Particle swarm optimization-based solution updating strategy for biogeography-based optimization

Biogeography-based optimization (BBO) is a powerful evolutionary algorithm inspired from the science of biogeography. It mainly uses the biogeography-based migration operator to share the information among individuals. In canonical BBO, according to the principle of immigration and emigration, poor solutions are like to be completely replaced by better ones. Consequently, this will lead to reduction of the population diversity. On the other hand, for Particle Swarm Optimization, a particle will learn from the global best solution and its own history best solution, which also deteriorate population diversity. In this paper, Particle Swarm Optimization (PSO) employs the selection mechanism of BBO and provides its solution updating strategy for BBO. A good particle has a large probability to be learned, while a poor particle has a small probability to be learned. In this way, the whole swarm can eliminate the affects from only one solution. The simulation is done using fourteen benchmark functions, and the results demonstrate that this hybrid BBO-PSO algorithm works efficiently.