Skip to main content
padlock icon - secure page this page is secure

Parameter Tuning of Metaheuristics Using Metaheuristics

Buy Article:

$106.34 + tax (Refund Policy)

Using metaheuristics requires a lot of work setting different parameters. This paper presents a multilevel algorithm to tackle this issue. An upper level metaheuristic is used to determine the most appropriate set of parameters for a low level metaheuristic. This schema is applied to instances of Ant Colony System and Scatter Search metaheuristics that were designed to solve the Set Covering Problem. These algorithms had been widely used on the resolution of different optimization problems requiring an important effort on parameter setting. Here, we use a Genetic Algorithm to optimize the parameter values of Ant Colony System and Scatter Search solving the problem at hand. The idea is transferring the parameter setting effort of one algorithm to other algorithm. A multilevel approach is proposed so that one metaheuristic (Ant Colony or Scatter Search) acts as a low level metaheuristic whose parameters are tuned by a upper level metaheuristic (Genetic Algorithm).
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Document Type: Research Article

Publication date: December 1, 2013

More about this publication?
  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
  • Editorial Board
  • Information for Authors
  • Subscribe to this Title
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more