The paper introduces Biogeography-Based Optimization (BBO), a novel optimization method inspired by the mathematical models of biogeography, which studies the geographical distribution of biological organisms. The author, Daniel J. Simon, from Cleveland State University, discusses the similarities between biogeography and other biology-based optimization methods like Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). BBO shares features with these methods but also has unique aspects, making it applicable to high-dimensional problems with multiple local optima. The paper outlines the mathematical foundations of biogeography, including the concepts of habitat suitability index (HSI), immigration rates, and emigration rates. These concepts are then generalized to formulate a general-purpose optimization algorithm called BBO. The algorithm involves migration, where solutions probabilistically share information, and mutation, where solutions are randomly modified based on their a priori probability of existence. BBO is compared with other population-based optimization algorithms on a set of 14 standard benchmarks and a real-world sensor selection problem for aircraft engine health estimation. The results show that BBO performs well, often outperforming or competing with other methods in terms of both average and best performance. The paper concludes by highlighting the potential of BBO in solving complex optimization problems and suggests further research directions.The paper introduces Biogeography-Based Optimization (BBO), a novel optimization method inspired by the mathematical models of biogeography, which studies the geographical distribution of biological organisms. The author, Daniel J. Simon, from Cleveland State University, discusses the similarities between biogeography and other biology-based optimization methods like Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). BBO shares features with these methods but also has unique aspects, making it applicable to high-dimensional problems with multiple local optima. The paper outlines the mathematical foundations of biogeography, including the concepts of habitat suitability index (HSI), immigration rates, and emigration rates. These concepts are then generalized to formulate a general-purpose optimization algorithm called BBO. The algorithm involves migration, where solutions probabilistically share information, and mutation, where solutions are randomly modified based on their a priori probability of existence. BBO is compared with other population-based optimization algorithms on a set of 14 standard benchmarks and a real-world sensor selection problem for aircraft engine health estimation. The results show that BBO performs well, often outperforming or competing with other methods in terms of both average and best performance. The paper concludes by highlighting the potential of BBO in solving complex optimization problems and suggests further research directions.