A Permutation-Invariant and Variable-Dimension Data-Driven Evolutionary Algorithm for Indoor Antenna Layout Optimization
提出一种数据驱动进化算法DEAL，利用基于集合的神经网络代理模型处理天线布局中昂贵的评估、排列不变性和可变维度问题，在八个测试场景中网络覆盖率优于五种对比算法。
This article focuses on indoor antenna layout optimization, aiming to minimize the number of antennas while maximizing the network coverage rate by optimizing the number and locations of antennas. This optimization problem exhibits three key characteristics: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Expensive evaluation:</i> Indoor scenarios often require expensive propagation models to evaluate the network coverage rate; 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Permutation invariance:</i> Rearranging antenna locations does not change the network coverage rate; and 3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Variable dimension:</i> The variable number of antennas leads to variable-dimensional solutions. Although surrogate models can be adopted for existing data-driven evolutionary algorithms to replace expensive propagation models, thereby reducing evaluation costs, they overlook permutation invariance and struggle to handle variable-dimensional solutions. To this end, this article proposes a novel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$</tex-math> </inline-formula>ata-driven <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$e$</tex-math> </inline-formula>volutionary algorithm for indoor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$a$</tex-math> </inline-formula>ntenna <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l$</tex-math> </inline-formula>ayout optimization, called DEAL. Since the indoor antenna layout consists of a set of antenna locations, DEAL leverages a neural network that operates on sets as a surrogate model. For this surrogate model, the inherent invariance of set elements to permutations is leveraged, enabling DEAL to effectively achieve permutation invariance. Furthermore, due to the variable number of set elements, DEAL can adapt to variable-dimensional solutions. DEAL also incorporates a clustering-based strategy to generate initial antenna layouts and a local search method to further improve the performance of promising solutions. Extensive experiments on eight test scenarios demonstrate that DEAL outperforms five other algorithms in terms of the network coverage rate.