基于改进蝴蝶算法的水文地质参数优化

Optimization of hydrogeological parameters based on improved butterfly optimization algorithm

摘要

针对水文地质参数求解精度不足以及传统配线法等策略在求参过程中效率低下等的问题,提出一种基于黄金正弦加权蝴蝶优化算法的水文地质参数优化策略。首先在蝴蝶优化算法的全局与局部搜索阶段引入黄金正弦算子,缩小算法解空间;其次引入自适应权重,调整算法后期种群个体移动步长与搜索方向。通过6个基准测试函数的寻优对比测试结果表明:黄金正弦加权蝴蝶优化算法的寻优精度较高且收敛速度较快。将该优化策略应用于水文地质参数导水系数与贮水系数的优化以达到最小降深误差,并与粒子群优化算法、配线法等优化策略进行实验对比。结果表明黄金正弦加权蝴蝶优化算法能有效优化水文地质参数并提高泰斯公式计算性能,获得更小抽水降深误差,为后续抽水试验提供了新方法。

Abstract

In order to solve the problems of insufficient accuracy of hydrogeological parameters and low efficiency of traditional routing methods, an optimization strategy of hydrogeological parameters based on golden sine weighted butterfly optimization algorithm (GSWBOA) is proposed. Firstly, the golden sine operator is introduced in the global and local search phase of butterfly optimization algorithm to reduce the solution space of the algorithm. Secondly, adaptive weights are introduced to adjust the individual moving step size and search direction in the later stage of the algorithm. The comparison test results of 6 benchmark test functions show that the GSWBOA has higher optimization accuracy and faster convergence. The optimization strategy is applied to the optimization of hydrogeological parameters water conductivity coefficient and water storage coefficient to achieve the minimum depth reduction error, and the optimization strategy is compared with particle swarm optimization algorithm, wiring method and other optimization strategies. The results show that the golden sinusoidal weighted butterfly optimization algorithm can effectively optimize the hydrogeological parameters, improve the calculation performance of Theis formula, and obtain a smaller drawdown error, which provides a new method for the subsequent pumping test.

关键词

蝴蝶优化算法 / 黄金正弦算子 / 自适应权重 / 水文地质参数 / 抽水试验

Key words

butterfly optimization algorithm / golden sine operator / adaptive weight coefficient / hydrogeological parameters / pumping test

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基金资助

国家自然科学基金资助项目(62266007)

广西自然科学基金资助项目(2021GXNSFAA220068)