融合模式搜索的蝗虫优化算法及其应用

肖怡心; 刘三阳

doi:10.19665/j.issn1001-2400.20230602

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融合模式搜索的蝗虫优化算法及其应用

计算机科学与技术&网络空间安全 | 更新时间：2024-06-03

- 融合模式搜索的蝗虫优化算法及其应用
- Integration of pattern search into the grasshopper optimization algorithm and its applications
- 西安电子科技大学学报 2024年51卷第2期页码：137-156
- 作者机构：
  
  西安电子科技大学数学与统计学院,陕西西安 710126
- 作者简介：
  
  [ "肖怡心(1999—),女,西安电子科技大学硕士研究生,E-mail:[email protected]; " ]
  [ "刘三阳(1959—),男,教授,E-mail:[email protected]" ]
- 基金信息：
  
  国家自然科学基金(61877046);国家自然科学基金(62106186);国家自然科学基金(12271419);陕西省自然科学基础研究计划(2022JQ-620);中央高校基本科研基金(JB210701);中央高校基本科研基金(XJS220709);中央高校基本科研基金(QTZX23002)
- DOI：10.19665/j.issn1001-2400.20230602
  中图分类号： O29
- 纸质出版日期：2024-4-20，
  
  网络出版日期：2023-9-18，
  
  收稿日期：2023-3-8，
扫描看全文
肖怡心, 刘三阳. 融合模式搜索的蝗虫优化算法及其应用[J]. 西安电子科技大学学报, 2024,51(2):137-156.

Yixin XIAO, Sanyang LIU. Integration of pattern search into the grasshopper optimization algorithm and its applications[J]. Journal of Xidian University, 2024,51(2):137-156.
肖怡心, 刘三阳. 融合模式搜索的蝗虫优化算法及其应用[J]. 西安电子科技大学学报, 2024,51(2):137-156. DOI： 10.19665/j.issn1001-2400.20230602.

Yixin XIAO, Sanyang LIU. Integration of pattern search into the grasshopper optimization algorithm and its applications[J]. Journal of Xidian University, 2024,51(2):137-156. DOI： 10.19665/j.issn1001-2400.20230602.

摘要

在智能优化算法应用于复杂优化问题的求解过程中

平衡开发和探索以获得最优解具有重要意义。因此针对传统蝗虫优化算法在处理一些较为复杂的优化问题时出现的收敛精度低、搜索能力弱且容易陷入局部最优等缺陷

提出一种融合模式搜索的蝗虫优化算法。首先引入Sine混沌映射初始化蝗虫个体种群位置

减少个体重叠概率以增强种群迭代初期的多样性;其次利用模式搜索法

对种群目前找到的最优目标展开局部搜索

提高算法的收敛速度与寻优精度;同时为了避免算法后期陷入局部最优

引入了基于凸透镜成像的反向学习策略。实验部分通过对改进的蝗虫算法进行消融实验

验证了Sine 混沌映射、模式搜索、反向学习每个策略的独立有效性。并用两组测试函数进行仿真实验

采用Wilcoxon秩和检验、Friedman 检验的方法进行结果分析。实验结果均表明了融合模式搜索法改进的蝗虫算法在收敛速度与寻优精度上得到明显提高。最后

将其应用于移动机器人路径规划

测试结果进一步验证了改进算法的有效性。

Abstract

In the process of applying intelligent optimization algorithms to solve complex optimization problems

balancing exploration and exploitation is of great significance in order to obtain optimal solutions.Therefore

this paper proposes a grasshopper optimization algorithm that integrates pattern search to address the limitations of traditional grasshopper optimization algorithm

such as low convergence accuracy

weak search capability

and susceptibility to local optima in handling complex optimization problems.First

a Sine chaotic mapping is introduced to initialize the positions of individual grasshopper population

reducing the probability of individual overlap and enhancing the diversity of the population in the early iterations.Second

the pattern search method is employed to perform local search for the currently found optimal targets in the population

thereby improving the convergence speed and optimization accuracy of the algorithm.Additionally

to avoid falling into local optima in the later stages of the algorithm

a reverse learning strategy based on the imaging of convex lenses is introduced.In the experimental section

a series of ablative experiments is conducted on the improved grasshopper algorithm to validate the independent effectiveness of each strategy

including the Sine chaotic mapping

pattern search

and reverse learning.Simulation experiments are performed on two sets of test functions

with the results analyzed using the Wilcoxon rank-sum test and Friedman test.Experimental results consistently demonstrate that the fusion mode search strategy improved grasshopper algorithm exhibits significant enhancements in both convergence speed and optimization accuracy.Furthermore

the application of the improved algorithm to mobile robot path planning further validates its effectiveness.

