An Improved Cuckoo Search Algorithm and Its Application in Robot Path Planning

被引:0
作者
Min, Wei [1 ]
Mo, Liping [2 ]
Yin, Biao [1 ]
Li, Shan [1 ]
机构
[1] Jishou Univ, Sch Commun & Elect Engn, Jishou 416000, Peoples R China
[2] Jishou Univ, Coll Comp Sci & Engn, Jishou 416000, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 20期
基金
中国国家自然科学基金;
关键词
cuckoo search algorithm; tent chaotic mapping; Levy flight; beetle antennae search algorithm; sine cosine algorithm; robot path planning; OPTIMIZATION;
D O I
10.3390/app14209572
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
This manuscript introduces an improved Cuckoo Search (CS) algorithm, known as BASCS, designed to address the inherent limitations of CS, including insufficient search space coverage, premature convergence, low search accuracy, and slow search speed. The proposed improvements encompass four main areas: the integration of tent chaotic mapping and random migration in population initialization to reduce the impact of random errors, the guidance of Levy flight by the directional determination strategy of the Beetle Antennae Search (BAS) algorithm during the global search phase to improve search accuracy and convergence speed, the adoption of the Sine Cosine Algorithm for local exploitation in later iterations to enhance local optimization and accuracy, and the adaptive adjustment of the step-size factor and elimination probability throughout the iterative process to convergence. The performance of BASCS is validated through ablation experiments on 10 benchmark functions, comparative experiments with the original CS and its four variants, and application to a robot path planning problem. The results demonstrate that BASCS achieves higher convergence accuracy and exhibits faster convergence speed and superior practical applicability compared to other algorithms.
引用
收藏
页数:25
相关论文
共 50 条
  • [21] Elite particle swarm optimization algorithm and its application in robot path planning
    Yan, Xue-Song
    Hu, Cheng-Yu
    Yao, Hong
    Wu, Qing-Hua
    Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2013, 21 (12): : 3160 - 3168
  • [22] Improved Particle Swarm Optimization Based on Cuckoo Search Operations and Its Application
    Tchapda, Ghislain Yanick Gninkeu
    Wang, Zenghui
    2017 2ND INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION ENGINEERING (ICRAE), 2017, : 290 - 294
  • [23] An Improved Cuckoo Search Algorithm for Parallel Machine Scheduling
    Laha, Dipak
    Behera, Dhiren Kumar
    SWARM, EVOLUTIONARY, AND MEMETIC COMPUTING, SEMCCO 2014, 2015, 8947 : 788 - 800
  • [24] ROBOT PATH PLANNING WITH A HYBRID ARITHMETIC OPTIMIZATION ALGORITHM
    Song, Qiang
    UNIVERSITY POLITEHNICA OF BUCHAREST SCIENTIFIC BULLETIN SERIES C-ELECTRICAL ENGINEERING AND COMPUTER SCIENCE, 2024, 86 (03): : 197 - 210
  • [25] Cuckoo Search Algorithm for the Mobile Robot Navigation
    Mohanty, Prases Kumar
    Parhi, Dayal R.
    SWARM, EVOLUTIONARY, AND MEMETIC COMPUTING, PT I (SEMCCO 2013), 2013, 8297 : 527 - 536
  • [26] AN IMPROVED ANT COLONY SYSTEM ALGORITHM FOR ROBOT PATH PLANNING AND PERFORMANCE ANALYSIS
    You, Xiao-Ming
    Liu, Sheng
    Zhang, Chen
    INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION, 2018, 33 (05) : 527 - 533
  • [27] An Improved Artificial Electric Field Algorithm for Robot Path Planning
    Tang, Jun
    Pan, Qingtao
    Chen, Zhishuai
    Liu, Gang
    Yang, Guoli
    Zhu, Feng
    Lao, Songyang
    IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2024, 60 (02) : 2292 - 2304
  • [28] Application of the Improved Cuckoo Algorithm in Differential Equations
    Sun, Yan
    MATHEMATICS, 2024, 12 (02)
  • [29] Quantum-inspired firefly algorithm integrated with cuckoo search for optimal path planning
    Kundra, Harish
    Khan, Wasim
    Malik, Meenakshi
    Rane, Kantilal Pitambar
    Neware, Rahul
    Jain, Vishal
    INTERNATIONAL JOURNAL OF MODERN PHYSICS C, 2022, 33 (02):
  • [30] An Improved Shuffled Frog Leaping Algorithm for Robot Path Planning
    Ni, Jianjun
    Yin, Xiahong
    Chen, Junfeng
    Li, Xinyun
    2014 10TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC), 2014, : 545 - 549