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 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. The next generation population is produced by mixing these four subpopulations. The MOHGA can effectively search the feasible solution space due to population diversity. The MOHGA has been applied to CMNDP. By examining computational results, we notice that the MOHGA can find most non-dominated solutions and is much more effective and efficient than other multiobjective genetic algorithms.