Advertisement

Cluster Computing

, Volume 22, Supplement 2, pp 4647–4657 | Cite as

Application of chaos discrete particle swarm optimization algorithm on pavement maintenance scheduling problem

  • Kawther AhmedEmail author
  • Belal Al-Khateeb
  • Maher Mahmood
Article
  • 118 Downloads

Abstract

Particle swarm optimization (PSO) is one of the most popular and successful optimization algorithms used for solving single objective and multi-objective optimization problems. It is found that the Multi objective particle swarm optimization (MOPSO) has ability to find the optimal solution quickly and more efficient than other optimization algorithms. In this paper, a discrete (binary) MOPSO with chaos methods is developed and applied to pavement maintenance management. The main objective of this research is to find optimal maintenance and rehabilitation plan for flexible pavement with minimum maintenance cost and maximum pavement performance. This research is the first attempt to combine the crossover operation with velocity and position with multi objective PSO algorithm. The results show that the improvements in pavement performance and cost objectives are 94.65 and 54.01% respectively, while the improvement in execution time is 99.9%. In addition, it is found that the developed algorithm is able to converge to the optimal solution quickly, comparing with another PSO algorithm.

Keywords

Particle swarm optimization Pavement maintenance Multi-objective optimization Chaotic mapping Binary PSO 

