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

Abstract

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.