A Hybrid GABC-GA Algorithm for Mechanical Design Optimization Problems

In this paper, we proposed a hybrid algorithm, which is embedding the genetic operators in the global-best-guided artificial bee colony algorithms called GABCGA to solve the nonlinear design optimization problems. The genetic algorithm has no memory function and good at find global optimization with large probability, but the artificial bee colony algorithm not have selection, crossover and mutation operator and most significant at local search. The hybrid algorithm balances the exploration and exploitation ability further by combining the advantages of both. The experimental results of five engineering optimization and comparisons with existing approaches show that the proposed approach is outperforms to those typical approaches in terms of the quality of the results solutions in most cases.