Improved Wolf Pack Algorithm for Optimum Design of Truss Structures
In order to find a more effective method in structural optimization, an improved wolf pack optimization algorithm was proposed. In the traditional wolf pack algorithm, the problem of falling into local optimum and low precision often occurs. Therefore, the adaptive step size search and Levy's flight strategy theory were employed to overcome the premature flaw of the basic wolf pack algorithm. Firstly, the reasonable change of the adaptive step size improved the fineness of the search and effectively accelerated the convergence speed. Secondly, the search strategy of Levy's flight was adopted to expand the search scope and improved the global search ability of the algorithm. At last, to verify the performance of improved wolf pack algorithm, it was tested through simulation experiments and actual cases, and compared with other algorithms. Experiments show that the improved wolf pack algorithm has better global optimization ability. This study provides a more effective solution to structural optimization problems.
Mayencourt, Paul, and Caitlin Mueller. “Hybrid Analytical and Computational Optimization Methodology for Structural Shaping: Material-Efficient Mass Timber Beams.” Engineering Structures 215 (July 2020): 110532. doi:10.1016/j.engstruct.2020.110532.
Baghdadi, Abtin, Mahmoud Heristchian, and Harald Kloft. “Design of Prefabricated Wall-Floor Building Systems Using Meta-Heuristic Optimization Algorithms.” Automation in Construction 114 (June 2020): 103156. doi:10.1016/j.autcon.2020.103156.
Beheshti, Zahra, and Siti Mariyam Shamsuddin. “Non-Parametric Particle Swarm Optimization for Global Optimization.” Applied Soft Computing 28 (March 2015): 345–359. doi:10.1016/j.asoc.2014.12.015.
Li, Wenjing, Yingzhou Bi, Xiaofeng Zhu, Chang-an Yuan, and Xiang-bo Zhang. “Hybrid Swarm Intelligent Parallel Algorithm Research Based on Multi-Core Clusters.” Microprocessors and Microsystems 47 (November 2016): 151–160. doi:10.1016/j.micpro.2016.05.009.
Ma, Lianbo, Yunlong Zhu, Dingyi Zhang, and Ben Niu. “A Hybrid Approach to Artificial Bee Colony Algorithm.” Neural Computing and Applications 27, no. 2 (March 13, 2015): 387–409. doi:10.1007/s00521-015-1851-x.
Jahangiri, Milad, Mohammad Ali Hadianfard, Mohammad Amir Najafgholipour, Mehdi Jahangiri, and Mohammad Reza Gerami. “Interactive Autodidactic School: A New Metaheuristic Optimization Algorithm for Solving Mathematical and Structural Design Optimization Problems.” Computers & Structures 235 (July 2020): 106268. doi:10.1016/j.compstruc.2020.106268.
John H. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press, New York (1975).” Bulletin of Mathematical Biology 38, no. 2 (1976): 211–214. doi:10.1016/s0092-8240(76)80036-5.
VaeziNejad, SeyedMahmood, SeyedMorteza Marandi, and Eysa Salajegheh. “A Hybrid of Artificial Neural Networks and Particle Swarm Optimization Algorithm for Inverse Modeling of Leakage in Earth Dams.” Civil Engineering Journal 5, no. 9 (September 23, 2019): 2041–2057. doi:10.28991/cej-2019-03091392.
Dorigo, M., V. Maniezzo, and A. Colorni. “Ant System: Optimization by a Colony of Cooperating Agents.” IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 26, no. 1 (1996): 29–41. doi:10.1109/3477.484436.
Li, Xiao-lei. "An optimizing method based on autonomous animats: fish-swarm algorithm." Systems Engineering-Theory & Practice 22, no. 11 (2002): 32-38.
Karaboga, Dervis. An idea based on honey bee swarm for numerical optimization. Vol. 200. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
O’Neil, Michael, Franco Woolfe, and Vladimir Rokhlin. “An Algorithm for the Rapid Evaluation of Special Function Transforms.” Applied and Computational Harmonic Analysis 28, no. 2 (March 2010): 203–226. doi:10.1016/j.acha.2009.08.005.
Pan, Wen-Tsao. “A New Fruit Fly Optimization Algorithm: Taking the Financial Distress Model as an Example.” Knowledge-Based Systems 26 (February 2012): 69–74. doi:10.1016/j.knosys.2011.07.001.
Yang, Xin-She. “Flower Pollination Algorithm for Global Optimization.” Lecture Notes in Computer Science (2012): 240–249. doi:10.1007/978-3-642-32894-7_27.
