Multi-objective path finding in stochastic networks using a biogeography-based optimization method

Multi-objective path finding (MOPF) problems are widely applied in both academic and industrial areas. In order to deal with the MOPF problem more effectively, we propose a novel model that can cope with both deterministic and random variables. For the experiment, we compared five intelligence-optimization algorithms: the genetic algorithm, artificial bee colony (ABC), ant colony optimization (ACO), biogeography-based optimization (BBO), and particle swarm optimization (PSO). After a 100-run comparison, we found the BBO is superior to the other four algorithms with regard to success rate. Therefore, the BBO is effective in MOPF problems.