An Optimization Algorithm for Minimizing Weight of the Composite Beam
The particle swarm optimization was applied to the optimum design of the composite beam.The total weight of the structure was taken as the objective function.A technique of applying PSO integrated with general finite element code was developed for the opt
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Abstract. The particle swarm optimization was applied to the optimum design of the composite beam.The total weight of the structure was taken as the objective function.A technique of applying PSO integrated with general finite element code was developed for the optimization.Optimization was also conducted using zero-order method included in ANSYS and a comparison was made between zero-order method and PSO.Results demonstrate that PSO can find the global optimal design with higher efficiency regardless of the initial designs.for zero-order method the optimum solution is worst than the result of the PSO optimum. Keywords: particle swam optimization, zero-order method, stacking sequence design.
1 Introduction Fiber reinforced plastic (FRP) composites with superior stiffness-to-weight or strength-to-weight ratios are becoming the structural materials in aerospace, automobile, shipbuilding and other industries. The study of composite materials is of great importance. The optimum design of composite structures which can reduce the structure’s weight without compromising its performance,or improve the performance without increasing its weight, provides the engineers with a tool that is essential in finding the best design among countless alternatives. In practical engineering problems, optimization may deal with functions which are discontinuous or un-differentiable, non-convex, multimodal, or contain noise. These make the optimization problem difficult or even impossible to be solved by traditional methods which require at least the first derivative of the objective function with respect to the design variables[1-2]. Moreover, for large solution spaces where extensive search is required, traditional methods are computationally expensive. In such cases, the intelligent optimization algorithms, which only use the evaluation of the function, should be taken as a promising tool to replace the traditional optimization techniques. Particle Swarm Optimization (PSO) is an intelligent optimization algorithm developed recently[3-4]. Similar to Evolutionary algorithms (EAs), PSO is a population based optimization method. Distinct from the other EAs where knowledge is destroyed between generations, individuals in the population of PSO retain memory of known good solutions as the search for better solutions continues. The other advantage of PSO is that it’s easy to implement and there are fewer parameters to adjust. However, as a new random search method, PSO encounters several problems such as premature H. Tan (Ed.): Knowledge Discovery and Data Mining, AISC 135, pp. 769–775. springerlink.com © Springer-Verlag Berlin Heidelberg 2012
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P. Jiang, Zhu-ming, and L. Jin Xu
convergence and / or slow search speed, too fast decrease of the variety of the particle swarm etc, resulting in a fail search in some cases[5-6]. References[7-10] indicated that the search capability of the algorithm can be enhanced by controlling the swarm variety. This paper develops an improved PSO method for the beam structural optimization. Special mutatio
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