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

Shuihua Wang; Jianfei Yang; Ge Liu; Sidan Du; Jie Yan

doi:10.1177/0037549715623847

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

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

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

30 Citations (Scopus)

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.

Original language	English
Pages (from-to)	637-647
Number of pages	11
Journal	SIMULATION
Volume	92
Issue number	7
DOIs	https://doi.org/10.1177/0037549715623847
Publication status	Published - 1 Jul 2016
Externally published	Yes

Keywords

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

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title = "Multi-objective path finding in stochastic networks using a biogeography-based optimization method",

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.",

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T1 - Multi-objective path finding in stochastic networks using a biogeography-based optimization method

AU - Wang, Shuihua

AU - Yang, Jianfei

AU - Liu, Ge

AU - Du, Sidan

AU - Yan, Jie

N1 - Publisher Copyright: © The Author(s) 2016.

PY - 2016/7/1

Y1 - 2016/7/1

N2 - 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.

AB - 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.

KW - ant colony optimization

KW - artificial bee colony

KW - biogeography-based optimization

KW - genetic algorithm

KW - multi-objective path finding

KW - particle swarm optimization

KW - stochastic network

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

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Keywords

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