A hybrid algorithm to bypass some weak features in the particle swarm and firefly algorithms...with an application to the traveling salesman problem
Article Sidebar
THIS IS AN OPEN ACCESS ARTICLE UNDER THE CC BY LICENSE
Main Article Content
Abstract
In this paper, the Traveling Salesman Problem (TSP) is solved through the use of some approximation techniques where the results of the previous work showed some defects in solving the problem to obtain an optimal or close to optimal solution,so the use of hybrid algorithms to solve some results from the use of intuitive and exact algorithms. A hybrid algorithm has been proposed that combines the characteristics of the firefly algorithm (FA) and Particle Swarm Optimization (PSO) to obtain an algorithm that works effectively in overcoming some of the problems resulting from the use of each algorithm separately. Then using an improvement factor to improve each solution within the resulting community and to obtain solutions with a high diversity. The efficiency of the proposed method was measured by solving some standard problems TSP, and the results showed a high convergence of the algorithm towards the known optimal solution for each problem by solving 13 standard problems.
Downloads
Article Details
References
Shibeeb, A. K., & Ahmed, M. H., (2019, May), Use of a new approach to automated break transposition cipher system. In IOP Conference Series: Materials Science and Engineering (Vol. 518, No. 5, p. 052020). IOP Publishing. DOI: https://doi.org/10.1088/1757-899X/518/5/052020
Mijbas, A. F., Hasan, B. A. A., & Salah, H. A., (2020), Optimal stabilizer PID parameters tuned by chaotic particle swarm optimization for damping low frequency oscillations (LFO) for single machine infinite bus system (SMIB). Journal of Electrical Engineering & Technology, 15(4), 1577-1584. DOI: https://doi.org/10.1007/s42835-020-00442-5
Brezina Jr, I., & Čičková, Z., (2011), Solving the travelling salesman problem using the ant colony optimization. Management Information Systems, 6(4), 10-14.
Cai, Z., & Sun, D., (2021), Bayesian analysis for quantile smoothing spline. Statistical Theory and Related Fields, 5(4), 346-364. DOI: https://doi.org/10.1080/24754269.2021.1946372
Udaiyakumar, K. C., & Chandrasekaran, M., (2014), Application of firefly algorithm in job shop scheduling problem for minimization of makespan. Procedia Engineering, 97, 1798-1807. DOI: https://doi.org/10.1016/j.proeng.2014.12.333
EF.Gouvêa G.,M. C.Goldbarg,G. R. d. Souza, (2008), Particle Swarm Optimization Algorithm for the Traveling Salesman Problem" Federico Greco, ISBN 978-953-7619-10-7, pp. 202, September 2008,DOI:10.5772/5580 In book: Traveling Salesman Problem Project: Experimental Algorithms Authors:
Elizabeth F. G. Goldbarg, Marco C. Goldbarg and Givanaldo R. de Souza, (2008), Particle Swarm Optimization Algorithm for the Traveling Salesman Problem, Federico Greco (Ed.), ISBN: 978-953-7619-10-7. DOI: https://doi.org/10.5772/5580
Elizabeth F. Gouvła Goldbarg, Givanaldo R. de Souza, and Marco Csar Goldbarg, (2006), Particle Swarm for the Traveling Salesman Problem”, J. Gottlieb and G.R. Raidl (Eds.): EvoCOP 2006, LNCS 3906, pp. 99 110, 2006. DOI: https://doi.org/10.1007/11730095_9
Gotoh, J. Y., Kim, M. J., & Lim, A. E., (2018), Robust empirical optimization is almost the same as mean–variance optimization. Operations research letters, 46(4), 448-452. DOI: https://doi.org/10.1016/j.orl.2018.05.005
Haroun A., (2010), Traveling Salesman Problem and Improved Algorithm for its Solution”, Tishreen University Journal for Research and Scientific Studies-Basic Sciences Series Vol. (32) No. (3).
Li, M., Ma, J., Zhang, Y., Zhou, H., & Liu, J., (2015), Firefly algorithm solving multiple traveling salesman problem. Journal of Computational and Theoretical Nanoscience, 12(7), 1277-1281. DOI: https://doi.org/10.1166/jctn.2015.3886
Kora, P., & Krishna, K. S. R., (2016), Hybrid firefly and particle swarm optimization algorithm for the detection of bundle branch block. International Journal of the Cardiovascular Academy, 2(1), 44-48. DOI: https://doi.org/10.1016/j.ijcac.2015.12.001
Kumbharana, S. N., & Pandey, G. M., (2013), Solving travelling salesman problem using firefly algorithm. International Journal for Research in science & advanced Technologies, 2(2), 53-57.
Saraei, M., Analouei, R., & Mansouri, P., (2015), Solving of travelling salesman problem using firefly algorithm with greedy approach. Fen Bilimleri Dergisi (CFD), 36(6).
Talukder, S., (2011), Mathematical modelling and applications of particle swarm optimization.
Shi, X. H., Liang, Y. C., Lee, H. P., Lu, C., & Wang, Q. X., (2007), Particle swarm optimization-based algorithms for TSP and generalized TSP. Information processing letters, 103(5), 169-176. DOI: https://doi.org/10.1016/j.ipl.2007.03.010
Pan, T. S., Dao, T. K., & Chu, S. C., (2015), Hybrid particle swarm optimization with bat algorithm. In Genetic and evolutionary computing (pp. 37-47). Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-12286-1_5
Gao, W., (2020), New ant colony optimization algorithm for the traveling salesman problem. International Journal of Computational Intelligence Systems, 13(1), 44-55. DOI: https://doi.org/10.2991/ijcis.d.200117.001
Pang, W., Wang, K. P., Zhou, C. G., & Dong, L. J., (2004, September), Fuzzy discrete particle swarm optimization for solving traveling salesman problem. In the Fourth International Conference on Computer and Information Technology, (2004). CIT'04. (pp. 796-800). IEEE.
Yang, X. S., (2013), Multiobjective firefly algorithm for continuous optimization. Engineering with computers, 29(2), 175-184. DOI: https://doi.org/10.1007/s00366-012-0254-1
Marinakis, Y., & Marinaki, M., (2010), A hybrid multi-swarm particle swarm optimization algorithm for the probabilistic traveling salesman problem. Computers & Operations Research, 37(3), 432-442. DOI: https://doi.org/10.1016/j.cor.2009.03.004
Zhang, D., Wu, L., Ye, K., & Wang, M., (2021), Bayesian quantile semiparametric mixed-effects double regression models. Statistical Theory and Related Fields, 5(4), 303-315. DOI: https://doi.org/10.1080/24754269.2021.1877961
الروبط الالكترونية: