基于改进樽海鞘群算法求解工程优化设计问题

doi:10.16182/j.issn1004731x.joss.19-0645

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (4): 854-866.doi: 10.16182/j.issn1004731x.joss.19-0645

基于改进樽海鞘群算法求解工程优化设计问题

刘景森^1,2, 袁蒙蒙¹, 李煜^3,*

1.河南大学软件学院,河南开封 475004;
2.河南大学智能网络系统研究所,河南开封 475004;
3.河南大学管理科学与工程研究所,河南开封 475004

收稿日期:2019-12-11 修回日期:2020-02-20 出版日期:2021-04-18 发布日期:2021-04-14
通讯作者: 李煜(1969-),女,博士,教授,研究方向为智能算法、电子商务等。E-mail：leey@henu.edu.cn
作者简介:刘景森(1968-),男,博士,教授,研究方向为智能算法、优化控制、网络与信息安全等。E-mail：ljs@henu.edu.cn
基金资助:
河南省重点研发与推广专项(182102310886); 河南大学研究生教育创新与质量提升项目(SYL18060145,SYL19050104)

Solving Engineering Optimization Design Problems Based on Improved Salp Swarm Algorithm

Liu Jingsen^1,2, Yuan Mengmeng¹, Li Yu^3,*

1. College of Software, Henan University, Kaifeng 475004, China;
2. Institute of Intelligent Network System, Henan University, Kaifeng 475004, China;
3. Institute of Management Science and Engineering, Henan University, Kaifeng 475004, China

Received:2019-12-11 Revised:2020-02-20 Online:2021-04-18 Published:2021-04-14

摘要/Abstract

摘要： 为更好解决工程优化设计问题,改善樽海鞘群算法的寻优性能,提出一种引入有效缩放和随机交叉策略的自适应动态角色樽海鞘群算法。在领导者位置更新公式中引入帕累托分布和混沌映射,更有效地进行全局搜索;在全局和局部搜索的选择上,引入领导者—跟随者自适应调整策略,提高收敛精度;在局部搜索中引入随机交叉策略,增加种群多样性。将改进算法应用于不同典型复杂程度的工程优化问题中,测试结果表明：其寻优结果、问题适应性和求解稳定性优于其他算法。

关键词: 樽海鞘群算法, 帕累托分布函数, 混沌映射, 随机交叉策略, 自适应调整策略, 工程优化设计

Abstract: In order to better solve the engineering optimization design problem and improve the optimization performance of salp swarm algorithm, an adaptive dynamic role salp swarm algorithm with effective scaling and random crossover strategy is proposed. A pareto distribution and chaotic map are introduced into the leader position updating formula to make global search more efficient. In the selection of global and local search, a leader-follower adaptive adjustment strategy is introduced to improve the convergence accuracy. A random crossover strategy is introduced in local search to increase population diversity. The improved algorithm is applied to engineering optimization problems with different typical complexity. The test results show that its optimization results, problem adaptability and solution stability are better than other algorithms.

Key words: salp swarm algorithm, pareto distribution function, chaotic maps, random crossover strategy, adaptive adjustment strategy, engineering optimization design

中图分类号:

TP301.6
TB21

刘景森, 袁蒙蒙, 李煜. 基于改进樽海鞘群算法求解工程优化设计问题[J]. 系统仿真学报, 2021, 33(4): 854-866.

Liu Jingsen, Yuan Mengmeng, Li Yu. Solving Engineering Optimization Design Problems Based on Improved Salp Swarm Algorithm[J]. Journal of System Simulation, 2021, 33(4): 854-866.

参考文献

[1] Guedria N B.Improved Accelerated PSO Algorithm for Mechanical Engineering Optimization Problems[J]. Applied Soft Computing (S1568-4946), 2016, 40: 455-467.
[2] 鲍文, 李伟鹏, 常军涛, 等. 基于遗传算法的高超声速进气道自动优化设计[J]. 系统仿真学报, 2012, 24(4): 765-768.
Bao Wen, Li Weipeng, Chang Juntao, et al.Automatic Optimization Design of Hypersonic Inlet Based on Genetic Algorithm[J]. Journal of System Simulation, 2012, 24(4): 765-768.
[3] Kumar V, Kumar D.An Astrophysics-inspired Grey Wolf Algorithm for Numerical Optimization and its Application to Engineering Design Problems[J]. Advances in Engineering Software (S0965-9978), 2017, 112: 231-254.
[4] Mirjalili S, Lewis A.The Whale Optimization Algorithm[J]. Advances in Engineering Software (S0965-9978), 2016, 95: 51-67.
[5] Arora S, Singh S.Butterfly Optimization Algorithm: a Novel Approach for Global Optimization[J]. Soft Computing (S1432-7643), 2019, 23(3): 715-734.
[6] Mirjalili S, Gandomi A H, Mirjalili S Z.Salp Swarm Algorithm: A Bio-inspired Optimizer for Engineering Design Problems[J]. Advances in Engineering Software (S1873-5339), 2017, 114(6): 163-191.
[7] 陈涛, 王梦馨, 黄湘松. 基于樽海鞘群算法的无源时差定位[J]. 电子与信息学报, 2018, 40(7): 1591-1597.
Chen Tao, Wang Mengxin, Huang Xiangsong.Time Difference of Arrival Passive Location based on Salp Swarm Algorithm[J]. Journal of Electronics & Information Technology, 2018, 40(7): 1591-1597.
[8] 王梦秋, 王艳, 纪志成. 基于改进樽海鞘群算法的PMSM多参数辨识[J]. 系统仿真学报, 2018, 30(11): 4284-4291, 4297.
Wang Mengqiu, Wang Yan, Ji Zhicheng.Multi-parameter Identification of PMSM Based on Improved Salp Swarm Algorithm[J]. Journal of System Simulation, 2018, 30(11): 4284-4291, 4297.
[9] Faris H, Mafarja M M, Heidari A A, et al.An Efficient Binary Salp Swarm Algorithm with Crossover Scheme for Feature Selection Problems[J]. Knowledge-Based Systems (S0950-7051), 2018, 154: 43-67.
[10] Fathy A, Rezk H, Nassef A M.Robust Hydrogen-consumption-minimization Strategy Based Salp Swarm Algorithm for Energy Management of Fuel cell/supercapacitor/batteries in Highly Fluctuated Load Condition[J]. Renewable Energy (S0960-1481), 2019, 139: 147-160.
[11] Abbassi R, Abbassi A, Heidari A A, et al.An Efficient Salp Swarm-inspired Algorithm for Parameters Identification of Photovoltaic Cell Models[J]. Energy Conversion and Management (S0196-8904), 2019, 179: 362-372.
[12] Masdari M, Tahani M, Naderi M H, et al.Optimization of airfoil Based Savonius Wind Turbine Using Coupled Discrete Vortex Method and Salp Swarm Algorithm[J]. Journal of Cleaner Production(S0959-6526), 2019, 222: 47-56.
[13] Qais M H, Hasanien H M, Alghuwainem S.Enhanced Salp Swarm Algorithm: Application to Variable Speed Wind Generators[J]. Engineering Applications of Artificial Intelligence (S0952-1976), 2019, 80: 82-96.
[14] Kang F, Li J J, Dai J H.Prediction of Long-term Temperature Effect in Structural Health Monitoring of Concrete Dams Using Support Vector Machines with Jaya Optimizer and salp Swarm Algorithms[J]. Advances in Engineering Software (S0965-9978), 2019, 131: 60-76.
[15] Yang B, Zhong L E, Shu H C, et al.Novel Bio-inspired Memetic Salp Swarm Algorithm and Application to MPPT for PV Systems Considering Partial Shading condition[J]. Journal of Cleaner Production (S0959-6526), 2019, 215: 1203-1222.
[16] 邢致恺, 贾鹤鸣, 宋文龙. 基于莱维飞行樽海鞘群优化算法的多阈值图像分割[J]. 自动化学报, 2021, 47(2): 363-377.
Xing Zhikai, Jia Heming, Song Wenlong.Multi-threshold Image Segmentation Based on Levy Flying Salp Swarm Optimization Algorithm[J]. Journal of Automation, 2021, 47(2): 363-377.
[17] 张达敏, 陈忠云, 辛梓芸, 等. 基于疯狂自适应的樽海鞘群算法[J]. 控制与决策, 2020, 35(9): 2112-2120.
Zhang Damin, Chen Zhongyun, Xin Ziyun, et al.An Algorithm based on Crazy Adaptive Salp Swarm Algorithm[J]. Control and Decision, 2020, 35(9): 2112-2120.
[18] Wolpert D H, William G, Macready W G.“No Free Lunch Theorems for Optimization” IEEE Transactions on Evolutionary Computation[J]. Journal of Japan Society for Fuzzy Theory and Systems (S0915-647X), 1997, 1(1): 67-83.
[19] 孙鑫, 易开祥, 孙优贤. 基于混沌系统的图像加密算法[J]. 计算机辅助设计与图形学学报, 2002, 14(2): 136-139.
Sun Xin, Yi Kaixiang, Sun Youxian.Image Encryption Algorithm Based on Chaotic System[J]. Journal of Computer-Aided Design & Computer Graphics, 2002, 14(2): 136-139.
[20] Bu Y D, Gardner L.Laser-welded Stainless Steel I-section Beam-columns: Testing, Simulation and Design[J]. Engineering Structures (S0141-0296), 2019, 179: 23-36.
[21] Kabir M I, Bhowmick A K.Applicability of North American Standards for Lateral Torsional Buckling of Welded I-beams[J]. Journal of Constructional Steel Research(S0143-974X), 2018, 147: 16-26.
[22] Zu L, Xu H, Zhang Q, et al.Design of Filament-wound Spherical Pressure Vessels Based on Non-geodesic Trajectories[J]. Composite Structures (S0263-8223), 2019, 218: 71-78.
[23] Lee J, Jo C, Chang D, et al.New Methodology for Estimating the Minimum Design Vapor Pressure of Prismatic Pressure Vessel for On-ship Application[J]. Ocean Engineering (S0029-8018), 2018, 158: 367-377.
[24] Brütting J, Desruelle J, Senatore G, et al.Design of Truss Structures Through Reuse[J]. Structures (S2352-0124), 2019, 18: 128-137.
[25] 周克民, 李霞. 三杆类桁架材料模型多工况最小柔度优化[J]. 工程力学, 2011, 28(10): 16-20.
Zhou Kemin, Li Xia.Optimization of Minimum Compliance for Multi-case Conditions of Three-bar truss Material Model[J]. Engineering Mechanics, 2011, 28(10): 16-20.

基于改进樽海鞘群算法求解工程优化设计问题

Solving Engineering Optimization Design Problems Based on Improved Salp Swarm Algorithm

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