Fault Tolerant Design using Single and Multicriteria Genetic Algorithm Optimization


This thesis incorporates a mixed discrete/continuous parameter genetic algorithm optimization capability into the Design Optimization/Markov Evaluation (DOME) program developed by the Charles Stark Draper Laboratory of Cambridge, Massachusetts. DOME combines the merits of Markov modeling and the Optimal Design Process to generate a systematic framework for fault tolerant system design with realistic reliability and cost analyses. The addition of genetic algorithms expands the permissible design problem domain to include discrete parameter problems, which current optimization methods continue to struggle with. A new variant of the traditional genetic algorithm called the steady-state genetic algorithm is introduced to eliminate the idea of distinct generations. Functional constraints are dealt with by ingenious use of "fitness penalty" that capitalizes on the function information contained in the genetic algorithm population. The optimal genetic algorithm parameter settings are investigated, and compared to those of earlier work in this area. The genetic algorithm is compared to the Monte Carlo method and an adapted form of the Branch and Bound optimization method to show its relative utility in the optimization field. This research shows that a single criterion genetic algorithm can be expected to outperform other methods in efficiency, accuracy, and speed on problems of moderate to high complexity. The work then extends to multicriteria optimization, as applied to fault tolerant system design. A multicriteria genetic algorithm is created as a competitive means of generating the efficient (Pareto) set. Method parameters such as cloning, sharing, domination pressure, and population variability are investigated. The method is compared to the e-constraint multicriteria method with a steady-state genetic algorithm performing the underlying single-criterion optimization. This research shows that a genetic algorithm using dominance as a selection criterion exhibits excellent performance for efficient set generation.