Graduation Semester and Year

2012

Language

English

Document Type

Thesis

Degree Name

Master of Science in Aerospace Engineering

Department

Mechanical and Aerospace Engineering

First Advisor

Bowling Alan

Abstract

This work presents an approach to solve function optimization problems using cognitive optimization. A cognitive search algorithm which is developed mimics human cognition in structure and characteristics. This algorithm is developed based on the key aspects of a cognitive system, namely, the use of prior knowledge, communication, and self-organization. The processes have the knowledge of how to solve the optimization problems from traditional techniques such as bracketing, gradient search and interval-halving methods. This search algorithm shows that, by passing messages, the processes communicate, share information, and self-organize around the solution. A mat lab program is coded to perform the tests on difficult problems which are not solved by the traditional bracketing and gradient search techniques alone. This work will be applied on complex functions such as non-convex, multiple local optima and discontinuous functions. The results for the different cases considered explore the structure and characteristics of cognitive optimization, which provide guidelines for the development of general cognitive systems for smart devices.

Disciplines

Aerospace Engineering | Engineering | Mechanical Engineering

Comments

Degree granted by The University of Texas at Arlington

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