Improving the performance and scalability of differential evolution

Differential Evolution (DE) is a powerful optimization procedure that self-adapts to the search space, although DE lacks diversity and sufficient bias in the mutation step to make efficient progress on non-separable problems. We present an enhancement to Differential Evolution that introduces greater diversity. The new DE approach demonstrates fast convergence towards the global optimum and is highly scalable in the decision space.