基于四阶贝塞尔曲线和改进狮群优化算法求解路径规划问题

doi:10.13976/j.cnki.xk.2022.1173

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摘要首先，针对基础狮群算法中存在搜索效率低、多样性不足等问题，提出使用Sin混沌种群初始化操作提高算法初始解的质量，并引入调节因子，提高算法的多样性。其次，针对路径规划的问题，引入方向约束性函数，增加算法的搜索精度和收敛速度，同时提出双种群的狮群结构，通过差异化种群的相互协作提高算法的搜索能力，并运用四阶贝塞尔曲线，进行路径平滑处理。最后，经测试实验仿真，论证了改进狮群算法，相比基础狮群算法、灰狼算法、粒子群算法和遗传算法，在性能上有着显著提高。路径规划实验中，改进狮群算法规划出的路径较对比算法平均减少了5.67%，相比改进前的算法，运行时间减少了8.82%。

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	黄志锋
	刘媛华

关键词 ：狮群优化算法, 路径规划, 四阶贝塞尔曲线, 双种群, 调节因子

Abstract：The basic lion swarm algorithm is associated with low search efficiency and insufficient diversity. Thus, in this study, we propose a Sin chaotic population initialization operation to improve the quality of the initial solution of the algorithm. We also introduce an adjustment factor to improve the diversity of the algorithm. The directional constraint function increases the search accuracy and convergence rate of the algorithm, resolving the issue of path planning. We also propose the lion structure of two populations and improve the search ability of the algorithm through the mutual cooperation of differentiated populations. Path smoothing is achieved using the fourth-order Bessel curve. Finally, the improved lion swarm algorithm is demonstrated using test simulation. The performance of the proposed algorithm is significantly improved compared with basic lion swarm optimization, gray wolf optimization, particle swarm optimization, and genetic algorithms. Our findings show that the path planned by the improved lion swarm optimization is reduced by 5.67% on average, and the running time is reduced by 8.82% compared with the other studied algorithms.

Key words： lion swarm optimization (LSO)algorithm path planning fourth-order Bezier curve dual populations adjustment factor

收稿日期: 2022-04-24 出版日期: 2023-05-06

ZTFLH:

TP18

基金资助:国家自然科学基金(72071130)

通讯作者: 刘媛华,liuyuanhua@usst.edu.cn E-mail: liuyuanhua@usst.edu.cn

作者简介: 黄志锋(1998-)，男，硕士生。研究领域为智能算法优化，路径规划。
刘媛华(1974-)，女，博士，副教授，硕士生导师。研究领域为智能算法优化，系统工程，复杂系统理论方法与应用。

引用本文:

黄志锋, 刘媛华. 基于四阶贝塞尔曲线和改进狮群优化算法求解路径规划问题[J]. 信息与控制, 2023, 52(2): 176-189.
HUANG Zhifeng, LIU Yuanhua. Solving Path Planning Problem Based on Fourth-order Bezier Curve and Improved Lion Swarm Optimization Algorithm. Information and control, 2023, 52(2): 176-189.

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https://xk.sia.cn/CN/10.13976/j.cnki.xk.2022.1173 或 https://xk.sia.cn/CN/Y2023/V52/I2/176

