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A Novel Context-Free Grammar to Guide the Construction of Particle Swarm Optimization Algorithms

机译：新型无上下文语法指导粒子群优化算法的构建

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摘要

Particle Swarm Optimization algorithm (PSO) has been largely studied over the years due to its flexibility and competitive results in different applications. Nevertheless, its performance depends on different aspects of design (e.g., inertia factor, velocity equation, topology). The task of deciding which is the best algorithm design to solve a particular problem is challenging due to the great number of possible variations and parameters to take into account. This work proposes a novel context-free grammar for Grammar-Guided Genetic Programming (GGGP) algorithms to guide the construction of Particle Swarm Optimizers. The proposed grammar addresses four aspects of the PSO algorithm that may strongly influence on its convergence: swarm initialization, neighborhood topology, velocity update equation and mutation operator. To evaluate this approach, a GGGP algorithm was set with the proposed grammar and applied to optimize the PSO algorithm in 32 unconstrained continuous optimization problems. In the experiments, we compared the designs generated considering the proposed grammar with the designs produced by other grammars proposed in the literature to automate PSO designs. The results obtained by the proposed grammar were better than the counterparts. Besides, we also compared the generated algorithms to state-of-art algorithms. The results have shown that the algorithms produced from the grammar achieved competitive results.
机译：多年来，由于粒子群优化算法（PSO）的灵活性和在不同应用中的竞争结果，人们对此进行了广泛的研究。然而，其性能取决于设计的不同方面（例如，惯性因子，速度方程，拓扑）。由于要考虑大量可能的变化和参数，因此决定哪种解决方案是解决特定问题的最佳算法设计是一项艰巨的任务。这项工作为语法指导遗传规划（GGGP）算法提出了一种新颖的无上下文语法，以指导粒子群优化器的构建。拟议的语法解决了可能对PSO算法的收敛性产生重大影响的四个方面：群体初始化，邻域拓扑，速度更新方程和变异算子。为了评估这种方法，使用提出的语法设置了GGGP算法，并将其用于优化32个无约束的连续优化问题中的PSO算法。在实验中，我们将考虑了建议语法的设计与文献中提出的其他语法生成的设计进行了比较，以使PSO设计自动化。所提出的语法所得到的结果要好于相应的语法。此外，我们还将生成的算法与最新算法进行了比较。结果表明，从语法产生的算法取得了竞争性的结果。

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