关键词

蝗虫优化算法粒子群优化算法模式搜索时间复杂度统计检验路径规划

Keywords

grasshopper optimization algorithmparticles warm optimization algorithmpattern searchtime complexitystatistical testpath planning

references

KENNEDY J, EBERHART R. Particle Swarm Optimization[C]//Icnn95-International Conference on Neural Networks. Piscataway:IEEE, 1995:1942-1948.

CHEN A. How to Prove the Optimized Values of Hyperparameters for Particle Swarm Optimization(2023)[J/OL].[2023-02-01]. https://arxiv.org/abs/2302.00155. https://arxiv.org/abs/2302.00155https://arxiv.org/abs/2302.00155

闫群民, 马瑞卿, 马永翔, 等. 一种自适应模拟退火粒子群优化算法[J]. 西安电子科技大学学报, 2021, 48(4):120-127.

YAN Qunmin, MA Ruiqing, MA Yongxiang, et al. Adaptive Simulated Annealing Particle Swarm Optimization Algorithm[J]. Journal of Xidian University, 2021, 48(4):120-127.

MA L, ZHU Y, ZHANG D, et al. A Hybrid Approach to Artificial Bee Colony Algorithm[J]. Neural Computing and Applications, 2016,27:387-409.

YE M, CAI Y, QIU H, et al. A Hybrid Artificial Bee Colony Algorithm to Solve a New Minimum Exposure Path Problem with Various Boundary Conditions for Wireless Sensor Networks[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2022, 36(2):2159056.

HORING M H, LIOU R J. Multilevel Minimum Cross Entropy Threshold Selection Based on the Firefly Algorithm[J]. Expert Systems with Applications, 2011, 38(12):14805-14811.

El-SHORBAGY M A. El-REFAEY A M. A Hybrid Genetic-Firefly Algorithm for Engineering Design Problems[J]. Journal of Computational Design and Engineering, 2022, 9(2):706-730.

MIRJALILI S, LEWIS A. The Whale Optimization Algorithm[J]. Advances in Engineering Software, 2016,95:51-67.

LI M, YU X, FU B, et al. AModified Whale Optimization Algorithm with Multi-Strategy Mechanism for Global Optimization Problems(2023)[J/OL].[2023-12-01].https://doi.org/10.1007/s00521-023-08287-5. https://doi.org/10.1007/s00521-023-08287-5https://doi.org/10.1007/s00521-023-08287-5

TUBISHAT M, IDRIS N, SHUIB L, et al. Improved Salp Swarm Algorithm Based on Opposition Based Learning and Novel Local Search Algorithm for Feature Selection[J]. Expert Systems with Applications, 2020,145:113122.

IKRAMR M A, DAI H L, EWEES A A, et al. Application of Improved Version of Multi Verse Optimizer Algorithm for Modeling Solar Radiation[J]. Energy Reports, 2022,8:12063-12080.

MAULIK U, BANDYOPADHYAY S. Genetic Algorithm-Based Clustering Technique[J]. Pattern Recognition, 2000, 33(9):1455-1465.

WOLPERT D H. The Lack of a Priori Distinctions Between Learning Algorithms[J]. Neural Computation, 1996, 8(7):1341-1390.

SAREMI S, MIRJALILI S, LEWIS A. Grasshopper Optimization Algorithm:Theory and Application[J]. Advances in Engineering Software, 2017,105:30-47.

VEZA I, KARAOGLAN A D, ILERI E, et al. Grasshopper Optimization Algorithm for Diesel Engine Fuelled with Ethanol-Biodiesel Diesel Blends[J]. Case Studies in Thermal Engineering, 2022.31:101817.

RAHMANU A E, KATOULI M. Diagnosing Lung Cancer Using Grasshopper Optimization Algorithm and K-Nearest Neighbor Classification[J]. Review of Computer Engineering Studies, 2019, 6(4):69-75.

HOSSEINY S, RAHMANI A, DERAKHSHAN M. Improve Intrusion Detection Using Grasshopper Optimization Algorithm and Decision Trees[J]. International Journal of Safety and Security Engineering, 2020, 10(3):359-364.

DWIWEDI S. Detecting Anonymous Attacks in Wireless Communication Medium Using Adaptive Grasshopper Optimization Algorithm[J]. Cognitive Systems Research, 2021,69:1-21.

何庆, 林杰, 徐航. 混合柯西变异和均匀分布的蝗虫优化算法[J]. 控制与决策, 2021, 36(7):1558-1568.

HE Qing, LIN Jie, XU Hang. Hybrid Cauchy Mutation and Uniform Distribution of Grasshopper Optimization Algorithm[J]. Control and Decision, 2021, 36(7):1558-1568.

