Convergence of an annealing algorithm
Mathematical Programming: Series A and B
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Journal of Global Optimization
The Stud GA: A Mini Revolution?
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Advances in Evolutionary Algorithms: Theory, Design and Practice (Studies in Computational Intelligence)
A markov chain framework for the simple genetic algorithm
Evolutionary Computation
Population distributions in biogeography-based optimization algorithms with elitism
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Biogeography-based optimization combined with evolutionary strategy and immigration refusal
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Biogeography-based optimization and the solution of the power flow problem
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Oppositional biogeography-based optimization
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Biogeography-based optimization with blended migration for constrained optimization problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
An analysis of the equilibrium of migration models for biogeography-based optimization
Information Sciences: an International Journal
Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms
Information Sciences: an International Journal
Adaptive strategy selection in differential evolution for numerical optimization: An empirical study
Information Sciences: an International Journal
Information Sciences: an International Journal
A Differential Covariance Matrix Adaptation Evolutionary Algorithm for real parameter optimization
Information Sciences: an International Journal
Niching particle swarm optimization with local search for multi-modal optimization
Information Sciences: an International Journal
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Biogeography-Based Optimization
IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Markov Models for Biogeography-Based Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Biological invasion-inspired migration in distributed evolutionary algorithms
Information Sciences: an International Journal
Let a biogeography-based optimizer train your Multi-Layer Perceptron
Information Sciences: an International Journal
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Biogeography-based optimization (BBO) is a new evolutionary algorithm that is inspired by biogeography. Previous work has shown that BBO is a competitive optimization algorithm, and it demonstrates good performance on various benchmark functions and real-world optimization problems. Motivated by biogeography theory and previous results, three variations of BBO migration are introduced in this paper. We refer to the original BBO algorithm as partial immigration-based BBO. The new BBO variations that we propose are called total immigration-based BBO, partial emigration-based BBO, and total emigration-based BBO. Their corresponding Markov chain models are also derived based on a previously-derived BBO Markov model. The optimization performance of these BBO variations is analyzed, and new theoretical results that are confirmed with simulation results are obtained. Theoretical results show that total emigration-based BBO and partial emigration-based BBO perform the best for three-bit unimodal problems, partial immigration-based BBO performs the best for three-bit deceptive problems, and all these BBO variations have similar results for three-bit multimodal problems. Performance comparison is further investigated on benchmark functions with a wide range of dimensions and complexities. Benchmark results show that emigration-based BBO performs the best for unimodal problems, and immigration-based BBO performs the best for multimodal problems. In addition, BBO is compared with a stud genetic algorithm (SGA), standard particle swarm optimization (SPSO 07), and adaptive differential evolution (ADE) on real-world optimization problems. The numerical results demonstrate that BBO outperforms SGA and SPSO 07, and performs similarly to ADE for the real-world problems.