Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach
Computers and Industrial Engineering - Supply chain management
Hybrid genetic algorithm for multi-time period production/distribution planning
Computers and Industrial Engineering - Special issue: Selected papers from the 30th international conference on computers; industrial engineering
A genetic algorithm for energy-efficient based multicast routing on MANETs
Computer Communications
Erratum: A genetic algorithm for energy-efficient based multicast routing on MANETs
Computer Communications
Evolutionary design of oriented-tree networks using Cayley-type encodings
Information Sciences: an International Journal
Hybrid genetic algorithm for multi-time period production/distribution planning
Computers and Industrial Engineering - Special issue: Selected papers from the 30th international conference on computers; industrial engineering
An efficient genetic algorithm for anycast routing in delay/disruption tolerant networks
IEEE Communications Letters
An analysis of genetic algorithm based anycast routing in delay and disruption tolerant networks
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Cooperative particle swarm optimization for multiobjective transportation planning
Applied Intelligence
Hi-index | 0.00 |
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