Repository logo
 

Augmented and exact Lagrangian approaches to continuous constrained optimization with evolution strategies

dc.contributor.authorPorter, Jeremy
dc.contributor.copyright-releaseNot Applicableen_US
dc.contributor.degreeDoctor of Philosophyen_US
dc.contributor.departmentFaculty of Computer Scienceen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.external-examinerDr. Youhei Akimotoen_US
dc.contributor.graduate-coordinatorDr. Michael McAllisteren_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.thesis-readerDr. Malcolm Heywooden_US
dc.contributor.thesis-readerDr. Thomas Trappenbergen_US
dc.contributor.thesis-supervisorDr. Dirk Arnolden_US
dc.date.accessioned2022-08-31T12:25:55Z
dc.date.available2022-08-31T12:25:55Z
dc.date.defence2022-08-08
dc.date.issued2022-08-31
dc.description.abstractWe consider variations on Lagrangian approaches to constraint-handling in the context of stochastic black-box optimization. The well-known augmented Lagrangian function transforms a constrained problem into a sequence of unconstrained problems, and has been adapted for use with evolution strategies. Existing adaptations are compared analytically and experimentally, and a new weakness highlighted. This leads to proposing a new algorithm for constrained optimization that adapts an exact Lagrangian approach for use with evolution strategies. The approach is distinguished by framing the multipliers as dependent on position in the search space rather than as separate parameters and by approaching a solution through solving implicit quadratic subproblems with identical optimal multipliers. Efficacy of the EL-ES algorithm is justified by a single-step analysis along with experimental comparisons on selected benchmark results from the literature and a range of archetypal test problems evaluated against implementations using the augmented Lagrangian approach.en_US
dc.identifier.urihttp://hdl.handle.net/10222/81935
dc.language.isoenen_US
dc.subjectevolution strategyen_US
dc.subjectblack-box optimizationen_US
dc.subjectconstrained optimizationen_US
dc.subjectaugmented Lagrangianen_US
dc.subjectexact Lagrangianen_US
dc.titleAugmented and exact Lagrangian approaches to continuous constrained optimization with evolution strategiesen_US

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
JeremyPorter2022.pdf
Size:
10.08 MB
Format:
Adobe Portable Document Format
Description:

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: