Hybrid evolutionary programming for heavily constrained problems

Abstract

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