基于粒子群和遗传算法的面板堆石坝土石方调配研究

Research on Earthwork Allocation of Face Rockfill Dam Based on Particle Swarm Optimization and Genetic Algorithm

土石方调配是大型水利工程施工中的关键环节,直接影响工程效益。为了合理调配五岳抽水蓄能电站面板堆石坝工程的挖填土石方料,通过深入分析施工过程中可能产生的各种不确定性问题,以施工总费用最低为目标,建立了满足施工进度、机械配置及上坝运输强度等要求的土石方调配模型;利用群体智能算法更适合求解复杂优化问题的特点,分别采用粒子群和遗传算法对所建模型进行计算。结果表明,由粒子群和遗传算法所得方案均能满足施工要求,采用遗传算法所得土石方调配方案的总体费用低,比粒子群算法优化方案的总体费用节省80.12万元。该研究成果可为其他类似工程的土石方调配提供参考。

Earthwork allocation is a key link of large-scale water conservancy project construction,which directly affects the project benefit.In order to reasonably allocate the excavated and filled earthwork materials of the face rockfill dam project of Wuyue pumped-storage power station in Henan,through in-depth analysis of various uncertainties that may occur in the construction process,and aiming at the minimum construction cost,a earthwork allocation model meeting the requirements of construction schedule,mechanical configuration and transportation intensity of the upper dam was established.Because swarm intelligence algorithm is more suitable for solving complex optimization problems,particle swarm optimization and genetic algorithm were used to calculate the model respectively.The results show that both the schemes obtained by particle swarm optimization and genetic algorithm can meet the construction requirements,the overall cost of earthwork allocation scheme obtained by genetic algorithm is lower,which saves 801200 yuan compared with the optimization scheme of particle swarm optimization algorithm.The research results can provide reference for the earthwork allocation of other similar projects.