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文章荐读 | 基于植物种群空间分布格局的群体无人机目标搜索算法

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文章荐读 | 基于植物种群空间分布格局的群体无人机目标搜索算法

小编导读

来自安徽大学的张晓明博士和青海科学与技术信息研究所的学者们在期刊 International Journal of Computional Intelligence Systems(IJCIS)上发表了题为“A Novel Target Searching Algorithm for Swarm UAVs Inspired From Spatial Distribution Patterns of Plant Population” 的文章,研究了基于植物种群空间分布格局的群体无人机目标搜索算法。

要点介绍

与某些社会性动物群体一样,自然界中植物种群的分布演化中也蕴含群体智能。针对无人机集群在复杂未知环境下的目标搜索问题,开展了植物种群空间分布格局演化的研究,探讨了适合无人机集群的搜索模型,提出了一种基于柯西分布和正态分布相结合的群体机器人目标搜索算法(CRBOA),包括构建基于Cauchy分布的目标搜索模型,结合调度控制策略和自由运动空间机制,代替传统正态分布模型,提高了群体机器人的全局搜索能力和搜索效率。同时,为了解决在精细搜索阶段时负二项分布可用性差的问题,该算法用小方差正态分布代替负二项分布,在保证精细搜索性能的同时,提高了模型的可用性。通过与RBOA和自适应机器人粒子群优化算法(A-RPSO)的对比实验表明,CRBOA算法具有优秀的目标搜索性能和稳定性,在全局搜索与局部精细搜索之间取得了很好的平衡。

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参考文献

[1]  S. Garnier, J. Gautrais, G. Theraulaz, The biological principles of swarm intelligence, Swarm Intell. 1 (2007), 3–31.

[2]  E. Osaba, J. Del Ser, A. Iglesias, X.-S. Yang, Soft computing for swarm robotics: new trends and applications, J. Comput. Sci. 39 (2020), 101049.

[3]  L. Bayındır, A review of swarm robotics tasks, Neurocomputing. 172 (2016), 292–321.

[4]  B.Wang,W.Chen,J.Wang,B.Zhang,P.Shi,Semi-globaltracking cooperative control for multi-agent systems with input saturation: a multiple saturation levels framework, IEEE Trans. Automat. Contr. (2020).

[5]  J.Li,Y.Tan,Aprobabilisticfinitestatemachinebasedstrategyfor multi-target search using swarm robotics, Appl. Soft. Comput. 77 (2019), 467–483.

[6]  M. Bakhshipour, M.J. Ghadi, F. Namdari, Swarm robotics search & rescue: a novel artificial intelligence-inspired optimization approach, Appl. Soft. Comput. 57 (2017), 708–726.

[7]  M.S. Innocente, P. Grasso, Self-organising swarms of firefight- ing drones: harnessing the power of collective intelligence in decentralised multi-robot systems, J. Comput. Sci. 34 (2019), 80–101.

[8]  A. Scheidler, A. Brutschy, E. Ferrante, M. Dorigo, The k-Unanimity rule for self-organized decision-making in swarms of robots, IEEE Trans. Cybernetics. 46 (2016), 1175–1188.

[9]  P. Calvo, M. Gagliano, G.M. Souza, A. Trewavas, Plants are intel- ligent, here’s how, Ann. Bot. 125 (2020), 11–28.

[10]  D.A. Chamovitz, Plants are intelligent now what?, Nat. Plants. 4 (2018), 622–623.

[11]  X. Zhang, M. Ali, A bean optimization-based cooperation method for target searching by swarm UAVs in unknown environments, IEEE Access. 8 (2020), 43850–43862.

[12]  H. Li, Introduction to studies of the pattern of plant population, Chin. Bull. Bot. 12 (1995), 19–26.

[13]  Z. He, Spatial distribution patterns and association of two Apocy- naceae plants in the tropical mountain rainforests of Jianfengling, Biodivers. Sci. 25 (2017), 1065–1074.

[14]  Y. Guo, B. Wang, W. Xiang, T. Ding, S.H. Lu, Y. Huang, P. Huang, D. Li, X. Li, Spatial distribution of tree species in a tropical karst seasonal rainforest in Nonggang, Biodivers. Sci. 23 (2015), 183–191.

[15]  E.J.B. McIntire, A. Fajardo, Beyond description: the active and effective way to infer processes from spatial patterns, Ecology. 90 (2009), 45–56.

[16]  C.V.S. Gunatilleke, I.A.U.N. Gunatilleke, S. Esufali, K.E. Harms, P.M.S. Ashton, D.F.R.P. Burslem, P.S. Ashton, Species-habitat associations in a Sri Lankan dipterocarp forest, J. Trop. Ecol. 22 (2006), 371–384.

[17]  J. Lai, X. Mi, H. Ren, K. Ma, Species-habitat associations change in a subtropical forest of China, J. Veg. Sci. 20 (2009), 415–423.

[18]  X. Yang, Y. Han, L. Li, X. Chen, J. You, The effect of heteroge- neous spatial distribution of soil nitrogen on regeneration of Larix principis-rupprechtii seedlings in typical naturally-regenerated montane forests of Northern China, Acta Ecologica Sin. 29 (2009), 4656–4664.

