Multi-particle Simulated Annealing

Orcun Molvalioglu (), Zelda B. Zabinsky () and Wolf Kohn ()
Additional contact information
Orcun Molvalioglu: University of Washington
Zelda B. Zabinsky: University of Washington
Wolf Kohn: Clearsight Systems Inc.

A chapter in Models and Algorithms for Global Optimization, 2007, pp 215-222 from Springer

Abstract: Summary Whereas genetic algorithms and evolutionary methods involve a population of points, simulated annealing (SA) can be interpreted as a random walk of a single point inside a feasible set. The sequence of locations visited by SA is a consequence of the Markov Chain Monte Carlo sampler. Instead of running SA with multiple independent runs, in this chapter we study a multi-particle version of simulated annealing in which the population of points interact with each other. We present numerical results that demonstrate the benefits of these interactions on algorithm performance.

Date: 2007
References: Add references at CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-0-387-36721-7_14

Ordering information: This item can be ordered from
http://www.springer.com/9780387367217

DOI: 10.1007/978-0-387-36721-7_14

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().