Posterior exploration based sequential Monte Carlo for global optimization

Bin Liu ()
Additional contact information
Bin Liu: Nanjing University of Posts and Telecommunications

Journal of Global Optimization, 2017, vol. 69, issue 4, No 4, 847-868

Abstract: Abstract We propose a global optimization algorithm based on the sequential Monte Carlo (SMC) sampling framework. In this framework, the objective function is normalized to be a probabilistic density function (pdf), based on which a sequence of annealed target pdfs is designed to asymptotically converge on the set of global optimum. A sequential importance sampling procedure is performed to simulate the resulting targets and the maxima of the objective function are assessed from the yielded samples. The disturbing issue lies in the design of the importance sampling (IS) pdf, which crucially influences the IS efficiency. We propose an approach to design the IS pdf by embedding a posterior exploration (PE) procedure into each iteration of the SMC framework. The PE procedure can explore the important regions of the solution space supported by the target pdf. A byproduct of the PE procedure is an adaptive mechanism to design the annealing temperature schedule. We compare the proposed algorithm with related existing methods using a dozen benchmark functions. The result demonstrates the appealing properties of our algorithm.

Keywords: Global optimization; Sequential Monte Carlo; Adaptive annealing schedule; Mixture model; Particle filter optimization; Student’s t distribution; Expectation-maximization (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10898-017-0543-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:jglopt:v:69:y:2017:i:4:d:10.1007_s10898-017-0543-8

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/10898

DOI: 10.1007/s10898-017-0543-8

Access Statistics for this article

Journal of Global Optimization is currently edited by Sergiy Butenko

More articles in Journal of Global Optimization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().