[19]  S. Liu, J. Li, D. Li, K.W. Zhu, R. Guo, Y. Wen, Z. Ma, Compari- son and adaptability of analytical methods for spatial distribution patterns in forest, Scientia Silvae Sin. 55 (2019), 73–84.

[20]  H. Yan, Spatial Distribution Patterns of Tree Species and Their Correlation with Habitat in Secondary Cold-warm Picea Forest in Guandi Mountain, PhD Dissertation, Shanxi Agricultural Uni- versity, Taigu, China, 2018.

[21]  M. Rao, G. Feng, J. Zhang, X. Mi, J.-H. Chen, Effects of environ- mental filtering and dispersal limitation on species and phyloge- netic beta diversity in Gutianshan National Nature Reserve, Chin. Sci. Bull. 13 (2013), 44–52.

[22] B.M.J. Engelbrecht, L.S. Comita, R. Condit, T.A. Kursar, M.T. Tyree, B.L. Turner, S.P. Hubbell, Drought sensitivity shapes species distribution patterns in tropical forests, Nature. 447 (2007), 80–82.

[23] P. Legendre, X. Mi, H. Ren, K. Ma, M. Yu, I. Sun, F. He, Partition- ing beta diversity in a subtropical broad-leaved forest of China, Ecology. 90 (2009), 663–674.

[24] T. Wiegand, K.A. Moloney, Rings, circles, and null-models for point pattern analysis in ecology, Oikos. 104 (2004), 209–229.

[25] X. Zhang, B. Sun, T. Mei, R. Wang, A novel evolutionary algorithm inspired by beans dispersal, Int. J. Comput. Int. Sys. 6 (2013), 79–86.

[26] X. Zhang, H. Wang, B. Sun, W. Li, R. Wang, The markov model of bean optimization algorithm and its convergence analysis, Int. J. Comput. Int. Sys. 6 (2013), 609–615.

[27] X. Zhang, T. Feng, Q. Niu, X. Deng, A novel swarm optimisa- tion algorithm based on a mixed-distribution model, Appl. Sci. 8 (2018), 632.

[28] X. Zhang, T. Feng, Chaotic bean optimization algorithm. Soft Comput. 22 (2018), 67–77.

[29] Q. Wu, Cauchy mutation for decision-making variable of Gaus- sian particle swarm optimization applied to parameters selection of SVM, Expert Syst. Appl. 38 (2011), 4929–4934.

[30] X. Li, X. Yao, Cooperatively coevolving particle swarms for large scale optimization, IEEE Trans. Evolut. Comput. 16 (2011), 210–224.

[31] M. Hu, T. Wu, J.D. Weir, An adaptive particle swarm optimization with multiple adaptive methods, IEEE Trans. Evolut. Comput. 17 (2012), 705–720.

[32] M. Dadgar, S. Jafari, A. Hamzeh, A PSO-based multi-robot coop- eration method for target searching in unknown environments, Neurocomputing. 177 (2016), 62–74.

[33] M.S. Couceiro, P.A. Vargas, R.P. Rocha, N.M.F. Ferreira, Bench- mark of swarm robotics distributed techniques in a search task, Robot Auton. Syst. 62 (2014), 200–213.

原文信息

X. Zhang, Y. Hu, T. Li, "A Novel Target Searching Algorithm for Swarm UAVs Inspired From Spatial Distribution Patterns of Plant Population", International Journal of Computational Intelligence systems, 2020, DOI: 10.2991/ijcis.d.201109.001.

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关于作者

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张晓明,男,博士,安徽大学副教授,研究生导师,研究方向为群体智能与智能决策。在群体智能领域提出并构建了基于植物种群分布演化的系列群体智能算法,在智能决策领域主持构建了基于知识超图的中科知识库管理系统和基于知识图谱的机器人任务规划与调度系统,获得省级科技成果5项,在专业学术刊物已发表论文30余篇,获授权发明专利4项。联系邮箱:xmzhang@ustc.edu, 247075139@qq.com

团队介绍:作者一直从事群体智能和智能机器人方面的研究和开发。受自然界植物种子传播方式和植物种群分布演化启发,团队构建并持续在开展基于植物种群分布演化的仿生算法和群体机器人研究,相关的研究论文已经发表在多个国内外知名专业学术期刊上,并多次在群体智能专业学术会议报告研究进展。部分实验的视频和介绍请参见:

2020328C-RBOA实验视频)https://share.weiyun.com/5VEJy2u

2020218RBOA实验视频)https://share.weiyun.com/5QhVuKx

关于期刊

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Impact Factor: 1.838, CiteScore: 3.59

International Journal of Computational Intelligence Systems(IJCIS)是欧洲模糊逻辑和技术学(EUSFLAT)会刊,主要刊载有关应用计算智能各个方面的原创性研究,尤其是针对证明使用了计算智能理论的技术和方法的研究型论文及综述等,由西班牙哈恩大学Luis Martínez Lopez教授和澳大利亚悉尼科技大学路节教授担任共同主编。本刊目前已被DOAJ, Science Citation Index Expanded (SCIE), Ei Compendex and Scopus等数据库收录。

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