The development of a dual-agent strategy for efficient search across whole system engineering design hierarchies

Abstract

Evolutionary and adaptive search (AS) strategies for diverse multi-level search across a preliminary, whole-system design hierarchy defined by both discrete and continuous variable parameters is described. Such strategies provide high-level decision support when integrated with preliminary design software describing the major elements of an engineering system. Initial work has involved a Structured Genetic Algorithm (stGA) with appropriate mutation regimes to encourage search diversity. The shortcomings of the stGA approach are identified and a dual agent strategy is introduced (GAANT). Results are compared to those of the stGA. Appropriate communication between search agents concurrently manipulating the discrete and continuous variable parameter sets results in a more efficient search across the hierarchy than that achieved by the stGA whilst also simplifying the chromosomal representation. This simplification allows the further development of the preliminary design hierarchy in terms of complexity. The technique therefore represents a significant contribution to preliminary design where multi-level, mixed discrete/continuous parameter problems can be prevalent.KeywordsDiscrete ParameterAdaptive SearchTunnel LengthSearch AgentDiscrete PathThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.