Investigation of genetic algorithm design representation for multi-objective truss optimization
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The objective of this research is to develop a flexible design grammar and genetic algorithm representation to be used in a multi-objective optimization method to design efficient steel roof trusses given space dimensions and loading requirements by the user. The goal of implementing the method as a multi-objective problem is to obtain a set of near-optimal trusses for the defined unstructured problem domain, not just a single near-optimal design. The method developed was required to support the exploration of a broad range of conceptual designs before making design decisions. Therefore, a method was developed that could define numerous design variables, support techniques to locate global or near-global optimal designs, and improve the efficiency of the computational procedures implemented. This research effort was motivated by the need to consider structural designs that may be beyond the established conventions of designers in the search for cost-efficient, structurally-sound designs. An effective design grammar that is capable of generating stable trusses is defined in this research. The design grammar supports the optimization of member size, in addition to truss geometry and topology. Multi-objective genetic algorithms were used to evolve sets of Pareto-optimal trusses that had varying topology, geometry, and member sizes. The Pareto-optimal curves provided design engineers with a range of near-optimal design alternatives that showed the tradeoffs that occur in meeting the stated objectives. Designers can select their final design from this set based on their own individual weighting of the design objectives. Trials are performed using a multiobjective genetic algorithm that works with the design grammar to evolve trusses for different span lengths. In addition to evaluate the performance of the developed optimization method further, trials were performed on a benchmark truss problem domain and the results obtained were compared with results obtained by other researchers. The results of the performance evaluation trials for the proposed method, in which the sizing, shape and topology were simultaneously performed, indicated that the method was effective in evolving a variety of truss topologies compared to previous published results, which evolved from a ground structure. The diverse topologies, however, were obtained over several trials instead of being found in a Pareto-optimal set found by a single trial. In addition, the proposed method was not able to locally optimize the member section sizes. Additional trials were performed to determine the benefit of applying local optimization to the member section sizes for a given truss topology or geometry provided by the method. The results indicate that significant weight reduction could be achieved by performing local optimization to the truss designs obtained by the proposed multi-objective optimization method.
Pathi, Soumya Sundar (2006). Investigation of genetic algorithm design representation for multi-objective truss optimization. Master's thesis, Texas A&M University. Texas A&M University. Available electronically from