This thesis presents results of a new hybrid optimization method which combines the best features of four traditional optimization methods together with an intelligent adjustment algorithm to speed convergence on unconstrained and constrained optimization problems. It is believed that this is the first time that such a broad array of methods has been employed to facilitate synergistic enhancement of convergence. Particle swarm optimization is based on swarm intelligence inspired by the social behavior and movement dynamics of bird flocking, fish schooling, and swarming theory. This method has been applied for structural damage identification, neural network training, and reactive power optimization. It is also believed that this is the first time an intelligent parameter adjustment algorithm has been applied to maximize the effectiveness of individual component algorithms within a hybrid method. A comprehensive sensitivity analysis of the traditional optimization methods within the hybrid group is used to demonstrate how the relationship among the design variables in a given problem can be used to adjust algorithm parameters. The new method is benchmarked using 11 classical test functions and the results show that the new method outperforms eight of the most recently published search methodologies.
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