模拟植物生长算法的结构优化新机制
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摘要
作为一种新型启发式智能优化算法,模拟植物生长算法(PGSA)建立以植物向光性机理为基础的生长动力模型,以形成向全局最优解迅速生长的搜索机制.针对大规模复杂优化问题中生长空间大、设计变量多、可能存在多个局部最优解、算法难以自动终止等特点,基于PGSA基本原理,提出了3种新的算法改进机制——可生长点集合限定机制、新增可生长点剔除机制以及混合步长并行搜索机制,并通过典型数学和桁架结构算例分析对提出的改进算法的效果进行验证.结果表明:可生长点集合限定机制能有效控制生长空间规模,具有较强的局部搜索能力;新增可生长点剔除机制通过与前者的结合,为PGSA提供了有效的算法终止机制;混合步长并行搜索机制在生长前期便具备优异的全局搜索能力,能快速获取到最优解范围.所提出的新机制显著提升了PGSA算法优化的有效性及适应性,从而为结构优化问题提供了新思路.
As a new heuristic intelligent optimization algorithm,plant growth simulation algorithm(PGSA) esta-blishes the dynamic growth model based on the plant phototropism mechanism and forms the search mechanism rapidly towards the global optimal solution.According to the characteristics of large-scale complex optimization problems such as large growth space,multiple design variables,multiple local optimal solutions,difficulty in automatic termination and so on,three novel improved mechanisms(the limited strategy of growth point set,the elimination strategy of new growth points,the parallel search strategy of mixed step size) were proposed based on the basic principle of PGSA and the effectiveness of the proposed improved mechanisms were proved by typical mathematic example and structural example of truss.Several conclusions can be drawn:(1) the scale of growth space can be effectively controlled by the limited strategy of growth point set,which leads to high search capacity of the algorithm;(2) combined with the former strategy,the elimination strategy of new growth points can provide effective termination mechanism for PGSA;(3) excellent global search capacity can be offered by the parallel search strategy of mixed step size in the early growth stage and the optimal solution range can be quickly obtained.The proposed mechanisms can dramatically improve the effectiveness and adaptability of PGSA in optimization,which provides a new approach for structural optimization problems.
As a new heuristic intelligent optimization algorithm,plant growth simulation algorithm(PGSA) esta-blishes the dynamic growth model based on the plant phototropism mechanism and forms the search mechanism rapidly towards the global optimal solution.According to the characteristics of large-scale complex optimization problems such as large growth space,multiple design variables,multiple local optimal solutions,difficulty in automatic termination and so on,three novel improved mechanisms(the limited strategy of growth point set,the elimination strategy of new growth points,the parallel search strategy of mixed step size) were proposed based on the basic principle of PGSA and the effectiveness of the proposed improved mechanisms were proved by typical mathematic example and structural example of truss.Several conclusions can be drawn:(1) the scale of growth space can be effectively controlled by the limited strategy of growth point set,which leads to high search capacity of the algorithm;(2) combined with the former strategy,the elimination strategy of new growth points can provide effective termination mechanism for PGSA;(3) excellent global search capacity can be offered by the parallel search strategy of mixed step size in the early growth stage and the optimal solution range can be quickly obtained.The proposed mechanisms can dramatically improve the effectiveness and adaptability of PGSA in optimization,which provides a new approach for structural optimization problems.
引文
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[2] LI Tong,Wang Zhong-tuo.Application of plant growth simulation algorithm on solving facility location problem [J].Systems Engineering-Theory & Practice Online,2008,28(12):107- 115.
[3] 郗莹,马良,戴秋萍.多目标旅行商问题的模拟植物生长算法求解 [J].计算机应用研究,2012,29(10):3733- 3735.XI Ying,MA Liang,DAI Qiu-ping.Plant growth simulation algorithm for multi-criteria travelling salesman [J].Application Research of Computers,2012,29(10):3733- 3735.
[4] LU Shi-lei,YU Shun-zheng.A fuzzy k-coverage approach for RFID network planning using plant growth simulation algorithm [J].Journal of Network & Computer Applications,2014,39(1):280- 291.
[5] 张瑞珍.基于改进模拟植物生长算法的双目标逆向物流选址研究 [D].太原:中北大学,2017.
[6] 吴俊秋,何迪.模拟植物生长算法及其改进研究 [J].通信技术,2016,49(12):1629- 1634.WU Jun-qiu,HE Di.Plant growth simulation algorithm and its improved algorithm [J].Communication Technology,2016,49(12):1629- 1634.
[7] 石开荣,阮智健,姜正荣,等.模拟植物生长算法的改进策略及桁架结构优化研究 [J].建筑结构学报,2018,39(1):120- 128.SHI Kai-rong,RUAN Zhi-jian,JIANG Zheng-rong,et al.Improved strategies of plant growth simulation algorithm and optimization of truss structures [J].Journal of Buil-ding Structures,2018,39(1):120- 128.
