Name
Title | ||
|---|---|---|
A multiobjective hybrid genetic algorithm for the capacitated multipoint 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 |
Keywords | Field | DocType |
np-complete,computational results,nondominated solutions,stochastic processes,nondominated solution,random processes,minimal spanning tree,telecommunication networks,subpopulations,hybrid genetic algorithm,population diversity,network topology,complete random method,computational complexity,index terms—genetic algorithms,mul- tiobjective function,capacitated multipoint network design problem,genetic algorithms,multiobjective hybrid genetic algorithm,np-complete problem,subpopulation,graph theory,sampling methods,elitism reservation strategy,shifting prufer vector,capacitated multipoint network design,stochastic universal sampling,subpopulation.,information management,network design,algorithm design and analysis,genetic algorithm,np complete problem,encoding,constraint optimization | Population,Mathematical optimization,Network planning and design,Computer science,Algorithm,Stochastic process,Population diversity,Sampling (statistics),Genetic algorithm,Stochastic universal sampling | Journal |
Volume | Issue | ISSN |
30 | 3 | 1083-4419 |
ISBN | Citations | PageRank |
0-7803-5284-X | 20 | 1.57 |
References | Authors | |
19 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chi-Chun Lo | 1 | 593 | 54.99 |
| Wei-Hsin Chang | 2 | 105 | 10.25 |