Empirical evaluation of distributed Differential Evolution on standard benchmarks

摘要

This paper presents a new distributed Differential Evolution (dDE) algorithm and provides an exhaustive evaluation of it by using two standard benchmarks. One of them was proposed in the special session of Real-Parameter Optimization of CEC’05, and the other was proposed in the special session of Large Scale Global Optimization of CEC’08. We statistically validate and compare our results versus all other techniques presented in these special sessions. This means that more than 25 problems, with different dimensions: 30, 50, 100, and 500 variables, are evaluated; and 15 algorithms are compared in the experiments. Our dDE is simple, accurate, and competitive when applied to a wide variety of problems, with scaling dimensions, and different function features: noisy, non-separable, multimodal, rotated, etc.