Biogeography-based optimization for identifying promising compounds in chemical process

Identifying promising compounds from a vast collection of potential compounds is an important and yet challenging problem in chemical engineering. An efficient solution to this problem will help to reduce the expenditure at the early states of chemical process. In an attempt to solve this problem, the industry is looking for predictive tools that would be useful in testing optimal properties of a candidate compound earlier. This paper explores the application of biogeography-based optimization (BBO) to achieve such predictive work. BBO is a new evolutionary algorithm that is based on the science of biogeography. BBO is a population-based search method that achieves information sharing by species migration. The performance of BBO is compared with genetic algorithm (GA) and particle swarm optimization (PSO) on a set of test functions and the cases of identifying promising compounds. Simulation results show that BBO is a competitive method in determining an optimal solution to the optimization of promising compounds.