A novel differential evolution approach for constraint optimisation

Abstract

In the present study, a modified DE framework is proposed, which is a fusion of two modifications in the parent DE: 1) self-adaptive control parameter; 2) single population structure. Both the concepts are used to modify the parent DE that improves the convergence rate without compromising on quality of the solution. While self-adaptive control parameters are used to get a good quality solution, the single population structure helps in faster convergence as reducing the memory and computational efforts. The resultant algorithm, named NDE, found by application of these concepts balances the exploration and exploitation of the parent DE algorithm. The validation of the performance of the proposed NDE algorithm is drawn on a set of benchmark test functions and is compared to several other state-of-the-arts of DE variants. Numerical results pointed out that the proposed NDE algorithm is better than or at least comparable to the parent DE algorithm.