Biogeography-Based Optimization (BBO) is a new optimization method inspired by the study of biogeography, the geographical distribution of biological organisms. The paper explores the mathematical principles of biogeography and applies them to develop BBO, which shares similarities with other biology-based optimization methods like genetic algorithms (GAs) and particle swarm optimization (PSO). BBO is particularly effective for high-dimensional problems with multiple local optima. The paper compares BBO with seven other biology-based optimization algorithms on 14 benchmark functions and applies it to a real-world sensor selection problem for aircraft engine health estimation. BBO is based on the concept of species migration and habitat suitability. High HSI (Habitat Suitability Index) habitats have many species and are more static, while low HSI habitats have fewer species and are more dynamic. BBO uses these principles to probabilistically share information between solutions, with high HSI solutions sharing features with low HSI solutions and vice versa. Mutation is used to introduce diversity, with lower HSI solutions more likely to mutate. BBO also incorporates elitism to retain the best solutions. The paper demonstrates that BBO performs well on benchmark functions, outperforming other methods on seven of the 14 functions. It also shows that BBO is effective for the sensor selection problem, finding optimal sensor configurations for aircraft engine health estimation. The computational requirements of BBO are comparable to other optimization methods, but the paper highlights that the fitness function evaluation is the most expensive part of the process. The results indicate that BBO is a promising new approach to optimization, with potential applications in various fields.Biogeography-Based Optimization (BBO) is a new optimization method inspired by the study of biogeography, the geographical distribution of biological organisms. The paper explores the mathematical principles of biogeography and applies them to develop BBO, which shares similarities with other biology-based optimization methods like genetic algorithms (GAs) and particle swarm optimization (PSO). BBO is particularly effective for high-dimensional problems with multiple local optima. The paper compares BBO with seven other biology-based optimization algorithms on 14 benchmark functions and applies it to a real-world sensor selection problem for aircraft engine health estimation. BBO is based on the concept of species migration and habitat suitability. High HSI (Habitat Suitability Index) habitats have many species and are more static, while low HSI habitats have fewer species and are more dynamic. BBO uses these principles to probabilistically share information between solutions, with high HSI solutions sharing features with low HSI solutions and vice versa. Mutation is used to introduce diversity, with lower HSI solutions more likely to mutate. BBO also incorporates elitism to retain the best solutions. The paper demonstrates that BBO performs well on benchmark functions, outperforming other methods on seven of the 14 functions. It also shows that BBO is effective for the sensor selection problem, finding optimal sensor configurations for aircraft engine health estimation. The computational requirements of BBO are comparable to other optimization methods, but the paper highlights that the fitness function evaluation is the most expensive part of the process. The results indicate that BBO is a promising new approach to optimization, with potential applications in various fields.