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Research on Quality Management of Urban Rail Transit Vehicle Frame Overhaul Project Read chapter Read first chapter

2023 | OriginalPaper | Chapter

26. Research on Gannet Optimization Algorithm and Its Application in Traveling Salesman Problem

Authors : Jeng-Shyang Pan, Fei-Fei Liu, Jie Wu, Tien-Szu Pan, Shu-Chuan Chu

Published in: Advances in Smart Vehicular Technology, Transportation, Communication and Applications

Publisher: Springer Nature Singapore

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Abstract

With the high level of information technology in modern society, a series of intelligent optimization algorithms have emerged to solve classic multi-combinatorial optimization applications. The origin of intelligent algorithms is the intelligent behavior and physical phenomenon of biological communities in nature, and a large number of intelligent optimization algorithms are widely used in various combinatorial optimization problems. Gannet optimization algorithm (GOA) is a newly proposed intelligent optimization algorithm, which is applied to large-scale constrained optimization problems with the advantages of high convergence and high-quality solutions. For the traveling salesman optimization problem (TSP), the original traditional way is very difficult to calculate. The calculation difficulty increases exponentially with the increase in the number of cities and is rarely used in real life. In this paper, we use the GOA to optimize the TSP problem. Experiments are carried out through two TSP instances, it can be seen from the experimental results that GOA can find a better solution with less computation time.

