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

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.

[1]  Brian Boffey,et al.  Dual-based heuristics for a hierarchical covering location problem , 2003, Comput. Oper. Res..

[2]  Gerard B. M. Heuvelink,et al.  Using simulated annealing for resource allocation , 2002, Int. J. Geogr. Inf. Sci..

[3]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[4]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[5]  B. Noble,et al.  On certain integrals of Lipschitz-Hankel type involving products of bessel functions , 1955, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[6]  N. Wang,et al.  Reliability Modeling in Spatially Distributed Logistics Systems , 2006, IEEE Transactions on Reliability.

[7]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[8]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..