Degree Type

Thesis

Date of Award

2008

Degree Name

Master of Science

Department

Industrial and Manufacturing Systems Engineering

First Advisor

Douglas D. Gemmill

Abstract

Pheromone particle swarm optimization (PSO) of stochastic systems tests the impact of adjustments to algorithm parameters on algorithm performance when searching for optimal solutions to stochastic simulations. To test the benefit of adjusting PSO, the tuned algorithm is compared to the results from the commercial optimization software, OptQuest. In addition, two modifications to pheromone PSO are proposed. These include utilizing orthogonal arrays as an initial position for the algorithm and biasing the release of pheromones in the first iteration based on the relative strength of the objective function. These modifications are shown to improve the average objective functions found as well as the time to convergence in the optimization of some problem types. This paper also highlights the applicability of using pheromone PSO to optimize stochastic simulations compared to commercial optimization software.

DOI

https://doi.org/10.31274/etd-180810-1871

Copyright Owner

Paul Allan Wilhelm

Language

en

Date Available

2012-04-30

File Format

application/pdf

File Size

106 pages

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