Genetic Algorithms for Solving Shortest Path Problem in Maze-Type Network with Precedence Constraints

  • JunWoo Kim
  • Soo Kyun Kim


Shortest path (SP) problem is a classical combinatorial optimization problem, which has various application domains such as communication network routing and location-based services under cloud environment. However, maze-type networks, sparse networks with many pairs of disconnected nodes, had rarely been studied. A maze-type network is more difficult to analyze than common dense network, since it has rare feasible paths. Moreover, precedence constraints among the nodes further increase the complexity of maze-type network, and this paper aims to develop genetic algorithms for finding the shortest path in maze-type network with precedence constraints. In order to address precedence constrained maze-type shortest path (PCM-SP) problem, the fitness switching genetic algorithm (FSWGA), which has been developed to solve the unconstrained maze-type shortest path (M-SP) problems, is revised by adopting position listing representation as encoding scheme and applying two enhanced decoding procedures. In addition, genetic operator of candidate order based genetic algorithm (COGA) is used to explore the search space effectively, and experiment results demonstrate that the enhanced FSWGA can solve PCM-SP problems more effectively than the previous FSWGA.


Maze-type network Shortest path problem Precedence constraint Fitness switching genetic algorithm Candidate order based genetic algorithm 



This research was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (Ministry of Science, ICT & Future Planning) (NRF-2017R1C1B1008650).


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Industrial and Management Systems EngineeringDong-A UniversityBusanKorea
  2. 2.Department of Game EngineeringPaichai UniversityDaejeonKorea

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