Hybrid evolutionary programming with fast convergence for constrained optimization problems

A hybridization of accelerated evolutionary programming (AEP) and a deterministic optimization procedure is applied to a series of constrained nonlinear and quadratic optimization problems. The hybrid scheme is compared with other existing schemes such as AEP alone, two-phase (TP) optimization, and EP with a nonstationary penalty function (NS-EP). The results indicate that the hybrid approach can outperform the other methods when addressing constrained optimization problems with respect to the computational efficiency and solution accuracy