Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 14788))
Included in the following conference series:
377Accesses
Abstract
Although particle swarm optimization has shown great potentials in solving the complex elevator group scheduling problem, it still suffers from the issue of local optimum. In order to improve the capability of finding the global optimum, a multi-strategy enhanced particle swarm optimization algorithm has been proposed for elevator group scheduling in this work. For the initialization of particle position, Tent map is used to generate a diverse position distribution for faster and more effective explorations in the entire solution space. Spiral flight strategy is then utilized to update the position and velocity of particles in a more flexible way with exploring more spaces. Once an optimum is obtained, a local search strategy is finally employed to search the nearby solution spaces to further avoid local optimum. Simulation results have demonstrated that the proposed algorithm can achieve a shorter passenger waiting time than traditional particle swarm optimization.
This work was supported by National Natural Science Foundation of China (No. 62241102), and the Suzhou Municipal Science and Technology Plan Project (No. SYG202351, No. SYG202129).
This is a preview of subscription content,log in via an institution to check access.
Access this chapter
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
- Chapter
- JPY 3498
- Price includes VAT (Japan)
- eBook
- JPY 17159
- Price includes VAT (Japan)
- Softcover Book
- JPY 10581
- Price includes VAT (Japan)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Maleki, E.F., Bhatta, D., Mashayekhy, L.: A game-theoretic approach to energy-efficient elevator scheduling in smart buildings. IEEE Trans. Syst. Man Cybern. Syst.53(7), 3944–3955 (2023)
Ahmed, S., Shekha, M., Skran, S., et al.: Investigation of optimization techniques on the elevator dispatching problem. arXiv preprintarXiv:2202.13092, pp. 37–49 (2022)
Beamurgia, M., Basagoiti, R., Rodríguez, I., et al.: Improving waiting time and energy consumption performance of a bi-objective genetic algorithm embedded in an elevator group control system through passenger flow estimation. Soft. Comput.26(24), 13673–13692 (2022)
Van, L.D., Lin, Y.B., Wu, T.H., et al.: An intelligent elevator development and management system. IEEE Syst. J.14(2), 3015–3026 (2019)
Zhang, J.R., Li, H.Y., Miao, J.R., et al.: A dispatching model and improved ADMM decomposition algorithm for elevator group control system. Control Decis.38(1), 39–48 (2023)
Beamurgia, M., Basagoiti, R., Rodríguez, I., et al.: A modified genetic algorithm applied to the elevator dispatching problem. Soft. Comput.3(20), 3595–3609 (2016)
Fernández, J., Cortés, P., Guadix, J., et al.: Dynamic fuzzy logic elevator group control system for energy optimization. Int. J. Inf. Technol. Decis. Making12(3), 591–617 (2013)
Liu, Y., Hu, Z., Su, Q., et al.: Energy saving of elevator group control based on optimal zoning strategy with interfloor traffic. In: 3rd International Conference on Information Management, Innovation Management and Industrial Engineering, pp. 328–331. IEEE, Kunming, China (2010)
Bolat, B., Altun, O., Cortés, P.: A particle swarm optimization algorithm for optimal car-call allocation in elevator group control systems. Appl. Soft Comput.12(5), 591–617 (2013)
Wei, Q., Wang, L., Liu, Y., et al.: Optimal elevator group control via deep asynchronous actor-critic learning. IEEE Trans. Neural Netw. Learn. Syst.31(12), 5245–5256 (2020)
Sun, J., Zhao, Q.C., Luh, P.B.: Optimization of group elevator scheduling with advance information. IEEE Trans. Autom. Sci. Eng.7(2), 352–363 (2009)
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw.95, 51–67 (2016)
Chang, J., Bai, T., Huang, Q., et al.: Optimization of water resources utilization by PSO-GA. Water Resour. Manage.27, 3525–3540 (2013)
Wang, Y., Li, B.: Multi-strategy ensemble evolutionary algorithm for dynamic multi-objective optimization. Memetic Comput.1, 3–24 (2010)
Li, C., Feng, B., Li, S., et al.: Dynamic analysis of digital chaotic maps via state-mapping networks. IEEE Trans. Circuits Syst. I Regul. Pap.66(6), 2322–2335 (2019)
Ouyang, C., Qiu, Y., Zhu, D.: Adaptive spiral flying sparrow search algorithm. Sci. Program.2021, 1–16 (2021)
Zhao, K., Xu, B., Lu, M., Shi, J., Li, Z.: An efficient scheduling and navigation approach for warehouse multi-mobile robots. In: Tan, Y., Shi, Y., Niu, B. (eds.) Advances in Swarm Intelligence, ICSI 2022. LNCS, vol. 13345, pp. 45–55. Springer, Cham (2022).https://doi.org/10.1007/978-3-031-09726-3_5
Author information
Authors and Affiliations
School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, 215500, China
Chen Zhang & Mingli Lu
School of Mechanical Engineering, Changshu Institute of Technology, Suzhou, 215500, China
Xu Zhou
School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China
Benlian Xu
School of Mechanical Engineering, Yancheng Institute of Technology, Yancheng, 224007, China
Zhicheng Jin
R&D Center, General Elevator Co., Ltd., Suzhou, 215200, China
Yuejiang Gu
- Chen Zhang
You can also search for this author inPubMed Google Scholar
- Mingli Lu
You can also search for this author inPubMed Google Scholar
- Xu Zhou
You can also search for this author inPubMed Google Scholar
- Benlian Xu
You can also search for this author inPubMed Google Scholar
- Zhicheng Jin
You can also search for this author inPubMed Google Scholar
- Yuejiang Gu
You can also search for this author inPubMed Google Scholar
Corresponding authors
Correspondence toXu Zhou orBenlian Xu.
Editor information
Editors and Affiliations
Peking University, Beijing, China
Ying Tan
Southern University of Science and Technology, Shenzhen, China
Yuhui Shi
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, C., Lu, M., Zhou, X., Xu, B., Jin, Z., Gu, Y. (2024). Multi-strategy Enhanced Particle Swarm Optimization Algorithm for Elevator Group Scheduling. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2024. Lecture Notes in Computer Science, vol 14788. Springer, Singapore. https://doi.org/10.1007/978-981-97-7181-3_5
Download citation
Published:
Publisher Name:Springer, Singapore
Print ISBN:978-981-97-7180-6
Online ISBN:978-981-97-7181-3
eBook Packages:Computer ScienceComputer Science (R0)
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative