Population set-based global optimization algorithms: some modifications and numerical studies

  • Authors:

  • M. M. Ali;A. Törn

  • Affiliations:

  • School of Computational and Applied Mathematics, Witwatersrand University, Private Bag 3, Wits 2050, Johannesburg, South Africa;Department of Computer Science, Åbo Akademi University, Turku, Finland

  • Venue:
  • Computers and Operations Research
  • Year:
  • 2004

Quantified Score

Hi-index 0.01

Visualization

Abstract

This paper studies the efficiency and robustness of some recent and well known population set-based direct search global optimization methods such as Controlled Random Search, Differential Evolution and the Genetic Algorithm. Some modifications are made to Differential Evolution and to the Genetic Algorithm to improve their efficiency and robustness. All methods are tested on two sets of test problems, one composed of easy but commonly used problems and the other of a number of relatively difficult problems.