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Sampling-based modified ant colony optimization method for high-speed rail alignment development
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AI
DOI：10.1111/mice.12809.png)
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High-speed railway (HSR) alignment development is a complex and tedious problem due to an infinite number of possible solutions, the existence of non-linear costs and impacts, and complex location and geometric design constraints. In this study, a low-discrepancy point sampling-based modified ant colony optimization (SMACO) algorithm for obtaining horizontal alignments with optimized HSR-specific cost and impact, including noise and vibration impacts, is proposed. The low-discrepancy sampling approach is used to identify the potentialpointsofintersection(HPIs), from which appropriate intermediate HPIs are selected to develop feasible alignments using the SMACO algorithm. It effectively avoids restricted land parcels and satisfies HSR-related geometric design requirements. A real-world case study demonstrated that the HSR alignment obtained using the proposed method was marginally better than the path planner method-based alignment and the constructed alignment. The sensitivity analysis highlighted the impact of two key parameters, that is, the right of way widths and noise and vibration screening distances on the HSR alignment development. This study advances the alignment development automation, particularly the HSR horizontal alignment for design speeds over 180 km/h. It facilitates extensive search space exploration independent of infeasible regions, identifies and selects HPI without being constrained to prespecified locations and a user-defined number, and proposes a suitably modified ACO algorithm for the HSR alignment development.
Keyword:
HORIZONTAL ALIGNMENT
DISTANCE TRANSFORM
ALGORITHMS
DESIGN
GEOMETRY
期刊
C
IF:
9.1
论文数:
2.0K
被引数:
10.0K
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