School of Computer Science, Sichuan University, Chengdu, Sichuan Province, China.
PLoS One. 2019 Jun 20;14(6):e0218285. doi: 10.1371/journal.pone.0218285. eCollection 2019.
Motivated by the concepts of quantum mechanics and particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO) was developed to achieve better global search ability. This paper proposes a new method to improve the global search ability of QPSO with fractional calculus (FC). Based on one of the most frequently used fractional differential definitions, the Grünwald-Letnikov definition, we introduce its discrete expression into the position updating of QPSO. Extensive experiments on well-known benchmark functions were performed to evaluate the performance of the proposed fractional-order quantum particle swarm optimization (FQPSO). The experimental results demonstrate its superior ability in achieving optimal solutions for several different optimizations.
受量子力学和粒子群优化（PSO）概念的启发，开发了量子行为粒子群优化（QPSO）以实现更好的全局搜索能力。本文提出了一种利用分数阶微积分（FC）提高 QPSO 全局搜索能力的新方法。基于最常用的分数阶微分定义之一，Grünwald-Letnikov 定义，我们将其离散表达式引入到 QPSO 的位置更新中。通过对多个著名的基准函数进行广泛的实验，评估了所提出的分数阶量子粒子群优化（FQPSO）的性能。实验结果表明，它在实现多种不同优化的最优解方面具有卓越的能力。