EWEES A A, ELAZIZ M A, HOUSSEIN E H. Improved Grasshopper Optimization Algorithm Using Opposition-Based Learning[J]. Expert Systems with Applications, 2018,112:156-172.

FANG L, LIANG X. A Novel Method Based on Nonlinear Binary Grasshopper Whale Optimization Algorithm for Feature Selection[J]. Journal of Bionic Engineering, 2023,20:237-252.

WU L, WU J, WANG T. Enhancing Grasshopper Optimization Algorithm(GOA) with Levy Flight for Engineering Applications[J]. Scientific Reports, 2023, 13(124):1-49.

DENG L, LIU S. A Novel Hybrid Grasshopper Optimization Algorithm for Numerical and Engineering Optimization Problems[J]. Neural Processing Letters, 2023,55:9851-9905.

HVATTUM L M, GLOVER F. Finding Local Optima of High-Dimensional Functions Using Direct Search Methods[J]. European Journal of Operational Research, 2009, 195(1):31-45.

KANG F, LI J, LI H. Artificial Bee Colony Algorithm and Pattern Search Hybridized for Global Optimization[J]. Applied Soft Computing, 2013, 13(4):1781-1791.

BELAZI A, El-LATIF A A A, et al. A Simple Yet Efficient S-Box Method Based on Chaotic Sine Map[J]. Optik, 2017,130:1438-1444.

喻飞, 李元香, 魏波, 等. 透镜成像反学习策略在粒子群算法中的应用[J]. 电子学报, 2014, 42(2):230-235.

YU Fei, LI Yuanxiang, WEI Bo, et al. The Application of a Novel OBL Based on Lens Imaging Principle in PSO[J]. Acta Electronica Sinica, 2014, 42(2):230-235.

陈功, 曾国辉, 黄勃, 等. 融合互利共生和透镜成像学习的HHO算法[J]. 计算机工程与应用, 2022, 58(10):76-86. DOI:10.3778/j.issn.1002-8331.2106-0105http://doi.org/10.3778/j.issn.1002-8331.2106-0105

CHEN Gong, ZENG Guohui, HUANG Bo, et al. HHO Algorithm Combining Mutualism and Lens Imaging Learning[J]. Computer Engineering and Applications, 2022, 58(10):76-86. DOI:10.3778/j.issn.1002-8331.2106-0105http://doi.org/10.3778/j.issn.1002-8331.2106-0105

龙文, 伍铁斌, 唐明珠, 等. 基于透镜成像学习策略的灰狼优化算法[J]. 自动化学报, 2020, 46(10):2148-2164.

LONG Wen, WU Tiebin, TANG Mingzhu, et al. Grey Wolf Optimization Algorithm Based on Lens Imaging Learning Strategy[J]. Acta Automatica Sinica, 2020, 46(10):2148-2164.

AWAD N H, ALI M Z, SUGANTHAN P N, et al. Problem Definitions and Evaluation Criteria for the CEC2017 Special Session and Competition on Single Objective Real-Parameter Numerical[R]. Singapore: Nanyang Technological University, 2016.

ABDOLLAHZADEH B, GHAREHCHOPOGH F S, MIRJALILI S. African Vultures Optimization Algorithm:A New Nature-Inspired Metaheuristic Algorithm for Global Optimization Problems[J]. Computers & Industrial Engineering, 2021,158:107408.

TAHER M A, KAMEL S, JURADO F, et al. Modified Grasshopper Optimization Framework for Optimal Power Flow Solution[J]. Electrical Engineering, 2019,101:121-148.

TANABE R, FUKUNAGA A. Success-History Based Parameter Adaptation for Differential Evolution[C]//2013 IEEE Congress on Evolutionary Computation. Piscataway:IEEE, 2013: 71-78.

DERRAC J, GARCía S, MOLINA D, et al. A Practical Tutorial on The Use of Nonparametric Statistical Tests as a Methodology for Comparing Evolutionary and Swarm Intelligence Algorithms[J]. Swarm & Evolutionary Computation, 2011, 1(1):3-18.

ŽALIK K R. Cluster Validity Index for Estimation of Fuzzy Clusters of Different[J]. Pattern Recognition, 2010, 43(10):3374-3390.

YU J, SU Y, LIAO Y. The Path Planning of Mobile Robot by Neural Networks and Hierarchical Reinforcement Learning[J]. Frontiers in Neurorobotics, 2020,14:63.

DONG L, HE Z, SONG C, et al. A Review of Mobile Robot Motion Planning Methods:from Classical Motion Planning Workflows to Reinforcement Learning-Based Architectures[J]. Journal of Systems Engineering and Electronics, 2023, 34(2):439-459. DOI:10.23919/JSEE.2023.000051http://doi.org/10.23919/JSEE.2023.000051

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