A hybrid real-parameter genetic algorithm for function optimization

One drawback of genetic algorithm is that it may spend much computation time in the encoding and decoding processes. Also, since genetic algorithm lacks hill-climbing capacity, it may easily fall in a trap and find a local minimum not the true solution. In this paper, a novel adaptive real-parameter simulated annealing genetic algorithm that maintains the merits of genetic algorithm and simulated annealing is proposed. Adaptive mechanisms are also included to insure the solution quality and to improve the convergence speed. The performance of the proposed operators has been discussed in detail and compared to other operators, and the performance of the proposed algorithm is demonstrated in 16 benchmark functions and two engineering optimization problems. Due to their versatile characteristics, these examples are suitable to test the ability of the proposed algorithm. The results indicate that the global searching ability and the convergence speed of this novel hybrid algorithm are significantly better, even though small population size is used. Also, the proposed algorithm has good application to engineering optimization problems. Hence, the proposed algorithm is efficient and improves the drawbacks of genetic algorithm.