Multi-start methods for combinatorial optimization
AbstractMulti-start methods strategically sample the solution space of an optimization problem. The most successful of these methods have two phases that are alternated for a certain number of global iterations. The first phase generates a solution and the second seeks to improve the outcome. Each global iteration produces a solution that is typically a local optimum, and the best overall solution is the output of the algorithm. The interaction between the two phases creates a balance between search diversification (structural variation) and search intensification (improvement), to yield an effective means for generating high-quality solutions. This survey briefly sketches historical developments that have motivated the field, and then focuses on modern contributions that define the current state-of-the-art. We consider two categories of multi-start methods: memory-based and memoryless procedures. The former are based on identifying and recording specific types of information (attributes) to exploit in future constructions. The latter are based on order statistics of sampling and generate unconnected solutions. An interplay between the features of these two categories provides an inviting area for future exploration.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal European Journal of Operational Research.
Volume (Year): 226 (2013)
Issue (Month): 1 ()
Contact details of provider:
Web page: http://www.elsevier.com/locate/eor
Metaheuristics; Multi-start methods; Adaptive memory programming; GRASP;
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Braysy, Olli & Hasle, Geir & Dullaert, Wout, 2004. "A multi-start local search algorithm for the vehicle routing problem with time windows," European Journal of Operational Research, Elsevier, vol. 159(3), pages 586-605, December.
- Taillard, Eric D. & Gambardella, Luca M. & Gendreau, Michel & Potvin, Jean-Yves, 2001. "Adaptive memory programming: A unified view of metaheuristics," European Journal of Operational Research, Elsevier, vol. 135(1), pages 1-16, November.
- Shizuo Senju & Yoshiaki Toyoda, 1968. "An Approach to Linear Programming with 0-1 Variables," Management Science, INFORMS, vol. 15(4), pages B196-B207, December.
- Figueiredo, Rosa & Frota, Yuri, 2014. "The maximum balanced subgraph of a signed graph: Applications and solution approaches," European Journal of Operational Research, Elsevier, vol. 236(2), pages 473-487.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.