Meng, Xianbing, Yu Liu, Xiaozhi Gao, and Hengzhen Zhang. “A New Bio-Inspired Algorithm: Chicken Swarm Optimization.” Advances in Swarm Intelligence (2014): 86–94. doi:10.1007/978-3-319-11857-4_10.
Wu, H. S., Fengming Zhang, and Lushan Wu. "New swarm intelligence algorithm-wolf pack algorithm." Systems engineering and electronics 35, no. 11 (2013): 2430-2438.
Yang, Chenguang, Xuyan Tu, and Jie Chen. “Algorithm of Marriage in Honey Bees Optimization Based on the Wolf Pack Search.” The 2007 International Conference on Intelligent Pervasive Computing (IPC 2007) (October 2007). doi:10.1109/ipc.2007.104.
Wu, Chenghai, Kaiyu Qin, Penghui He, and Houbiao Li. “An Improved Wolf Colony Search Algorithm Based on Mutual Communication by a Sensor Perception of Wireless Networking.” EURASIP Journal on Wireless Communications and Networking 2018, no. 1 (June 15, 2018). doi:10.1186/s13638-018-1171-9.
Husheng, Wu, and Zhang Fengming. “A Uncultivated Wolf Pack Algorithm for High-Dimensional Functions and Its Application in Parameters Optimization of PID Controller.” 2014 IEEE Congress on Evolutionary Computation (CEC) (July 2014). doi:10.1109/cec.2014.6900432.
Teng, Zhi-jun, Jin-ling Lv, and Li-wen Guo. “An Improved Hybrid Grey Wolf Optimization Algorithm.” Soft Computing 23, no. 15 (June 25, 2018): 6617–6631. doi:10.1007/s00500-018-3310-y.
Chen, Xiayang, Chaojing Tang, Jian Wang, Lei Zhang, and Qingkun Meng. “Improved Wolf Pack Algorithm Based on Differential Evolution Elite Set.” IEICE Transactions on Information and Systems E101.D, no. 7 (July 1, 2018): 1946–1949. doi:10.1587/transinf.2017edl8201.
Zhang, Y., Sun, H., Wei, Z. and Han, B. “Chaos grey wolf optimization algorithm with adaptive adjustment strategy”. Computer Science, Vol. 44, No. 11, (2017):119-123.
Kaveh, A., and P. Zakian. “Improved GWO Algorithm for Optimal Design of Truss Structures.” Engineering with Computers 34, no. 4 (November 30, 2017): 685–707. doi:10.1007/s00366-017-0567-1.
Mo, Y., Nie, H., Liu, Z. and Yang, H. “Grey wolf optimization algorithm based on levy flight”. Microelectronics and Computer, Vol. 36, No. 4, (2019):78-83.
Guo, L. “Improved wolf pack algorithm based on adaptive and variable wandering direction”. Journal of Zhejiang University, Vol. 45, No. 3, (2018):284-293.
Tang, Qin, Yi Shen, Chengyu Hu, Jianyou Zeng, and Wenyin Gong. “Swarm Intelligence: Based Cooperation Optimization of Multi-Modal Functions.” Cognitive Computation 5, no. 1 (April 28, 2012): 48–55. doi:10.1007/s12559-012-9144-5.
Parpinelli, Rafael S., Fábio R. Teodoro, and Heitor S. Lopes. “A Comparison of Swarm Intelligence Algorithms for Structural Engineering Optimization.” International Journal for Numerical Methods in Engineering 91, no. 6 (May 30, 2012): 666–684. doi:10.1002/nme.4295.
Caamaño, Pilar, Francisco Bellas, Jose A. Becerra, and Richard J. Duro. “Evolutionary Algorithm Characterization in Real Parameter Optimization Problems.” Applied Soft Computing 13, no. 4 (April 2013): 1902–1921. doi:10.1016/j.asoc.2013.01.002.
Wu, J. H., Jing Zhang, R. F. Li, and C. H. Liu. "A Multi-Subpopulation PSO Immune Algorithm and Its Application on Function Optimization." Journal of Computer Research and Development 49, no. 9 (2012): 1883-1898.
Wu, Hu-Sheng, and Feng-Ming Zhang. “Wolf Pack Algorithm for Unconstrained Global Optimization.” Mathematical Problems in Engineering 2014 (2014): 1–17. doi:10.1155/2014/465082.
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