A novel hybrid genetic algorithm using local optimizer based on heuristic pattern move

A new hybrid genetic algorithm with the significant improvement of converge nce performance is proposed in this study. This algorithm comes from the in corporation of a modified microgenetic algorithm with a local optimizer bas ed on the heuristic pattern move. The hybridization process is implemented by replacing the two worst individuals in the offspring obtained from the c onventional genetic operations with two new individuals generated from the local optimizer in each generation. Some implementation-related problems su ch as the selection of control parameters in the local optimizer are addres sed in detail. This new algorithm has been examined using six benchmarking functions, and is compared with the conventional genetic algorithms without the local optimizer incorporated, as well as the hybrid algorithms incorpo rated with the hill-climbing method in terms of convergence performance. Th e results show that the proposed hybrid algorithm is more effective and eff icient to obtain the global optimum. It takes about 6.4%-74.4% of the numbe r of generations normally required by the conventional genetic algorithms t o obtain the global optimum, while the computation cost for reproducing eac h new generation has hardly increased compared to the conventional genetic algorithms. Another advantage of this new algorithm is the implementation p rocess is very simple and straightforward. There are no extra function eval uations and other complex calculations involved in the added local optimize r as well as in the hybridization process. This makes the new algorithm eas y to be incorporated with the existing software packages of genetic algorit hms so as to further improve their performance. As an engineering example, this new algorithm is applied for the detection of a crack in a composite p late, which demonstrates its effectiveness in solving engineering practical problems.