【摘要】 大型临时工程（大临工程）的合理布设是保证铁路项目在规定的工期、质量、投资等要求下顺利建设的关键。现有以试错法为导向的线-临协同优化方法在复杂选线环境搜索效率低，甚至难以产生可行解。针对上述问题，进行基于引力场导向的铁路选线与大临工程选址智能协同优化研究。首先，建立以协同度为核心的线-临协同优化目标函数，并据此进行选线环境线路通行和大临工程布设适宜度的量化评价。然后，将线路通行适宜度和大临工程布设适宜度分别抽象为1,2级引力场。最后，将上述适宜度引力场融入粒子群算法，指引线-临方案的优化方向。将智能协同优化方法应用于某山区铁路案例，结果表明：该方法能高效获得造价、线路通行适宜度、施工工期等指标，优于既有方法的最优方案及人工方案的线-临协同优化方案。更多还原

【Abstract】 The properly configuration of large-scale auxiliary construction project(LACP) is critical to ensuring the successful construction of railway projects within the specified schedule, quality, and investment requirements. Current trial-and-error guided alignment-LACP concurrent optimization methods exhibit low search efficiency in complex environments and often struggle to yield feasible solutions. To address this issue, this paper proposes a gravity field-guided railway alignment-LACP intelligent concurrent optimization method. First, an alignmentLACP concurrent optimization objective is established based on their synergy. Based on this, a quantitative evaluation method for environmental suitability of traversing alignments and LACP layouts is proposed. Then, the alignment traversing suitability and LACP layout suitability are abstracted into primary and secondary gravitational fields, respectively. Finally, the these suitability gravity fields are integrated into a particle swarm optimization algorithm for guiding the optimization of the railway alignment-LACP solutions. The intelligent concurrent optimization method proposed in this study is applied to a real-world railway case in a mountainous region. Experimental results show that this method efficiently generates an alignment-LACP concurrent optimization scheme that outperforms the manually-designed solution in terms of cost, alignment environmental suitability, and construction period.更多还原