References

  1. 1.
    Chang, J.R.: Particle swarm optimization method for optimal prioritization of pavement sections for maintenance and rehabilitation activities. Appl. Mech. Mater. 343, 43–49 (2013)Google Scholar
  2. 2.
    Chen, C., Flintsch, G.W., Al-Qadi, I.L.: Fuzzy logic-based life-cycle costs analysis model for pavement and asset management. In: 6th Int. Conf. Manag. Pavements (2004)Google Scholar
  3. 3.
    Mohammed, M.A., Abd Ghani, M.K., Hamed, R.I., Mostafa, S.A., Ahmad, M.S., Ibrahim, D.A.: Solving vehicle routing problem by using improved genetic algorithm for optimal solution. J. Comput. Sci. 21, 255–262 (2017)Google Scholar
  4. 4.
    Mohammed, M.A., et al.: Solving vehicle routing problem by using improved K-nearest neighbor algorithm for best solution. J. Comput. Sci. 21, 232–240 (2017)Google Scholar
  5. 5.
    Reséndiz, E., Rull-Flores, C.A.: Mahalanobis–Taguchi system applied to variable selection in automotive pedals components using Gompertz binary particle swarm optimization. Expert Syst. Appl. 40(7), 2361–2365 (2013)Google Scholar
  6. 6.
    Tayebi, N.R., Moghadasnejhad, F., Hassani, A.: Analysis of pavement management activities programming by particle swarm optimization. In: Int. Conf. Adv. Electr. Electron. ACEEE, Trivandrum, Kerala, India 1643(1), 149–154 (2010)Google Scholar
  7. 7.
    Elhadidy, A.A., Elbeltagi, E.E., Ammar, M.A.: Optimum analysis of pavement maintenance using multi-objective genetic algorithms. HBRC J. 11(1), 107–113 (2015)Google Scholar
  8. 8.
    Chou, J.S., Le, T.S.: Reliability-based performance simulation for optimized pavement maintenance. Reliab. Eng. Syst. Saf. 96(10), 1402–1410 (2011)Google Scholar
  9. 9.
    Mahmood, M.S.: Network—level maintenance decisions for flexible pavement using a soft computing-based framework, PhD Thesis. Nottingham Trent Univ. (2015)Google Scholar
  10. 10.
    Shen, Y., Bu, Y., Yuan, M.: “A novel chaos particle swarm optimization (PSO) and its application in pavement maintance decision. Ind. Electron. Appl. ICIEA 2009, 3521–3526 (2009)Google Scholar
  11. 11.
    Moreira, A.V., Oliveira, J.R.M., Costa, L., Fwa, T.F.: Assessment of different genetic algorithms for pavement management systems. In: Proceedings of the Eighth International Conference on Maintenance and Rehabilitation of Pavements (2016)Google Scholar
  12. 12.
    Santos, J., Ferreira, A., Flintsch, G.: An adaptive hybrid genetic algorithm for pavement management. Int. J. Pavement Eng. 8436(March), 1–21 (2017)Google Scholar
  13. 13.
    Fwa, T.F., et al.: Genetic-algorithm programing of road maintenance and rehabilitation. J. Transp. Eng. 3, 246–253 (1996)Google Scholar
  14. 14.
    Rentz, H.H.; Life-cycle cost analysis in pavement design. Fed. Highw. Adm., p. 107 (1998)Google Scholar
  15. 15.
    Mahmood, M.S., Mathavan, S., Rahman, M.M.: Pavement maintenance decision optimization using a novel discrete bare-bones particle swarm algorithm. Nottingham Trent University, Nottingham (2016)Google Scholar
  16. 16.
    Fwa, T.F., Chan, W.T., Tan, C.Y.: Genetic-algorithm programing of road maintenance and rehabilitation. J. Transp. Eng. 122, 246–253 (1996)Google Scholar
  17. 17.
    Shahin, M.Y., Walther, J.A.: Pavement maintenance management PAVER system, p. 278 (1990)Google Scholar
  18. 18.
    Kennedy, J., Eberhart, R.: Particle Swarm Optimization. IEEE Int. Conf. 4, 1942–1948 (1995)Google Scholar
  19. 19.
    Coello, C.C., Reyes-Sierra, M.: Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int. J. Comput. Intell. Res. 2(3), 287–308 (2006)Google Scholar
  20. 20.
    Teodorović, D.: Swarm intelligence systems for transportation engineering: principles and applications. Transp. Res. Part C 16(6), 651–667 (2008)Google Scholar
  21. 21.
    Reddy, M.J., Kumar, D.N.: Multi-objective particle swarm optimization for generating optimal trade-offs in reservoir operation. Hydrol. Process 2274, 2897–2909 (2007)Google Scholar
  22. 22.
    De Carvalho, A.B., Pozo, A.: Measuring the convergence and diversity of CDAS multi-objective particle swarm optimization algorithms: a study of many-objective problems. Neurocomputing 75(1), 43–51 (2012)Google Scholar
  23. 23.
    Mostaghim, S., Teich, J.: Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO). IEEE Swarm Intell. Symp. 2(5), 26–33 (2003)Google Scholar
  24. 24.
    Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: 1997 IEEE Int. Conf. Syst. Man, Cybern. Comput. Cybern. Simul. 5, 4104–4108 (1997)Google Scholar
  25. 25.
    Khanesar, M.A., Teshnehlab, M., Shoorehdeli, M.A.: A novel binary particle swarm optimization. In: Proc. 15th Mediterr. Conf. Control Autom. 1(1), 3–8 (2007)Google Scholar
  26. 26.
    Xia, W., Jin, X., Dou, F.: Beam performance optimization of multibeam imaging sonar based on the hybrid algorithm of binary particle swarm optimization and convex optimization. Int. J. Antennas Propag. (2016)Google Scholar
  27. 27.
    Yang, C.-S., Chuang, L.-Y., Li, J.-C., Yang, C.-H.: Chaotic maps in binary particle swarm optimization for feature selection. In: 2008 IEEE Conf. Soft Comput. Ind. Appl., pp. 107–112 (2008)Google Scholar
  28. 28.
    Tang, X., Zhuang, L., Cai, J., Li, C.: Multi-fault classification based on support vector machine trained by chaos particle swarm optimization. Knowle.-Based Syst. 23(5), 486–490 (2010)Google Scholar
  29. 29.
    Raquel, C.R., Naval, P.C.: An effective use of crowding distance in multiobjective particle swarm optimization. In: Proceeding GECCO’05 Proc. 7th Annu. Conf. Genet. Evol. Comput., pp. 257–264 (2005)Google Scholar
  30. 30.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)Google Scholar
  31. 31.
    Zhou, Q., Zhang, W., Cash, S., Olatunbosun, O., Xu, H., Lu, G.: Intelligent sizing of a series hybrid electric power-train system based on chaos-enhanced accelerated particle swarm optimization. Appl. Energy 189, 588–601 (2017)Google Scholar
  32. 32.
    Zhang, Y., Gong, D.-W., Ding, Z.: A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch. Inf. Sci. 192, 213–227 (2012)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Kawther Ahmed
    • 1
    Email author
  • Belal Al-Khateeb
    • 1
  • Maher Mahmood
    • 2
  1. 1.Computer Science Department, College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq
  2. 2.Civil Engineering Department, Faculty of EngineeringUniversity of AnbarRamadiIraq

Personalised recommendations