  In this paper, we present an empirical study on convergence nature of
Differential Evolution (DE) variants to solve unconstrained global optimization
problems. The aim is to identify the competitive nature of DE variants in
solving the problem at their hand and compare. We have chosen fourteen
benchmark functions grouped by feature: unimodal and separable, unimodal and
nonseparable, multimodal and separable, and multimodal and nonseparable.
Fourteen variants of DE were implemented and tested on fourteen benchmark
problems for dimensions of 30. The competitiveness of the variants are
identified by the Mean Objective Function value, they achieved in 100 runs. The
convergence nature of the best and worst performing variants are analyzed by
measuring their Convergence Speed (Cs) and Quality Measure (Qm).
