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A parallel ant colony algorithm for bus network optimization
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DOI：10.1111/j.1467-8667.2006.00469.x.png)
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This study presents an optimization model for a bus network design based on the coarse-grain parallel ant colony algorithm (CPACA). It aims to maximize the number of direct travelers per unit length, that is, direct traveler density, subject to route length and nonlinear rate constraints (ratio of the length of a route to the shortest road distance between the origin and destination). CPACA is a new optimal algorithm that (1) develops a new strategy to update the increased pheromone, called Ant-Weight, by which the path-searching activities of ants are adjusted based on the objective function, and (2) uses parallelization strategies of an ant colony algorithm (ACA) to improve the calculation time and the quality of the optimization. Data collected in Dalian City, China, are used to test the model and the algorithm. The results show that the optimized bus network has significantly reduced transfers and travel time. They also reveal that the proposed CPACA is effective and efficient compared to some existing ant algorithms.
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