I. Boulkabeit, Linda Mthembu, T. Marwala, Fernando Buarque de Lima-Neto
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引用次数: 4
Abstract
A recent nature inspired optimization algorithm, Fish School Search (FSS) is applied to the finite element model (FEM) updating problem. This method is tested on a GARTEUR SM-AG19 aeroplane structure. The results of this algorithm are compared with two other metaheuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). It is observed that on average, the FSS and PSO algorithms give more accurate results than the GA. A minor modification to the FSS is proposed. This modification improves the performance of FSS on the FEM updating problem which has a constrained search space.
将一种受自然启发的优化算法鱼群搜索(Fish School Search, FSS)应用于有限元模型更新问题。该方法在GARTEUR SM-AG19飞机结构上进行了试验。将该算法与遗传算法(GA)和粒子群算法(PSO)进行了比较。观察到,平均而言,FSS和PSO算法比遗传算法给出更准确的结果。提出了对金融监督制度的一个小修改。这种改进提高了FSS在有约束搜索空间的有限元更新问题上的性能。