Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Systems Engineering

First Advisor

Stanfield, Paul M.


Due to its potential for significant impact, interest continues to grow in the assessment of products from a life cycle perspective. As the nature of products shifts from mechanized and Newtonian to more adaptive and complex, the behavior of products more closely resembles biological organisms in community. The change in product nature is increasingly mirrored at the component level. The work presented in this dissertation is twofold. First, the research proposes a general, systematic and holistic classification of life cycle data to transform the design problem into an optimization problem. Second, the research proposes two new metaheuristics (bio-inspired and socio-inspired) to solve optimization problems to produce grouped solutions that are efficient, evolvable and sustainable. The bio-inspired approach is schooling genetic algorithms (SGA), while the socio-inspired approach is referred to as genetic social networks (GSN). SGA is an approach that combines fish schooling concepts with genetic algorithms (GAs) to enable a dynamic search process. The application of GA operators is subject to the perception of the immediate local environment by clusters of candidate solutions behaving as schools of fish. GSN is an approach that adds social network concepts to GAs, implementing single and dyadic social interactions of social groups (clusters of similar candidate solutions) with GA operators. SGA and GSN both use phenotypic representations of a hypothetical product or system as input. The representations are derived from the proposed life cycle engineering (LCE) data classification. The outputs of either method are the representations that are more than likely to perform better, longer, and more autonomously within their environment during their life cycle. Both methods can also be used as a decision making tool. Both approaches were tested on product design problems with differing parametric relations, underlying solution space, and problem size.