论文标题
使用坐标下降算法有效地解决大规模昂贵的优化问题
Towards Solving Large-scale Expensive Optimization Problems Efficiently Using Coordinate Descent Algorithm
论文作者
论文摘要
许多现实世界中的问题被归类为大规模问题，而元催化算法是解决大规模问题的替代方法。他们需要对许多候选解决方案进行评估以在收敛之前解决它们，这对于实际应用而言是不起作用的，因为它们大多数在计算上都是昂贵的。换句话说，这些问题不仅是大规模的，而且在计算上也很昂贵，这使得它们很难解决。没有有效的替代模型来支持大规模昂贵的全球优化（LSEGO）问题。结果，算法应使用有限的计算预算解决LSEGO问题，适用于现实世界应用程序。坐标下降（CD）算法是一种优化策略，基于n维问题分解为n一维问题。据我们所知，尚无重要的研究来评估具有各种维度和景观特性的基准功能来研究CD算法。在本文中，我们提出了一种修改的坐标下降算法（MCD），以解决有限的计算预算的LSEGO问题。我们提出的算法受益于两个领先步骤，即，通过以指数速度将其折叠成一半，找到了感兴趣的区域，然后缩小了搜索空间的收缩。所提出算法的主要优点之一是没有任何控制参数，这远离了调谐过程的复杂性。将所提出的算法与合作的共同进化进行了比较，Delta在20个基准函数上具有1000尺寸的基准函数。此外，我们在CEC-2017上进行了一些实验，d = 10、30、50和100，以研究较低维度MCD算法的行为。结果表明，MCD不仅在大规模问题中，而且在低规模优化问题中都是有益的。
Many real-world problems are categorized as large-scale problems, and metaheuristic algorithms as an alternative method to solve large-scale problem; they need the evaluation of many candidate solutions to tackle them prior to their convergence, which is not affordable for practical applications since the most of them are computationally expensive. In other words, these problems are not only large-scale but also computationally expensive, that makes them very difficult to solve. There is no efficient surrogate model to support large-scale expensive global optimization (LSEGO) problems. As a result, the algorithms should address LSEGO problems using a limited computational budget to be applicable in real-world applications. Coordinate Descent (CD) algorithm is an optimization strategy based on the decomposition of a n-dimensional problem into n one-dimensional problem. To the best our knowledge, there is no significant study to assess benchmark functions with various dimensions and landscape properties to investigate CD algorithm. In this paper, we propose a modified Coordinate Descent algorithm (MCD) to tackle LSEGO problems with a limited computational budget. Our proposed algorithm benefits from two leading steps, namely, finding the region of interest and then shrinkage of the search space by folding it into the half with exponential speed. One of the main advantages of the proposed algorithm is being free of any control parameters, which makes it far from the intricacies of the tuning process. The proposed algorithm is compared with cooperative co-evolution with delta grouping on 20 benchmark functions with dimension 1000. Also, we conducted some experiments on CEC-2017, D=10, 30, 50, and 100, to investigate the behavior of MCD algorithm in lower dimensions. The results show that MCD is beneficial not only in large-scale problems, but also in low-scale optimization problems.
