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

作者：Wang, Shuihua; Yang, Jianfei; Liu, Ge; Du, Sidan^*; Yan, Jie^*

来源：Simulation-Transactions of the Society for Modeling and Simulation International, 2016, 92(7): 637-647.

DOI：10.1177/0037549715623847

• multi-objective path finding
• stochastic network
• biogeography-based optimization
• genetic algorithm
• ant colony optimization
• artificial bee colony
• particle swarm optimization

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.

• 出版日期2016-7
• 单位南京大学