An Improved Genetic Algorithm for Bi-objective Problem: Locating Mixing Station

  • Shujin Ye
  • Han Huang
  • Changjian XuEmail author
  • Liang Lv
  • Yihui Liang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 562)


Locating mixing station (LMS) optimization has a considerable influence on controlling quality and prime cost for the specific construction. As a NP-hard problem, it is more complex than common p-median problem. In this paper, we proposed a hybrid genetic algorithm with special coding scheme, crossover and mutation to solve LMS. In addition, a specified evaluation functions are raised in order to achieve a better optimization solution for the LMS. Moreover, a local search strategy was added into the genetic algorithm (GALS) for improving the stability of the algorithm. On the basis of the experiment results, we can conclude that the proposed algorithm is more stable than the compared algorithm and GALS can be considered as a better solution for the LMS.


Locating mixing station Genetic algorithm Local search 



This work is supported by National Training Program of Innovation and Entrepreneurship for Undergraduates (201410561096), National Natural Science Foundation of China (61370102, 61203310, 61202453, 61370185), the Fundamental Research Funds for the Central Universities, SCUT (2014ZG0043), the Ministry of Education C China, Mobile Research Funds (MCM20130331), Project of Department of Education of Guangdong Province (2013KJCX0073) and the Pearl River Science & Technology Star Project (2012J2200007).


  1. 1.
    Aerts, J.C., Heuvelink, G.B.: Using simulated annealing for resource allocation. Int. J. Geogr. Inf. Sci. 16(6), 571–587 (2002)CrossRefGoogle Scholar
  2. 2.
    Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)Google Scholar
  3. 3.
    Eason, G., Noble, B., Sneddon, I.: On certain integrals of Lipschitz-Hankel type involving products of Bessel functions. Philos. Trans. R. Soc. Lond. A: Math. Phys. Eng. Sci. 247(935), 529–551 (1955)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Espejo, L.G.A., Galvao, R.D., Boffey, B.: Dual-based heuristics for a hierarchical covering location problem. Comput. Oper. Res. 30(2), 165–180 (2003)CrossRefzbMATHGoogle Scholar
  5. 5.
    Holland, J.H.: Concerning Efficient Adaptive Systems. Self-Organizing Systems, p. 230. Spartan Books, Washington, D.C. (1962)Google Scholar
  6. 6.
    Kennedy, J.: Particle swarm optimization. Encyclopedia of Machine Learning, pp. 760–766. Springer, US (2010)Google Scholar
  7. 7.
    Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. glob. Optim. 11(4), 341–359 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Wang, N., Lu, J.C., Kvam, P.: Reliability modeling in spatially distributed logistics systems. IEEE Trans. Reliab. 55(3), 525–534 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Shujin Ye
    • 1
  • Han Huang
    • 2
  • Changjian Xu
    • 2
    Email author
  • Liang Lv
    • 2
  • Yihui Liang
    • 2
  1. 1.Hong Kong Baptist UniversityHong KongChina
  2. 2.South China University of TechnologyGuangzhouChina

Personalised recommendations