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.
KeywordsLocating 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).
- 2.Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)Google Scholar
- 5.Holland, J.H.: Concerning Efficient Adaptive Systems. Self-Organizing Systems, p. 230. Spartan Books, Washington, D.C. (1962)Google Scholar
- 6.Kennedy, J.: Particle swarm optimization. Encyclopedia of Machine Learning, pp. 760–766. Springer, US (2010)Google Scholar