Biogeography-Based Optimization Evolutionary Algorithm (BBO)

Biogeography-Based Optimization (BBO) is an intelligent optimization algorithm inspired by biogeography, simulating species migration and mutation mechanisms between habitats to solve optimization problems. The algorithm conceptualizes the solution space as "habitats," where solution fitness corresponds to Habitat Suitability Index (HSI), and achieves global exploration and local exploitation through migration and mutation operations. Core Concepts The BBO algorithm primarily consists of two key operations: Migration and Mutation. High-HSI solutions (quality habitats) tend to share information with low-HSI solutions (similar to species emigration), while low-HSI solutions receive information (similar to species immigration). Mutation operations maintain population diversity through random perturbations, preventing premature convergence. Algorithm Characteristics Balancing Mechanism: Migration rates dynamically adjust exploration and exploitation capabilities Low Parameter Dependency: Only requires setting population size and iteration count Topology Independence: Applicable to both continuous and discrete optimization problems Test Function Implementation Standard test functions like QuarticDisc (quartic function) are commonly used to validate algorithm performance. The algorithm can be invoked using BBO(@QuarticDisc) to optimize this function, with outputs including optimal solutions and convergence curves. Other frequently used test functions like Rastrigin and Schwefel can be called using the same method through appropriate function handles. Implementation Guidelines For practical applications, adjust population size according to problem dimensionality (typically 20-50 individuals). Recommended iteration count ranges from 100-500 generations. For multimodal functions, appropriately increase mutation rates to enhance escape capability from local optima. The algorithm demonstrates strong competitiveness in engineering optimization and neural network training applications, where the migration operator can be implemented using probabilistic selection based on HSI rankings while mutation typically employs Gaussian or uniform random perturbations.