论文标题
部分可观测时空混沌系统的无模型预测
On enhancing efficiency and accuracy of particle swarm optimization algorithms
论文作者
论文摘要
最近在非线性编程中引入了粒子群优化（PSO）算法，被广泛研究并用于各种应用中。已经从其原始配方开始，已经提出了许多用于改进和专业化的变体，但没有任何确定的结果，因此，如今对这一领域的研究仍然相当活跃。本文通过提出对基本PSO算法的一些修改，旨在提高影响优化算法效率和准确性的方面。特别是，与基本的PSO公式和从文献中获得的其他一些其他优化算法相比，已经开发了基于模糊逻辑和贝叶斯理论的PSO变体，它们表现出更好或更具竞争力的性能。
The particle swarm optimization (PSO) algorithm has been recently introduced in the non--linear programming, becoming widely studied and used in a variety of applications. Starting from its original formulation, many variants for improvement and specialization of the PSO have been already proposed, but without any definitive result, thus research in this area is nowadays still rather active. This paper goes in this direction, by proposing some modifications to the basic PSO algorithm, aiming at enhancements in aspects that impact on the efficiency and accuracy of the optimization algorithm. In particular, variants of PSO based on fuzzy logics and Bayesian theory have been developed, which show better, or competitive, performances when compared to both the basic PSO formulation and a few other optimization algorithms taken from the literature.
