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A genetic algorithm for 2d shape optimization

Chen, Wei Hang
In this study, an optimization code has been developed based on genetic algorithms associated with the finite element modeling for the shape optimization of plane stress problems. In genetic algorithms, constraints are mostly handled by using the concept of penalty functions, which penalize infeasible solutions by reducing their fitness values in proportion to the degrees of constraint violation. In this study, An Improved GA Penalty Scheme is used. The proposed method gives information about unfeasible individual fitness as near as possible to the feasible region in the evaluation function. The objective function in this study is the area of the structure. The area is minimized considering the Von-Misses stress criteria. In order to minimize the objective function, one-point crossover with roulette-wheel selection approach is used. Optimum dimensions of four problems available in the literature have been solved by the code developed . The algorithm is tested using several strategies such as; different initial population number, different probability of mutation and crossover. The results are compared with the ones in literature and conclusions are driven accordingly.