使用CMA-ES降低稳定斜拉桥的代价
Reducing the Price of Stable Cable Stayed Bridges with CMA-ES
AI总结：
本研究针对斜拉桥设计变量相互影响的欺骗性景观问题，比较了遗传算法与CMA-ES算法，发现CMA-ES在相同评估次数下能设计出成本更低且结构安全的桥梁。
摘要：
斜拉桥的设计需要确定多个设计变量的值。土木工程师通常通过手工迭代步骤来完成这项任务，直到工程师对成本和维持解决方案的结构约束都感到满意为止。该问题的难度在于改变一个变量可能会影响其他变量，这意味着它们不是独立的，表明我们面临的是一个欺骗性景观。在这项工作中，我们比较了两种应用于基线解决方案的方法：遗传算法和CMA-ES算法。设计桥梁有两个目标：最小化成本和将结构约束维持在可接受的数值范围内以确保安全。这些是相互冲突的目标，这意味着降低成本通常会导致桥梁在结构上不安全。结果表明，CMA-ES是在搜索空间中寻找良好解决方案的更好选择，在相同的评估次数下击败了基线，而遗传算法则无法做到这一点。具体而言，CMA-ES方法能够设计出成本更低且结构安全的桥梁。
英文摘要：
The design of cable-stayed bridges requires the determination of several design variables' values. Civil engineers usually perform this task by hand as an iteration of steps that stops when the engineer is happy with both the cost and maintaining the structural constraints of the solution. The problem's difficulty arises from the fact that changing a variable may affect other variables, meaning that they are not independent, suggesting that we are facing a deceptive landscape. In this work, we compare two approaches to a baseline solution: a Genetic Algorithm and a CMA-ES algorithm. There are two objectives when designing the bridges: minimizing the cost and maintaining the structural constraints in acceptable values to be considered safe. These are conflicting objectives, meaning that decreasing the cost often results in a bridge that is not structurally safe. The results suggest that CMA-ES is a better option for finding good solutions in the search space, beating the baseline with the same amount of evaluations, while the Genetic Algorithm could not. In concrete, the CMA-ES approach is able to design bridges that are cheaper and structurally safe.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
