CONVERGENCE ANALYSIS OF DIFFERENTIAL EVOLUTION VARIANTS ON UNCONSTRAINED GLOBAL OPTIMIZATION FUNCTIONS

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 competitivenature of DE variants in solving the problem at their hand and compare. We have chosen fourteenbenchmark functions grouped by feature: unimodal and separable, unimodal and nonseparable, multimodal and separable, and multimodal and nonseparable. Fourteen variants of DE wereimplemented and tested on fourteen benchmark problems for dimensions of 30. The competitiveness ofthe variants are identified by the Mean Objective Function value, they achieved in 100 runs. Theconvergence nature of the best and worst performing variants are analyzed by measuring theirConvergence Speed (Cs) and Quality Measure (Qm