Query-Based Learning for Dynamic Particle Swarm Optimization
Query-Based Learning for Dynamic Particle Swarm Optimization
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DOI：
10.1109/access.2017.2694843
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发表时间：
2017-01-01
期刊：
影响因子：
3.9
通讯作者：
Ho, Jan-Ming
中科院分区：
文献类型：
--
作者：
Chang, Ray-I;Hsu, Hung-Min;Ho, Jan-Ming
近年来，许多研究人员研究了动态优化问题（DOP）。关键挑战在于 DOP 的最佳解决方案通常会随着时间而变化。本文重点介绍使用基于查询的学习动态粒子群优化（QBLDPSO）来解决 DOP。 QBLDPSO主要用于改进基于多群体的PSO；我们的 QBL 机制包括两种学习策略，将多样性和记忆的概念整合到 PSO 中。第一个学习策略是 QBL 量子参数自适应（QBLQPA），用于将多样性的概念应用于基于多群体的算法。这与典型的基于多样性的 PSO 方法不同，后者被动地维持解空间中粒子的多样性。我们主动调整量子粒子和中性粒子的比例以实现多样性，而无需分析解空间中最优值的分布。第二种学习策略是基于查询的学习最优预测（QBLOP）。尽管 QBLOP 利用了内存的概念，但我们不需要分析所有粒子的历史。我们选择距离当前最佳解决方案最近的 k 个粒子，并使用最小包围圆作为可能的预测区域。我们的实验结果基于广义动态基准生成器（GDBG），它被用作 DOP 的基准。所提出的方法优于两种最先进的基于多群体的 PSO 方法，使用 QBLQPA 的平均改进为 11.37% 和 8%。特别是，对于 GDBG 中反复出现的问题，我们的方法将性能提高了 35.06%。
In recent years, many researchers have examined dynamic optimization problems (DOPs). The key challenge lies in the fact that the optimal solution of a DOP typically changes over time. This paper focuses on using query-based learning dynamic particle swarm optimization (QBLDPSO) to solve DOPs. QBLDPSO is mainly used for improving multi-population-based PSO; our QBL mechanism includes two learning strategies that integrate the concepts of diversity and memory into PSO. The first learning strategy, QBL quantum parameter adaptation (QBLQPA), is used to apply the concept of diversity to the multi-population based algorithm. This is different from typical diversity-based PSO approaches, which passively maintain the diversity of particles in the solution space. We actively adapt the ratio of quantum particles and neutral particles to achieve diversity without analyzing the distribution of optima in the solution space. The second learning strategy is query-based learning optima prediction (QBLOP). Although QBLOP exploits the concept of memory, we do not need to analyze the history of all particles. We select the k nearest particles to the current best solution and use a minimum encompassing circle as the possible prediction region. Our experimental results are based on the generalized dynamic benchmark generator (GDBG), which is adopted as a benchmark for the DOP. The proposed method outperforms two state-of-the-art multi-population-based PSO methods with the average improvements of 11.37% and 8% using QBLQPA. In particular, for the recurrent problems in GDBG, our method improves performance by 35.06%.