Just as the science of genetics gave rise to genetic algorithms (GAs), and the study of animal swarms gave rise to particle swarm optimization (PSO), and the behaviors of bees gave rise to artificial bee colony optimization, so the science of biogeography has given rise to biogeography-based optimization (BBO).
Overview of the chapter
This chapter shows in section 3.1 how the biogeography theory of the previous chapter can be applied to optimization problems to build a basic BBO algorithm. Section 3.2 discusses the differences between BBO and other bio-inspired optimization algorithms. Section 3.3 demonstrates the performance of basic BBO on a set of standard benchmarks.
3.1. BBO definitions and algorithm
Biogeography is nature’s way of distributing species and optimizing environments for life, and operates according to underlying mathematical optimization rules. Suppose that we have an optimization problem and a population of candidate solutions that can be represented as vectors of independent variables; candidate solutions can be referred to as individuals, or solutions. Each independent variable in a solution is considered to be a suitability index variable (SIV) of biogeography. Further suppose that we have some way of assessing the goodness of the solutions. Those solutions that are good are considered to be habitats with a high habitat suitability index (HSI), and those that are poor are considered to be habitats with a low HSI. HSI is analogous ...