Applied soft computing for optimum design of structures
In this study a critical assessment of three metaheuristic optimization algorithms, namely differential evolution, harmony search and particle swarm optimization, is performed with reference to their efficiency and robustness for the optimum design of real-world structures. Furthermore, a neural network based prediction scheme of the structural response, required to assess the quality of each candidate design during the optimization procedure, is proposed. The proposed methodology is applied to an overhead crane structure using different finite element simulations corresponding to a solid discretization as well as mixed discretizations with shell-solid and beam-solid elements. The number of degrees of freedom (dof) resulted for the simulation of the structural response varies in the range of 60,000 to 1,400,000 dof leading to highly computational intensive problems.
metaheuristics neural networks overhead cranes finite element simulations design codes