A local exploration-based differential evolution algorithm for constrained global optimization

We propose a modified differential evolution (DE) algorithm for constrained global optimization. The modification is based on the mutation rule of DE. The new algorithm also incorporates a periodic local exploration technique. The local technique used is a ‘limited’ version of the pattern search (PS) method. The penalty functions such as the superiority of feasible points (SFP) and the parameter free penalty (PFP) are used for handling constraints. We numerically study SFP and PFP and based on a drawback observed, we adapt the selection rule of DE. The new algorithm is tested on 45 test problems. Comparisons are made with some recent algorithms.