TY - GEN
T1 - An Improved Genetic Algorithm for Bi-objective Problem
T2 - 10th International Conference on Bio-Inspired Computing – Theories and Applications, BIC-TA 2015
AU - Ye, Shujin
AU - Huang, Han
AU - Xu, Changjian
AU - Lv, Liang
AU - Liang, Yihui
N1 - 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).
Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2015.
PY - 2015/12/23
Y1 - 2015/12/23
N2 - 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.
AB - 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.
KW - Genetic algorithm
KW - Local search
KW - Locating mixing station
UR - http://www.scopus.com/inward/record.url?scp=84954110147&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-49014-3_49
DO - 10.1007/978-3-662-49014-3_49
M3 - Conference proceeding
AN - SCOPUS:84954110147
SN - 9783662490136
T3 - Communications in Computer and Information Science
SP - 550
EP - 562
BT - Bio-Inspired Computing -- Theories and Applications
A2 - Gong, Maoguo
A2 - Pan, Linqiang
A2 - Song, Tao
A2 - Tang, Ke
A2 - Zhang, Xingyi
PB - Springer Berlin Heidelberg
Y2 - 25 September 2015 through 28 September 2015
ER -
