A Novel Hybrid Simplex-Genetic Algorithm For The Optimum Design Of Truss Structures

摘要

Optimum design of complex truss structures often requires searching a high-dimensional, heavily constrained solution space. For the very same reason, the problem has been established as a standard benchmark for evaluating the effectiveness of multivariate optimization algorithms. Moreover, the popularity of such structures justifies the development, or at least custom-tuning, of numerical search algorithms tailored to determine the optimum topology/dimensions of large truss structures at minimum computational cost. We present a new hybrid algorithm for the weight minimization of large truss structures. The algorithm combines the fundamental elements of standard Genetic Algorithms with those proposed by Nelder and Mead in their Simplex algorithm. This would result in a search tool that inherits the power of GAs to quickly spot the promising regions of the search space and the ability of Simplex to effortlessly approach the optimum in convex subspaces. Furthermore, we improve the performance of the algorithm by incorporating a modified Tournament Selection and augment the resulting algorithm with a dynamic penalty method to continuously confine the search to the borders of the feasible region. To demonstrate the applicability and effectiveness of the proposed algorithm, we apply it to a variety of structural design problems and the results are compared with those reported in literature.