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Literature
1.
Zhang, F., Wu, T.Y., Wang, Y., Xiong, R., Ding, G., Mei, P., Liu, L.: Application of quantum genetic optimization of LVQ neural network in smart city traffic network prediction. IEEE Access 8, 104555–104564 (2020)CrossRef
2.
Kang, L., Chen, R.S., Chen, Y.C., Wang, C.C., Li, X., Wu, T.Y.: Using cache optimization method to reduce network traffic in communication systems based on cloud computing. IEEE Access 7, 124397–124409 (2019)CrossRef
3.
Wang, E.K., Zhang, X., Wang, F., Wu, T.Y., Chen, C.M.: Multilayer dense attention model for image caption. IEEE Access 7, 66358–66368 (2019)CrossRef
4.
Xi, J., Chen, Y., Liu, X., Chen, X.: Whale optimization algorithm based on nonlinear adjustment and random walk strategy. J. Netw. Intell. 7(2), 306–318 (2022)
5.
Schrijver, A.: On the history of combinatorial optimization (till 1960). Handb. Oper. Res. Manag. Sci. 12, 1–68 (2005)MATH
6.
Ibarra, O.H., Kim, C.E.: Heuristic algorithms for scheduling independent tasks on nonidentical processors. J. ACM (JACM) 24(2), 280–289 (1977)MathSciNetCrossRefMATH
7.
Yang, J., Shi, X., Marchese, M., Liang, Y.: An ant colony optimization method for generalized TSP problem. Prog. Nat. Sci. 18(11), 1417–1422 (2008)MathSciNetCrossRef
8.
Tao, Z.: TSP Problem solution based on improved Genetic Algorithm. In: 2008 Fourth International Conference on Natural Computation, vol. 1, pp. 686–690 (2008)
9.
Böckenhauer, H.J., Hromkovič, J., Klasing, R., Seibert, S., Unger, W.: An improved lower bound on the approximability of metric TSP and approximation algorithms for the TSP with sharpened triangle inequality. In: Annual Symposium on Theoretical Aspects of Computer Science, pp. 382–394 (2000)
10.
11.
Shmoys, D.B., Williamson, D.P.: Analyzing the Held-Karp TSP bound: a monotonicity property with application. Inf. Process. Lett. 35(6), 281–285 (1990)MathSciNetCrossRefMATH
12.
Chauhan, C., Gupta, R., Pathak, K.: Survey of methods of solving tsp along with its implementation using dynamic programming approach. Int. J. Comput. Appl. 52(4), 0975–8887 (2012)
13.
14.
Bouman, P., Agatz, N., Schmidt, M.: Dynamic programming approaches for the traveling salesman problem with drone. Networks 72(4), 528–542 (2018)MathSciNetCrossRef
15.
Xue, X., Tsai, P.W.: Integrating Energy Smart Grid’s ontologies through multi-objective particle swarm optimization algorithm with competitive mechanism. Sustainable Energy Technologies and Assessments 53, 102442 (2022)
16.
Luo, Y., Liu, J., Xue, X., Hu, R., Wang, Z.: The Experimental analysis on transfer function of binary particle swarm optimization. In: International Conference on Swarm Intelligence, vol. 12689, pp. 254–264 (2021)
17.
Song, W., Huang, C.: Mining high average-utility item sets based on particle swarm optimization. Data Sci. Pattern Recognit. 4(2), 19–32 (2020)
18.
Shi, Y.: Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 81–86 (2001)
19.
Wu, J., Xu, M., Liu, F.F., Huang, M., Ma, L., Lu, Z.M.: Solar wireless sensor network routing algorithm based on multi-objective particle swarm optimization. J. Inf. Hiding Multimed. Signal Process. 12(1), 1–11 (2021)
20.
Chu, S.C., Tsai, P.W., Pan, J.S.: Cat swarm optimization. In: Pacific Rim International Conference on Artificial Intelligence, vol. 4099, pp. 854–858 (2006)
21.
Bahrami, M., Bozorg-Haddad, O., Chu, X.: Cat swarm optimization (CSO) algorithm. Adv. Optim. Nat.-Inspired Algorithms 720, 9–18 (2018)
22.
23.
Blum, C.: Ant colony optimization: introduction and recent trends. Phys. Life Rev. 2(4), 353–373 (2005)CrossRef
24.
Pan, J.S., Tian, A.Q., Snášel, V., Kong, L., Chu, S.C.: Maximum power point tracking and parameter estimation for multiple-photovoltaic arrays based on enhanced pigeon-inspired optimization with Taguchi method. Energy 251, 123863 (2022)
25.
Mirjalili, S.: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 27(4), 1053–1073 (2016)MathSciNetCrossRef
26.
Liu, F.F., Chu, S.C., Wang, X., Pan, J.S.: A collaborative dragonfly algorithm with novel communication strategy and application for multi-thresholding color image segmentation. J. Internet Technol. 23(1), 45–62 (2022)CrossRef
27.
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)CrossRef
28.
Chen, H., Xu, Y., Wang, M., Zhao, X.: A balanced whale optimization algorithm for constrained engineering design problems. Appl. Math. Model. 71, 45–59 (2019)MathSciNetCrossRefMATH
29.
Tu, J., Chen, H., Wang, M., Gandomi, A.H.: The colony predation algorithm. J. Bionic Eng. 18(3), 674–710 (2021)CrossRef
30.
Shi, B., Ye, H., Zheng, L., Lyu, J., Chen, C., Heidari, A.A., Wu, P.: Evolutionary warning system for COVID-19 severity: Colony predation algorithm enhanced extreme learning machine. Comput. Biol. Med. 136, 104698 (2021)
31.
Chu, S.C., Du, Z.G., Pan, J.S.: Discrete fish migration optimization for traveling salesman problem. Data Sci. Pattern Recognit. 4(2), 1–18 (2020)
32.
Liu, Y., Zheng, W.M., Liu, S., Chai, Q.W.: Gaussian-based adaptive fish migration optimization applied to optimization localization error of mobile sensor networks. Entropy 24(8), 1109 (2022)
33.
Liang, L.L., Du, Z.G., Shieh, C.S., Hu, C.C., Chu, S.C., Feng, Q.: A new PPE algorithm based on parallel communication strategy. Adv. Intell. Inf. Hiding Multimed. Signal Process. 277, 289–298 (2022)
34.
Pan, J.S., Zhang, L.G., Wang, R.B., Snášel, V., Chu, S.C.: Gannet optimization algorithm: a new metaheuristic algorithm for solving engineering optimization problems. Math. Comput. Simul. 202, 343–373 (2022)MathSciNetCrossRefMATH
35.
Zhou, A., Kang, L., Yan, Z.: Solving dynamic TSP with evolutionary approach in real time. In: The 2003 Congress on Evolutionary Computation, vol. 2, pp. 951–957 (2003)
36.
Luo, X. H., Yang, Y., Li, X.: Solving TSP with shuffled frog-leaping algorithm. In: 2008 Eighth International Conference on Intelligent Systems Design and Applications, vol. 3, pp. 228–232 (2008)
37.
Fang, L., Chen, P., Liu, S.: Particle swarm optimization with simulated annealing for TSP. In: Proceedings of the 6th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases, pp. 206–210 (2007)
38.
Xu, L., Li, T.J., Ling, Y.F., Lu, J., Cai, Z.M.: GSGC: an improved path planning optimization method using guided sampling and gradual cutting. J. Netw. Intell. 7(1), 84–100 (2022)
39.
Nguyen, Trong-The., Dong-Nguyen, Trinh, Nguyen, Vinh-Tiep.: An optimizing pulse coupled neural network based on golden eagle optimizer for automatic image segmentation. J. Inf. Hiding Multimed. Signal Process. 13(3), 155–164 (2022)
40.
Chen, C.M., Xiang, B., Wang, K.H., Yeh, K.H., Wu, T.Y.: A robust mutual authentication with a key agreement scheme for session initiation protocol. Appl. Sci. 8(10), 1789 (2018)
Metadata
Title
Research on Gannet Optimization Algorithm and Its Application in Traveling Salesman Problem
Authors
Jeng-Shyang Pan
Fei-Fei Liu
Jie Wu
Tien-Szu Pan
Shu-Chuan Chu
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Advances in Smart Vehicular Technology, Transportation, Communication and Applications

Print ISBN: 978-981-9908-47-9

Electronic ISBN: 978-981-9908-48-6

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-99-0848-6_26

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