Paper Info
Title
A multiobjective hybrid genetic algorithm for the capacitatedmultipoint network design problem
Abstract
The capacitated multipoint network design problem (CMNDP) is NP-complete. In this paper, a hybrid genetic algorithm for CMNDP is proposed. The multiobjective hybrid genetic algorithm (MOHGA) differs from other genetic algorithms (GAs) mainly in its selection procedure. The concept of subpopulation is used in MOHGA. Four subpopulations are generated according to the elitism reservation strategy, the shifting Prufer vector, the stochastic universal sampling, and the complete random method, respectively. Mixing these four subpopulations produces the next generation population. The MOHGA can effectively search the feasible solution space due to population diversity. The MOHGA has been applied to CMNDP. By examining computational and analytical results, we notice that the MOHGA can find most nondominated solutions and is much more effective and efficient than other multiobjective GAs
Year
DOI
Venue
2000
10.1109/3477.846234
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Keywords
DocType
Volume
complete random method,next generation population,Prufer vector,capacitatedmultipoint network design problem,genetic algorithm,multiobjective hybrid genetic algorithm,hybrid genetic algorithm,multiobjective GAs,population diversity,analytical result,capacitated multipoint network design
Journal
30
Issue
ISSN
Citations 
3
1083-4419
7
PageRank 
References 
Authors
1.04
4
2
Name
Order
Citations
PageRank
Chi-Chun Lo159354.99
Wei-Hsin Chang210510.25