Effect of hybridizing Biogeography-Based Optimization (BBO) technique with Artificial Immune Algorithm (AIA) and Ant Colony Optimization (ACO)

Hybridization in optimization methods plays a very vital role to make it effective and efficient. Different optimizationmethods have different search tendency and it is always required to experiment the effect of hybridizing different search tendency of the optimization algorithmwith each other. This paper presents the effect of hybridizing Biogeography-BasedOptimization (BBO) techniquewith Artificial ImmuneAlgorithm (AIA) and Ant ColonyOptimization (ACO) in two differentways. So, four different variants of hybrid BBO, viz. two variants of hybrid BBO with AIA and two with ACO, are developed and experimented in this paper. All the considered optimization techniques have altogether a different search tendency. The iogeography Based Optimization nt Colony Optimization rtificial Immune Algorithm proposed hybrid method is tested on many benchmark problems and real life problems. Friedman test and Holm–Sidak test are performed to have the statistical validity of the results. Results show that proposed hybridization of BBO with ACO and AIA is effective over a wide range of problems. Moreover, the proposed hybridization is also effective over other proposed hybridization of BBO and different variants of BBO available in the literature.