Differential Evolution with Local Neighborhood

Differential evolution (DE) is well known as a simple and efficient scheme for global optimization over continuous spaces. It is, however, not free from the problem of slow and premature convergence. In this paper we present an improved variant of the classical DE2 scheme, by utilizing the concept of the local neighborhood of each vector. This scheme attempts to balance the exploration and exploitation abilities of DE without requiring additional function evaluations. The new scheme is shown to be statistically significantly better than three other popular DE variants on a six-function test-bed and also on two real-world optimization problems with respect to the following performance measures: solution quality, time to find the solution, frequency of finding the solution, and scalability.