Grammatical Swarm is a search and optimization algorithm that belongs to the more general Grammatical Evolution family, which works with a set of solutions called individuals or particles. It uses the Particle Swarm Optimization algorithm as the search engine in the evolution of solutions. In this paper, we present a Grammatical Swarm algorithm for total energy demand estimation in a country from macroeconomic variables. Each particle in the Grammatical Swarm encodes a different model for energy demand estimation, which will be decoded by a predefined grammar. The parameters of the model are also optimized by the proposed algorithm, in such a way that the model is adjusted to a training set of real energy demand data, selecting the more appropriate variables to appear in the model. We analyze the performance of the Grammatical Swarm evolution in two real problems of one‐year ahead energy demand estimation in Spain and France. The proposal is compared with previous approaches with competitive results.

中文翻译：

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用于能量需求估计的粒子群语法进化算法。

语法群算法是一种搜索和优化算法，属于更一般的语法进化家族，与一组称为“个体”或“粒子”的解决方案一起工作。在解决方案的演变中，它使用粒子群优化算法作为搜索引擎。在本文中，我们提出了一种从宏观经济变量估算一个国家的总能源需求的文法算法。语法群中的每个粒子都编码一个不同的模型用于能源需求估算，该模型将通过预定义的语法进行解码。模型的参数也可以通过提出的算法进行优化，以使模型调整为实际能源需求数据的训练集，从而选择更合适的变量出现在模型中。我们分析了在西班牙和法国提前一年进行的能源需求估算的两个实际问题中的语法群演化的性能。