A multiobjective hybrid genetic algorithm for the capacitated multipoint network design problem

Authors
Citation
Cc. Lo et Wh. Chang, A multiobjective hybrid genetic algorithm for the capacitated multipoint network design problem, IEEE SYST B, 30(3), 2000, pp. 461-470
Citations number
36
Categorie Soggetti
AI Robotics and Automatic Control
Journal title
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
ISSN journal
10834419 → ACNP
Volume
30
Issue
3
Year of publication
2000
Pages
461 - 470
Database
ISI
SICI code
1083-4419(200006)30:3<461:AMHGAF>2.0.ZU;2-R
Abstract
The capacitated multipoint network design problem (CMNDP) is NP-complete, I n this paper, a hybrid genetic algorithm for CMNDP is proposed. The multiob jective hybrid genetic algorithm (MOHGA) differs from other genetic algorit hms (GA's) mainly in its selection procedure. The concept of subpopulation is used in MOHGA, Four subpopulations are generated according to the elitis m reservation strategy, the shifting Prufer vector, the stochastic universa l sampling, and the complete random method, respectively, Mixing these four subpopulations produces the next generation population. The MOHGA can effe ctively 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 GA's.