Skip to main content
Article thumbnail
Location of Repository

Convergence Analysis of Differential Evolution Variants on Unconstrained Global Optimization Functions

By G. Jeyakumar C. Shanmugavelayutham

Abstract

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).Comment: 12 Pages, 1 Figure, 10 Table

Topics: Computer Science - Neural and Evolutionary Computing
Year: 2011
DOI identifier: 10.5121/ijaia.2011.2209
OAI identifier: oai:arXiv.org:1105.1901
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://arxiv.org/abs/1105.1901 (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.