University of Isfahan Computational Intelligence in Electrical Engineering 2821-0689 3 3 2012 10 22 Multi Objective Optimization Using Biogeography Based Optimization and Differentional Evolution Algorithm Multi Objective Optimization Using Biogeography Based Optimization and Differentional Evolution Algorithm 11 24 15343 FA Samira Abdi Dept. of Electrical and Computer Engineering, Urmia University of Technology, Urmia, Iran Mohammad Teshnehlab Dept. of Electrical and Computer Engineering, K.N. Toosi University of Technology, Tehran, Iran Mahdi Aliyari Shouredeli Dept. of Electrical and Computer Engineering, K.N. Toosi University of Technology, Tehran, Iran Hamid Golahmadi Engineering Department, Iranian Research Institute for Electrical Engineering, ACECR, Tehran, Iran Journal Article 2016 06 14 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. <br />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. 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. <br />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. https://isee.ui.ac.ir/article_15343_d6fe1e6345746b2a5dbb3c36c1756a09.pdf