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Biogeography-Based Optimization (BBO) is an elegant global optimization method inspired by natural processes of species migration between islands within archipelagos. The algorithm is based on a simple yet powerful idea: high-quality solutions actively share their characteristics, while low-quality ones actively adopt new features, creating a natural flow of information from the best solutions to the worst. A unique adaptive mutation operator provides an excellent balance between exploration and exploitation. BBO demonstrates high efficiency on a variety of tasks.

While reviewing some optimization algorithms, I became interested in the Biogeography-Based Optimization (BBO) algorithm, which was developed by Professor Dan Simon in 2008. BBO draws inspiration from biogeography, the study of the geographical distribution of biological organisms. Mathematical models describing species distribution patterns were first developed in the 1960s. Just as genetic algorithms were inspired by biological genetics and neural networks by biological neurons, BBO uses the mathematical principles of biogeography to solve optimization problems.

In nature, islands of an archipelago with favorable conditions (high Habitat Suitability Index - HSI) have a large number of species and high emigration, while islands with poor conditions have few species and high immigration. This natural dynamic of species migration between islands formed the basis of the BBO optimization mechanism. The algorithm uses the concept of species migration to exchange characteristics between solutions, the mutation probability is based on a theoretically sound species distribution model, and good solutions actively share their characteristics but remain robust to change. This feature is one of the algorithm’s defining characteristics.

In this article, we examine this elegant algorithmic concept, implement it in code, and evaluate the performance of the BBO algorithm.

Author: Andrey Dik