[8] SHI Kai-rong,RUAN Zhi-jian,JIANG Zheng-rong,et al.Improved plant growth simulation & genetic hybrid algorithm(PGSA-GA)and its structural optimization [J].Engineering Computations,2018,35(1):268- 286.
[9] 阮智健.基于改进PGSA的预应力钢结构优化设计及其拉索索力识别方法研究 [D].广州:华南理工大学,2015.
[10] 陈前.基于改进的模拟植物生长算法的弦支穹顶结构优化设计研究及其抗震性能分析 [D].广州:华南理工大学,2013.
[11] 林全攀.弦支穹顶结构找力优化方法及施工仿真分析 [D].广州:华南理工大学,2018.
[12] 吕俊锋.基于改进PGSA的高层悬挂结构优化设计方法及施工模拟分析 [D].广州:华南理工大学,2018.
[13] 丁雪枫,马良,丁雪松.基于模拟植物生长算法的易腐物品物流中心选址 [J].系统工程,2009,27(2):96- 101.DING Xue-feng,MA Liang,DING Xue-song.The location allocation of logistics center of perishable products based on plant growth simulation algorithm [J].Systems Engineering,2009,27(2):96- 101.
[14] 孔令飞,王淳,熊云,等.模拟植物多向生长的配电网重构算法 [J].电测与仪表,2016,53(24):1- 5.KONG Ling-fei,WANG Chun,XIONG Yun,et.al.Plant multi-direction growth simulation algorithm for the distribution network reconfiguration [J].Electrical Measurement & Instrumentation,2016,53(24):1- 5.
[15] 丁雪枫,尤建新,王洪丰.突发事件应急设施选址问题的模型及优化算法 [J].同济大学学报(自然科学版),2012,40(9):1428- 1433.DING Xue-feng,YOU Jian-xin,WANG Hong-feng.A model of emergency rescue facility location problem for emergency incidents and its optimal solution algorithm [J].Journal of Tongji University(Natural science),2012,40(9):1428- 1433.
[16] BHATTACHARJEE D,PAUL A,KIM J H,et al.An object localization optimization technique in medical images using plant growth simulation algorithm [J].Springerplus,2016,5(1):1784.
[17] BHATTACHARJEE D,PAUL A.A leukocyte detection technique in blood smear images using plant growth simu-lation algorithm [C]//Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence.San Francisco:AAAI Press,2017,17- 23.
[18] RAO R S,NARASIMHAM S V L,RAMALINGARAJU M.Optimal capacitor placement in a radial distribution system using Plant Growth Simulation Algorithm [J].International Journal of Electrical Power & Energy Systems,2011,33(5):1133- 1139.
[19] 李彤,王众托.大型城市地下物流网络优化布局的模拟植物生长算法 [J].系统工程理论与实践,2013,33(4):971- 980.LI Tong,WANG Zhong-tuo.Optimization layout of underground logistics network in big cities with plant growth simulation algorithm [J].Systems Engineering-Theory & Practice,2013,33(4):971- 980.
[20] 曹庆奎,刘新雨,任向阳.基于模拟植物生长算法的车辆调度问题 [J].系统工程理论与实践,2015,35(6):1449- 1456.CAO Qing-kui,LIU Xin-yu,REN Xiang-yang.Vehicle scheduling problem based on plant growth simulation algorithm [J].Systems Engineering-Theory & Practice,2015,35(6):1449- 1456.
[21] 陈立华,梅亚东.模拟植物生长算法在水库群优化调度中的应用 [J].水电与抽水蓄能,2010,34(2):1- 5.CHEN Li-hua,MEI Ya-dong.Plant growth simulation algorithm and its application in optimal operation of cascade reservoirs [J].Hydropower Automation and Dam Monitoring,2010,34(2):1- 5.
[22] 王军,张强,王国勋.基于模拟植物生长算法的平面度误差评定 [J].机械设计与制造,2015(7):8- 10.WANG Jun,ZHANG Qiang,WANG Guo-xun.Flatness error evaluation based on plant growth simulation algorithm [J].Machinery Design & Manufacture,2015(7):8- 10.
[23] 李永梅,张毅刚.离散变量结构优化的2级算法 [J].北京工业大学学报,2006,32(10):883- 889.LI Yong-mei,ZHANG Yi-gang.A 2-level optimization structural design algorithm on discrete variables [J].Journal of Beijing University of Technology,2006,32(10):883- 889.
[24] 孙焕纯,柴山,王跃方.离散变量结构优化设计 [M].大连:大连理工大学出版社,1995.
[25] SCHMIT L A,MIURA H.An advanced structural analysis/ synthesis capability;ACCESS 2 [J].International Journal for Numerical Methods in Engineering,1978,12(2):353- 377.