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Hybrid Evolutionary Optimization Algorithm for Structures

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In the structure problems, the randomness and the uncertainties of the distribution of the structural parameters are a crucial problem. In the case of optimization of structure, the objective is to play a dominant role in the structural optimization problem introducing the reliability concept. The optimization of the initial structural cost under constraints imposed on the values of elemental reliability indices corresponding to various limit states. In this paper we use a new optimization method for a modified particle swarm optimization algorithm (MPSO) combined with a simulated annealing algorithm (SA). MPSO is known as an efficient approach with a high performance of solving optimization problems in many research fields. It is a population intelligence algorithm inspired by social behavior simulations of bird flocking. Numerical results show the robustness of the MPSO-SA algorithm.
Title: Hybrid Evolutionary Optimization Algorithm for Structures
Description:
In the structure problems, the randomness and the uncertainties of the distribution of the structural parameters are a crucial problem.
In the case of optimization of structure, the objective is to play a dominant role in the structural optimization problem introducing the reliability concept.
The optimization of the initial structural cost under constraints imposed on the values of elemental reliability indices corresponding to various limit states.
In this paper we use a new optimization method for a modified particle swarm optimization algorithm (MPSO) combined with a simulated annealing algorithm (SA).
MPSO is known as an efficient approach with a high performance of solving optimization problems in many research fields.
It is a population intelligence algorithm inspired by social behavior simulations of bird flocking.
Numerical results show the robustness of the MPSO-SA algorithm.

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