Topology Optimization Using An Adaptive Genetic Algorithm And A New Geometric Representation
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B. Sid, M. Domaszewski & F. Peyraut
A new method for structural topology optimization using a genetic algorithm is proposed in this paper. This method uses a topology representation by Bézier curves with varying thickness material distribution in a finite element model. Two groups of variables are considered: control points of Bézier curves and thickness values for each curve. This new technique avoids the formation of disconnected elements and checkerboard patterns in optimal topology design. New adaptive strategies for crossover and mutation operators are also proposed. The numerical program has been developed in MATLAB and tested on a benchmark 2-D problem of topology optimization. The results obtained are compared with those obtained by the homogenization method and ANSYS code. Keywords: structural topology optimization, genetic algorithms. 1 Introduction Structural topology optimization is used to find a preliminary structural configuration that meets some predefined criteria. An optimal structural topology can be obtained by the modifications of holes and connectivities of the design domain. The aim is to redistribute material from within so-called reference domain in an iterative manner in order to arrive at a structural topology which is in some criterion optimal. Topology optimization methods have been discussed in a large number of publications. They are also very attractive for many industrial applications in the domain of mechanical and civil engineering. Generally, there are two fundamental approaches to topology optimization. The first one is based on the deterministic methods where the sensitivity analysis
structural topology optimization, genetic algorithms.