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 fo r 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 algorith m with BBO and ABC is proposed, namely HBBABC (Hybrid Biogeography based Artificial Bee Colony Optimization), for the global numerical optimization problem. HBBABC co mbines 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 HBBA BC, 14 benchmark functions are experimented with discrete design variables. Moreover 5 engineering optimization problems with discrete design variables fro m literature are also experimented. Experimental results indicate that proposed approach is effective and efficient for the considered benchmark problems and engineering optimization problems . Co mpared with BBO and ABC separately HBBA BC perfo rms better. 1. Introduction Hybridizat ion of algorithm means to combine the capabilities of different algorith m in a single algorith m. Hybridizat ion is done to overcome the drawback in the existing algorith ms and to obtain better solutions. Evolutionary Algorith ms (EAs) are very popular for the hybridizat ion due to their different capabilit ies in handling different types of problems. There is a continuous research to find new optimization techniques which are capable of handling variety of problems with high effectiveness, efficiency and flexib ility and thus there are many such optimizat ion algorith ms like GA , SA, DE, PSO, A CO, SFLA, ABC, BBO etc. Hybridization is one of the popular methods to increase the effectiveness, efficiency and flexibility of the algorith m to produce better solution and convergence rates and thus saving computational times. Many such hybrid algorith ms are availab le in the literature and continuous efforts are continued to develop new hybrid algorith ms. Literature survey of some of the recently developed hybrid algorithm is given in Table 1.ABC is a simple and powerful population-based algorithm for finding the global optimu m solutions. ABC div ides the population in two main parts viz. emp loyed bees and onlooker bees. Emp loyed bees start the search with specific rules and onlooker bee s follow the employed bees corresponding to the fitness of employed bees and it also updated the solution as employed bees. If there is no change in the fitness of employed bees for some number of generations than that bee is converted in scout bee which s tart for a new search and acts as a employed bees from then. Algorithm continues for predefined number of generations or until the best solution is found. So ABC finds the global solution by exploring the search space with specific ru les follo wed by emplo yed bees, onlooker bees and scout bees. Biogeography-Based Optimization (BBO), proposed by Simon (2008), is a new global optimization algorith m based on the biogeography theory, which is the study of distribution of species. BBO is also population-based optimization method. In the original BBO algorith m, each solution of the population is a vector of integers. BBO updates the solution following immig ration and emigrat ion phenomena of the species from one place to the other which is referred as islands by Simon. Simon co mpared BBO with seven other optimizat ion methods over 14 benchmark functions and a real-world sensor selection problem. The results demonstrated the good performan ce of BBO. BBO has good exploitation ability as solution is updated by exchanging the existing design variables among the solution. In order to combine the searching capabilit ies of ABC and BBO, in this paper, we propose a hybrid ABC with BBO, referred to as HBBABC, for the global nu merical optimizat ion problems. In HBBABC, algorith m starts by updating the solutions using immigrat ion and emigrat ion rates. Solution is further modified using the exploration tendency of ABC using employed, onlooker and scout bees.Experiments have been conducted on 14 bench mark functions with discrete design variables Simon (2008) and also on 5 engineering optimization problems .