Genetic Algorithm, as a generalized constructive search method, has already been applied to various fields of optimization problems using different encoding schemes. In conventional GAs, the optimum solution is usually announced as the fittest feasible individual achieved in a limited number of generations. In this paper, such a pseudo‐optimum is extended to a neighborhood structure, known as optimal design family.
In this paper, the constructive feature of genetic search is combined with trail update strategy of ant colony approach in a discrete manner, in order to sample more competitive individuals from various subspaces of the search space as a dynamic‐memory of updating design family.
The proposed method is applied to structural layout and size optimization utilizing an efficient integer index encoding and its appropriate genetic operators. Different applications of the proposed method are illustrated using three truss and frame examples. In the first example, topological classes are identified during layout optimization. In the second example, an objective function containing the stress response, displacement response, and the weight of the structure is considered to solve the optimal design of non‐braced frames. This approach allows the selection of less sensitive designs among the family of solutions. The third example is selected for eigenvalue maximization with minimal number of bracings and structural weight for braced frames.
In this paper, a pseudo‐optimum is extended to a neighborhood structure, known as optimal design family.
Kaveh, A. and Shahrouzi, M. (2008), "Optimal structural design family by genetic search and ant colony approach", Engineering Computations, Vol. 25 No. 3, pp. 268-288. https://doi.org/10.1108/02644400810857092Download as .RIS
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