Multi Objective Optimization Using Biogeography Based Optimization and Differentional Evolution Algorithm

Biogeography-Based Optimization (BBO) which is a new population based evolutionary optimization method inspired by biogeography and Differential Evolution (DE) is a fast and robust evolutionary algorithm for optimization problems. DE algorithm is good at the exploration of the search space and finds global minimum but is not good in exploitation of solutions. In this paper، we combine the exploration of DE with the exploitation of BBO to solve multi-objective problems by introducing a hybrid migration operator effectively. The proposed algorithm (MOBBO/DE) makes the use of nondominated sorting approach improve the convergence ability efficiently and hence it can generate the promising candidate solutions. It also combines crowding distance to guarantee the diversity of Pareto optimal solutions. The proposed approach is validated using several test functions and some metrics taken from the standard literature on evolutionary multi-objective optimization. Results indicate that the approach is highly competitive and that can be considered a viable alternative to solve multi-objective optimization problems.