 .

李盼池 , 卢爱平 . 量子衍生涡流搜索算法 [J]. 控制与决策 , 2016 , 31 ( 6 ): 990 - 996 .

LI P C , LU A P . Quantum-inspired vortex search algorithm [J]. Control and Decision , 2016 , 31 ( 6 ): 990 - 996 . (in Chinese)

LI X , NIU P F , LIU J P . Combustion optimization of a boiler based on the chaos and Levy flight vortex search algorithm [J]. Applied Mathematical Modelling , 2018 , 58 : 3 - 18 .

TOZ M . Chaos-based vortex search algorithm for solving inverse kinematics problem of serial robot manipulators with offset wrist [J]. Applied Soft Computing , 2020 , 89 : 106074 .

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

DEHKORDI A A , SADIQ A S , MIRJALILI S , et al . Nonlinear-based chaotic Harris hawks optimizer: algorithm and internet of vehicles application [J]. Applied Soft Computing , 2021 , 109 : 107574 .

GUPTA S , DEEP K , ENGELBRECHT A P . A memory guided sine cosine algorithm for global optimization [J]. Engineering Applications of Artificial Intelligence , 2020 , 93 : 103718 .

GHASEMI M , AKBARI E , RAHIMNEJAD A , et al . Phasor particle swarm optimization: a simple and efficient variant of PSO [J]. Soft Computing , 2019 , 23 ( 19 ): 9701 - 9718 .

WU G , MALLIPEDDI R , SUGANTHAN P N . Problem Definitions and Evaluation Criteria for the CEC 2017 Competition on Constrained Real-Parameter Optimization [R]. San Sebastian : IEEE , 2017 .

TANG K , LI X , SUGANTHAN P N , et al . Benchmark Functions for the CEC 2010 Special Session and Competition on Large-Scale Global Optimization [R]. Hefei : University of Science and Technology of China , 2009 .

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