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Combinatorial genetic algorithm in optimum design of structure
Fan, He (1); Liu, Bin (1); Zhang, Yan-Nian (2); Han, Gui-Wu (1)
2006, 27 (3):
312-315.
DOI: -
In view of the shortcomings of simple genetic algorithm, such as premature convergence, random oscillation and slow convergence, some measures are taken to improve them. Generating original population via the ergodicity of chaotic sequence, the optimized results generated from relative difference quotient algorithm are added to the original population to improve its performance. Introduces the fitness scaling to improve traditional fitness function. Relative difference quotient algorithm is strong in local search, while genetic algorithm with a parallel operation is highly effective in global search. To play the role of both advantages, a combinatorial genetic algorithm (CGA) is proposed, taking the relative difference quotient as a genetic operator which is parallel to selection, crossover and mutation and embedded into the improved genetic algorithm so as to upgrade the local optimization ability and avoid premature convergence. A numerical example of a ten-bar truss was given to demonstrate the validity and feasibility of CGA in its application, and it is found that the optimized results of CGA are superior to SGA and IGA.
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