[1] RUBIO F, VALERO F, LLOPIS-ALBERT C. A review of mobile robots:Concepts, methods, theoretical framework, and applications[J/OL]. International Journal of Advanced Robotic Systems. 2019[2022-01-16].http://www.researchgate.net/publication/332469850. DOI:10.1177/1729881419839596.
[2] HART P E, NILSSON N J, RAPHAEL B. A formal basis for the heuristic determination of minimum cost paths[J]. IEEE Transactions on Systems Science and Cybernetics, 1968, 4(2):100-107.
[3] 王洪斌, 尹鹏衡, 郑维, 等. 基于改进的A^*算法与动态窗口法的移动机器人路径规划[J]. 机器人, 2020, 42(3):346-353. WANG H B, YIN P H, ZHENG W, et al. Mobile robot path planning based on improved A^* algorithm and dynamic window method[J]. Robot, 2020, 42(3):346-353.
[4] 陈靖辉, 崔岩, 刘兴林, 等. 等基于改进A^*算法的移动机器人路径规划方法[J]. 计算机应用研究, 2020, 37(S1):118-119. CHEN J H, CUI Y, LIU X L, et al. Mobile robot path planning method based on improved A^* algorithm[J]. Computer Application Research, 2020, 37(S1):118-119.
[5] KHATIB O. Real-time obstacle avoidance for manipulators and mobile robots[M]//Autonomous Robot Vehicles. Berlin, Germany:Springer, 1986:396-404.
[6] 欧阳鑫玉, 杨曙光. 基于势场栅格法的移动机器人避障路径规划[J]. 控制工程, 2014, 21(1):134-137. OUYANG X Y, YANG S G.. Mobile robot obstacle avoidance path planning based on potential field grid method[J]. Control Engineering, 2014, 21(1):134-137.
[7] 赵明, 郑泽宇, 么庆丰, 等. 基于改进人工势场法的移动机器人路径规划方法[J]. 计算机应用研究, 2020, 37(S2):66-68, 72. ZHAO M, ZHENG Z Y, MO Q F, et al. Mobile robot path planning method based on improved artificial potential field method[J]. Computer Application Research, 2020, 37(S2):66-68, 72.
[8] 刘洋, 马建伟, 臧绍飞, 等. 基于融合Bezier优化遗传算法的路径规划[J]. 控制工程, 2021, 28(2):284-292. LIU Y, MA J W, ZANG S F, et al. Path planning based on Bezier optimized genetic algorithm[J]. Control Engineering, 2021, 28(2):284-292.
[9] 魏彤, 龙琛. 基于改进遗传算法的移动机器人路径规划[J]. 北京航空航天大学学报, 2020, 46(4):703-711. WEI T, LONG C. Mobile robot path planning based on improved genetic algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(4):703-711.
[10] 胡章芳, 程亮, 张杰, 等. 多约束条件下基于改进遗传算法的移动机器人路径规划[J]. 重庆邮电大学学报(自然科学版), 2021, 33(6):999-1006. HU Z F, CHENG L, ZHANG J, et al. Path planning of mobile robot based on improved genetic algorithm under multiple constraints[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2021, 33(6):999-1006.
[11] 陈嘉林, 魏国亮, 田昕. 改进粒子群算法的移动机器人平滑路径规划[J]. 小型微型计算机系统, 2019, 40(12):2550-2555. CHEN J L, WEI G L, TIAN X. Smooth path planning of mobile robot based on improved particle swarm optimization algorithm[J]. Small Microcomputer System, 2019, 40(12):2550-2555.
[12] MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69:46-61.
[13] 王永琦, 江潇潇. 基于混合灰狼算法的机器人路径规划[J]. 计算机工程与科学, 2020, 42(7):1294-1301. WANG Y Q, JIANG X X. Robot path planning based on hybrid grey wolf algorithm[J]. Computer Engineering and Science, 2020, 42(7):1294-1301.
[14] 游达章, 康亚伟, 刘攀, 等. 一种改进灰狼优化算法的移动机器人路径规划方法[J]. 机床与液压, 2021, 49(11):1-6. YOU D Z, KANG Y W, LIU P, et al. An improved grey wolf optimization algorithm for mobile robot path planning method[J]. Machine tool and Hydraulic, 2021, 49(11):1-6.
[15] 刘生建, 杨艳, 周永权. 一种群体智能算法——狮群算法[J]. 模式识别与人工智能, 2018, 31(5):431-441. LIU S J, YANG Y, ZHOU Y Q. A swarm intelligence algorithm-Lion swarm algorithm[J]. Pattern Recognition and Artificial Intelligence, 2018, 31(5):431-441.
[16] 汪婵婵. 基于改进狮群算法的汽轮机热耗率模型预测[J]. 计量学报, 2021, 42(7):853-860. WANG C C. Prediction of steam turbine heat consumption rate model based on improved lion swarm algorithm[J]. Metrology Report, 2021, 42(7):853-860.
[17] 王艳红, 张革文. 基于改进狮群算法的云计算资源调度策略[J]. 计算机应用与软件, 2021, 38(11):269-275. WANG Y H, ZHANG G W. Cloud computing resource scheduling strategy based on improved lion swarm algorithm[J]. Computer Application and Software, 2021, 38(11):269-275.
[18] 黄澄, 袁东风, 张海霞. 基于狮群算法的数字孪生车间调度问题优化[J]. 山东大学学报(工学版), 2021, 51(4):17-23, 34. HUANG C, YUAN D F, ZHANG H X. Optimization of digital twin shop scheduling problem based on lion group algorithm[J]. Journal of Shandong University (Engineering Edition), 2021, 51(4):17-23, 34.
[19] ZHAO F, LIU M, WANG K, et al. A soft measurement approach of wastewater treatment process by lion swarm optimizer-based extreme learning machine[J/OL]. Measurement, 2021[2021-11-30].http://www.zhangqiaokeyan.com/journal-foreign-detail/0704028818717. DOI:10.1016/measurement.2021.109322.
[20] LIU J, LI D, WU Y, et al. Lion swarm optimization algorithm for comparative study with application to optimal dispatch of cascade hydropower stations[J/OL]. Applied Soft Computing, 2020[2021-04-05]. http://www.sciencedirect.com/science/article/pii/s1568494679307550. DOI:10.1016/2020.105974.
[21] QIAO W, LU H, ZHOU G, et al. A hybrid algorithm for carbon dioxide emissions forecasting based on improved lion swarm optimizer[J/OL]. Journal of Cleaner Production, 2020[2021-06-05]. https://kd.nsfc.gov.cn/achievementsystem/isisn/detail/Page/25717f963427f55630165e5346de6bbc0. DOI:10.1016/2020.118612.
[22] DURAKLI Z, NABIYEV V. A new approach based on Bezier curves to solve path planning problems for mobile robots[J/OL]. Journal of Computational Science, 2022[2022-07-01].https://www.nstl.gov.cn/paper.detail.html?id=36e68c214f721cb6ac0e85f43ab1feab. DOI:10.1016/2022.101540.
[23] 张金炜, 王文扬, 郭蓬, 等. 基于蚁群四次贝塞尔曲线的无人车路径规划[J]. 现代电子技术, 2019, 42(13):113-116. ZHANG J W, WANG W Y, GUO P, et al. Unmanned vehicle path planning based on ant colony quartic Bezier curve[J]. Modern Electronic Technology, 2019, 42(13):113-116.
[24] 杨海东, 鄂加强. 自适应变尺度混沌免疫优化算法及其应用[J]. 控制理论与应用, 2009, 26(10):1069-1074. YANG H D, E J Q. Adaptive variable scale chaotic immune optimization algorithm and its application[J]. Control Theory and Application, 2009, 26(10):1069-1074.
[25] 赵燕伟, 吴斌, 蒋丽, 等. 车辆路径问题的双种群遗传算法求解方法[J]. 计算机集成制造系统, 2004(3):303-306. ZHAO Y W, WU B, JIANG L, et al. Solving method of bi-population genetic algorithm for vehicle routing problem[J]. Computer integrated Manufacturing System, 2004(3):303-306.