Multi-objective path finding in stochastic networks using a biogeography-based optimization method. (July 2016)

• Record Type: Journal Article
• Authors: Wang, Shuihua; Yang, Jianfei; Liu, Ge; Du, Sidan; Yan, Jie
• Other Names: Yu Bin guest-editor.; Zhang Yudong guest-editor.
• Is Part Of: Simulation. Volume 92:Number 7(2016:Jul.)
• Page Start: 637
• Page End: 647
• Publication Date: 2016-07
• Subjects: multi-objective path finding -- stochastic network -- biogeography-based optimization -- genetic algorithm -- ant colony optimization -- artificial bee colony -- particle swarm optimization; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549715623847
• Languages: English
• ISSNs: 0037-5497

Abstract

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.