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Kinetic Mechanical Engineering, 2022, 3(4); doi: 10.38007/KME.2022.030405.

Establishment and Analysis of Assembly Sequence Planning Model of Construction Machinery Components Based on Ant Colony Algorithm

Author(s)

Yu Han

Corresponding Author:
Yu Han
Affiliation(s)

Shenyang Jinbei Vehicle Manufacturing Co., LTD, Shenyang, Liaoning, China

Abstract

Taking the assembly sequence of construction machinery as the research object, this paper analyzes the application of traditional heuristic algorithm in the path optimization problem based on ant colony, and the simulation results show that the method has good performance. This paper mainly aims at establishing the assembly sequence planning model of engineering mechanical components based on ant colony algorithm. First, the principle of genetic operation and basic operators are introduced, and then the genetic evolution process is simulated using Matlab software. After it is converted into the optimal solution, the desired number of solving parameters and time minimum characteristic curves are finally obtained to analyze the nature of the problem. Finally, the simulation results show that the data processing time and the shortest path planning time of the construction machinery assembly sequence planning model based on ant colony algorithm are within 10 seconds. This shows that the model meets the needs of users.

Keywords

Ant Colony Algorithm, Construction Machinery, Component Assembly, Planning Model

Cite This Paper

Yu Han. Establishment and Analysis of Assembly Sequence Planning Model of Construction Machinery Components Based on Ant Colony Algorithm. Kinetic Mechanical Engineering (2022), Vol. 3, Issue 4: 37-45. https://doi.org/10.38007/KME.2022.030405.

References

[1] Farouq Zitouni, Saad Harous, Ramdane Maamri:A Distributed Approach to the Multi-Robot Task Allocation Problem Using the Consensus-Based Bundle Algorithm and Ant Colony System. IEEE Access 8: 27479-27494 (2020). https://doi.org/10.1109/ACCESS.2020.2971585

[2] Breno Augusto De Melo Menezes, Nina Herrmann, Herbert Kuchen, Fernando Buarque de Lima Neto:High-Level Parallel Ant Colony Optimization with Algorithmic Skeletons. Int. J. Parallel Program. 49(6): 776-801 (2021). https://doi.org/10.1007/s10766-021-00714-1

[3] Mohammadreza Jelokhani-Niaraki, Najmeh Neysani Samany, Moslem Mohammadi, Ara Toomanian:A hybrid ridesharing algorithm based on GIS and ant colony optimization through geosocial networks. J. Ambient Intell. Humaniz. Comput. 12(2): 2387-2407 (2021). https://doi.org/10.1007/s12652-020-02364-6

[4] Mohammad Vafaei, Ahmad Khademzadeh, Mohammad Ali Pourmina:A New QoS AdaptiveMulti-path Routing for Video Streaming in Urban VANETs Integrating Ant Colony Optimization Algorithm and Fuzzy Logic. Wirel. Pers. Commun. 118(4): 2539-2572 (2021). https://doi.org/10.1007/s11277-021-08142-7

[5] Bilal Kanso, Ali Kansou, Adnan Yassine:Open Capacitated ARC routing problem by:Hybridized Ant Colony Algorithm. RAIRO Oper. Res. 55(2): 639-652 (2021). https://doi.org/10.1051/ro/2021034

[6] Abdullah Mughees, Syed Mohsin Ali:Design and Control of Magnetic Levitation System by Optimizing Fractional Order PID Controller Using Ant Colony Optimization Algorithm. IEEE Access 8: 116704-116723 (2020). https://doi.org/10.1109/ACCESS.2020.3004025

[7] Manisha Rathee, Sushil Kumar, Amir H. Gandomi, Kumar Dilip, Balamurugan Balusamy, Rizwan Patan:Ant Colony Optimization Based Quality of Service Aware Energy Balancing Secure Routing Algorithm for Wireless Sensor Networks. IEEE Trans. Engineering Management 68(1): 170-182 (2021). https://doi.org/10.1109/TEM.2019.2953889

[8] Mohammed Hamim, Ismail El Moudden, Mohan D. Pant, Hicham Moutachaouik, Mustapha Hain:A Hybrid Gene Selection Strategy Based on Fisher and Ant Colony Optimization Algorithm for Breast Cancer Classification. Int. J. Online Biomed. Eng. 17(2): 148-163 (2021). https://doi.org/10.3991/ijoe.v17i02.19889

[9] Zain Anwar Ali, Zhangang Han:Path planning of hovercraft using an adaptive ant colony with an artificial potential field algorithm. Int. J. Model. Identif. Control. 39(4): 350-356 (2021). https://doi.org/10.1504/IJMIC.2021.10048636

[10] Arfa Muteeh, Muhammad Sardaraz, Muhammad Tahir: multi-resource load balancing algorithm for cloud computing using ant colony optimization. Clust. Comput. 24(4): 3135-3145 (2021). https://doi.org/10.1007/s10586-021-03322-3

[11] Muhammad Aria Rajasa Pohan, Bambang Riyanto Trilaksono, Sigit Puji Santosa, Arief Syaichu-Rohman:Path Planning Algorithm Using the Hybridization of the Rapidly-Exploring Random Tree and Ant Colony Systems. IEEE Access 9: 153599-153615 (2021). https://doi.org/10.1109/ACCESS.2021.3127635

[12] Fadl Dahan:An Effective Multi-Agent Ant Colony Optimization Algorithm for QoS-Aware Cloud Service Composition. IEEE Access 9: 17196-17207 (2021). https://doi.org/10.1109/ACCESS.2021.3052907

[13] Mikel Garcia de Andoin, Javier Echanobe:Implementable hybrid quantum ant colony optimization algorithm. Quantum Mach. Intell. 4(2): 1-14 (2022). https://doi.org/10.1007/s42484-022-00065-1

[14] Vipul Sharma, Roohie Naaz Mir:An enhanced time efficient technique for image watermarking using ant colony optimization and light gradient boosting algorithm. J. King Saud Univ. Comput. Inf. Sci. 34(3): 615-626 (2022). https://doi.org/10.1016/j.jksuci.2019.03.009

[15] Mehrdad Ahmadi Kamarposhti, Ilhami Colak, Celestine Iwendi, Shahab S. Band, Ebuka Ibeke:Optimal Coordination of PSS and SSSC Controllers in Power System Using Ant Colony Optimization Algorithm. J. Circuits Syst. Comput. 31(4): 2250060:1-2250060:20 (2022). https://doi.org/10.1142/S0218126622500608

[16] Subhadip Pramanik, Adrijit Goswami:Discovery of closed high utility itemsets using a fast nature-inspired ant colony algorithm. Appl. Intell. 52(8): 8839-8855 (2022). https://doi.org/10.1007/s10489-021-02922-1

[17] Mulki Indana Zulfa, Rudy Hartanto, Adhistya Erna Permanasari, Waleed Ali:Improving Cached Data Offloading Optimization Based on Enhanced Hybrid Ant Colony Genetic Algorithm. IEEE Access 10: 84558-84568 (2022). https://doi.org/10.1109/ACCESS.2022.3197205

[18] Nawaf Alharbe, Mohamed Ali Rakrouki, Abeer Aljohani:An Improved Ant Colony Algorithm for Solving a Virtual Machine Placement Problem in a Cloud Computing Environment. IEEE Access 10: 44869-44880 (2022). https://doi.org/10.1109/ACCESS.2022.3170103