HBBABC: A Hybrid Optimization Algorithm Combining Biogeography Based Optimization (BBO) and Artificial Bee Colony (ABC) Optimization For Obtaining Global Solution Of Discrete Design Problems

Artificial bee colony optimization (ABC) is a fast and robust algorithm for global optimization. It has been widely used in many areas including mechanical engineering. Biogeography -Based Optimizat ion (BBO) is a new biogeography inspired algorithm. It mainly uses the biogeography-based migration operator to share the information among solutions. In this work, a hybrid algorithm with BBO and ABC is proposed, namely HBBABC (Hybrid Biogeography based Artificial Bee Colony Optimization), for the global numerical optimization problem. HBBABC combines the searching behavior of ABC with that of BBO. Both the algorithms have different solution searching tendency like ABC have good explorat ion searching tendency while BBO have good exploitat ion searching tendency. To verify the performance of proposed HBBABC, 14 benchmark functions are experimented with discrete design variables. Moreover 5 engineering optimization problems with discrete design variables from literature are also experimented. Experimental results indicate that proposed approach is effective and efficient for the considered benchmark problems and engineering optimization problems . Compared with BBO and ABC separately HBBABC performs better.