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Framework for Autonomous Optimization

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: NNX14CP65P
Agency Tracking Number: 140022
Amount: $124,775.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: T11.02
Solicitation Number: N/A
Timeline
Solicitation Year: 2014
Award Year: 2014
Award Start Date (Proposal Award Date): 2014-06-20
Award End Date (Contract End Date): 2014-12-19
Small Business Information
1715 Pratt Drive, Suite 2000
Blacksburg, VA 24060-6472
United States
DUNS: 938911427
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Andy Ko
 Principal Investigator
 (540) 961-7215
 ako@phoenix-int.com
Business Contact
 Peter Menegay
Title: Business Official
Phone: (540) 557-7556
Email: pmenegay@phoenix-int.com
Research Institution
 Massachusetts Institute of Technology
 Michael Corcoran
 
77 Massachusetts Avenue - E19-750
Cambridge, MA 02139-4307
United States

 (617) 253-3906
 Domestic nonprofit research organization
Abstract

Phoenix Integration and MIT propose to create a novel autonomous optimization tool and application programming interface (API). The API will demonstrate the ability to link to many optimization algorithms, both open source and proprietary, as well as to framework tools that carry optimization algorithms within them. It will also allow users to connect their engineering models to it conveniently. The API will be available both as a cross-platform standalone product and as part of ModelCenter, an engineering integration and trade study environment. In addition to and included within the API will be techniques to perform optimization autonomously by providing a management layer which globally adjusts the run in an intelligent fashion. Thus, it will categorize problems to understand effective solution techniques for them, try many algorithms during a run, change the settings on single algorithms so they run more productively, adaptively learn which techniques worked and which didn't, and inquire of the user insight that may help the optimizer reach its destination sooner. A database of prior runs will be built to help facilitate these features. The management layer will also help the user understand errors that take place, log appropriately, and prevent failures.

* Information listed above is at the time of submission. *

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