Abstract: This study deals with an advanced numerical
techniques to detect tensile forces in cable-stayed structures. The
proposed method allows us not only to avoid the trap of minimum at
initial searching stage but also to find their final solutions in better
numerical efficiency. The validity of the technique is numerically
verified using a set of dynamic data obtained from a simulation of the
cable model modeled using the finite element method. The results
indicate that the proposed method is computationally efficient in
characterizing the tensile force variation for cable-stayed structures.
Abstract: In this study, an optimization of supersonic air-to-air ejector is carried out by a recently developed single-objective genetic algorithm based on adaption of sequence of individuals. Adaptation of sequence is based on Shape-based distance of individuals and embedded micro-genetic algorithm. The optimal sequence found defines the succession of CFD-aimed objective calculation within each generation of regular micro-genetic algorithm. A spring-based deformation mutates the computational grid starting the initial individualvia adapted population in the optimized sequence. Selection of a generation initial individual is knowledge-based. A direct comparison of the newly defined and standard micro-genetic algorithm is carried out for supersonic air-to-air ejector. The only objective is to minimize the loose of total stagnation pressure in the ejector. The result is that sequence-adopted micro-genetic algorithm can provide comparative results to standard algorithm but in significantly lower number of overall CFD iteration steps.