Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Aerts, J.C., Heuvelink, G.B.: Using simulated annealing for resource allocation. Int. J. Geogr. Inf. Sci. 16(6), 571–587 (2002)
Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)
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)
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)
Holland, J.H.: Concerning Efficient Adaptive Systems. Self-Organizing Systems, p. 230. Spartan Books, Washington, D.C. (1962)
Kennedy, J.: Particle swarm optimization. Encyclopedia of Machine Learning, pp. 760–766. Springer, US (2010)
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)
Wang, N., Lu, J.C., Kvam, P.: Reliability modeling in spatially distributed logistics systems. IEEE Trans. Reliab. 55(3), 525–534 (2006)
Acknowledge
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ye, S., Huang, H., Xu, C., Lv, L., Liang, Y. (2015). An Improved Genetic Algorithm for Bi-objective Problem: Locating Mixing Station. In: Gong, M., Linqiang, P., Tao, S., Tang, K., Zhang, X. (eds) Bio-Inspired Computing -- Theories and Applications. BIC-TA 2015. Communications in Computer and Information Science, vol 562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49014-3_49
Download citation
DOI: https://doi.org/10.1007/978-3-662-49014-3_49
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-49013-6
Online ISBN: 978-3-662-49014-3
eBook Packages: Computer ScienceComputer Science (R0)