Comparison of several types of methods for solving constrained function optimization problems

Abstract

Several types of methods for solving constrained function optimization problems are discussed in this paper including elite-subspace evolutionary algorithm (ESEA), multi-parent crossover evolutionary algorithm (MPCEA), smooth scheme and line search based particle swarm optimization (SLPSO) and Constrained Differential evolutionary algorithm (CDEA). Numerical simulation experiments show that CDEA is the best method. The approach can maintain population diversity and simple parameter setting and enable us to find the optimal solution within a fairly short period of time.