• Jul 29, 2019 News!IJMLC Had Implemented Online Submission System, Please Sumbit New Submissions thorough This System Only!   [Click]
  • Jul 16, 2019 News!Good News! All papers from Volume 9, Number 3 have been indexed by Scopus!   [Click]
  • Jul 08, 2019 News!Vol.9, No.4 has been published with online version.   [Click]
Search
General Information
    • ISSN: 2010-3700 (Online)
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.

IJMLC 2018 Vol.8(4): 345-353 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2018.8.4.710

Identifying Early Termination Criteria to Meet Accuracy Requirements in the MMAS Heuristic

Chase W. Gruszewski and Matthew H. Henry
Abstract—This paper examines the selection of algorithm termination criteria in the Max Min Ant System to meet specified accuracy requirements. Three separate types of termination criteria: iterations, total stagnations, and iteration stagnations are examined for predictability. The study takes a Design of Experiments approach, using Box-Behnken Response Surface Methodology to examine the interactions between algorithm parameters, problem characteristics and solution accuracy relative to the algorithm’s solution at convergence. The response surfaces are tested for sensitivity and validated for predictive use. We find that it is possible to predict termination criteria that will meet accuracy requirements and that the iterations termination criterion provides the most predictable accuracy.

Index Terms—Ant colony optimization, design of experiments, metaheuristic algorithms, multi-agent learning.

C. W. Gruszewski was with Johns Hopkins University, Baltimore MD 21075 USA (e-mail: cgrusze1@jhu.edu).
M. H. Henry is with the Applied Physics Laboratory, Johns Hopkins University, Baltimore MD 21075 USA (e-mail: mhhenry@jhu.edu).

[PDF]

Cite: Chase W. Gruszewski and Matthew H. Henry, "Identifying Early Termination Criteria to Meet Accuracy Requirements in the MMAS Heuristic," International Journal of Machine Learning and Computing vol. 8, no. 4, pp. 345-353, 2018.

Copyright © 2008-2019. International Journal of Machine Learning and Computing. All rights reserved.
E-mail: ijmlc@ejournal.net