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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
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 2014 Vol.4(3): 263-270 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.422

Recursive Variable Neighborhood Search

Mohammad R. Raeesi N. and Ziad Kobti
Abstract—Variable Neighborhood Search (VNS) is one of the most recent metaheuristics to solve optimization problems. A new variant of VNS is introduced in this article called Recursive VNS (R-VNS). The proposed R-VNS incorporates recursive methods in order to improve both the exploration and exploitation capability of the basic VNS. The experiments show that the proposed R-VNS outperforms the basic VNS by offering better solutions as well as higher convergence rate. The case study considers classical Job Shop Scheduling Problem in order to evaluate both proposed methods.

Index Terms—Job shop scheduling problem, recursive programing, variable neighborhood search.

M. R. Raeesi N. and Z. Kobti are with School of Computer Science, University of Windsor, Windsor, ON N9B 3P4 (e-mail: raeesim@uwindsor.ca, kobti@uwindsor.ca).


Cite: Mohammad R. Raeesi N. and Ziad Kobti, "Recursive Variable Neighborhood Search," International Journal of Machine Learning and Computing vol.4, no. 3, pp. 263-270, 2014.

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