Multidisciplinary System Optimality Conditions & Termination Design Optimization(MSDO) Gradient-based techniques Post-Optimality Analysis Heuristic techniques

Alternatives to Weighted Sum(WS)Approach Multiobjective Heuristic Programming Utility Function Optimization Physical Programming(Prof. Messac) Application to Space System Optimization Lab Preview(Friday 4-9-2003-Section 1) Massachusetts Institute of Technology-prof. de Weck and Prof. Willcox Engineering Systems Division and Dept of Aeronautics and Astronautics

Mlesd Particle Swarm Optimization 5. 9 A pseudo-optimization method (heuristic) inspired by the collective intelligence of swarms of biological populations Flocks of birds Colonies of insects C Rania Hassan 3/2004 Engineering Systems Division -Massachusetts Institute of Technology

Outline Heuristics Background in Statistical Mechanics -Atom Configuration Problem Metropolis Algorithm Simulated Annealing algorithm Sample Problems and Applications · Summary

What are heuristics The Origin and Analogy of Simulated Annealing The Simulated Annealing algorithm An Example: The Terrestrial Planet Finder Mission Sample results Ways to Tailor the simulated Algorithm Summary References O Massachusetts Institute of Technology- Dr. Cyrus D Jilla& Prof. Olivier de Weck ngineering Systems Division and Dept of Aeronautics Astronautics

This 90 minutes lecture to be delivered via telelink addresses a class of MIT undergraduates unrolled in Engineering Design and Rapid Prototyping Course. The students have been introduced to the basic optimization concepts of design variables, design space, objective function

More on Fitness Function Assignment Mutation Constraint implementation in Gas Multiobjective optimization with Gas Tabu search Selection of Optimization algorithms o Massachusetts Institute of Technology -Prof de Weck and Prof Willcox Engineering Systems Division and Dept of Aeronautics and Astronautics

Mesd Heuristic Search Techniques 16888 2S0.77 Main Motivation for Heuristic Techniques: (1)To deal with local optima and not get trapped in them (2)To allow optimization for systems, where the design variables are not only continuous, but discrete

Sensitivity Analysis effect of changing design variables effect of changing parameters effect of changing constraints Gradient calculation methods Analytical and Symbolic Finite difference Adjoint methods

Sequential Linear Programming Penalty and Barrier Methods Sequential Quadratic Programming Mixed Integer Programming C Massachusetts Institute of Technology - Prof de Weck and Prof Willcox Engineering Systems Division and Dept of Aeronautics and Astronautics