Graduation Semester and Year




Document Type


Degree Name

Master of Science in Aerospace Engineering


Mechanical and Aerospace Engineering

First Advisor

Ping Bo Wang


Engineering optimization problems normally include multiple, non-trivial, non-linear constraints. Traditionally gradient based optimization methods were used to solve these types of problems because of their high computational efficiency. But they generally require continuity of the optimization problem and the computation of derivatives and tend to get trapped in local minima. Hence non gradient based, evolutionary algorithms (EA) which are based on heuristic and stochastic nature and do not require continuity of the design space of the optimization problem and increase the probability to find a global optimal solution, therefore are becoming a popular choice in recent years. This work uses evolutionary algorithm based method called Augmented Lagrangian Particle Swarm Optimization (ALPSO). Note that Particle Swarm Optimization (PSO) technique was developed to solve unconstrained problem. An extended non-stationary penalty function approach, Augmented Lagrange Multiplier Method is used for constraint handling The ALPSO algorithm is robust and reliable technique for solving Engineering optimization problem. The capability of ALPSO is demonstrated by solving extension-twist coupling problem in composite laminate design. Besides verification of previously known optimal design computed by sequential quadratic programming method, ALPSO also generates new global optimal designs.


Aerospace Engineering | Engineering | Mechanical Engineering


Degree granted by The University of Texas at Arlington