Extended Particle Swarm Optimization (EPSO) for Feature Selection of High Dimensional Biomedical Data

Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks

Deep Convolutional Neural Networks (CNNs) have been widely used in image classification tasks, but the process of designing CNN architectures is very complex, so Neural Architecture Search (NAS), automatically searching for optimal CNN architectures, has attracted more and more research interests. However, the computational cost of NAS is often too high to apply NAS on real-life applications. In this paper, an efficient particle swarm optimisation method named EPSOCNN is proposed to evolve CNN architectures inspired by the idea of transfer learning. EPSOCNN successfully reduces the computation…

使用粒子群优化训练变分量子电路

Training Variational Quantum Circuits Using Particle Swarm Optimization

本文提出用粒子群优化算法训练变分量子电路，以规避梯度方法中的贫瘠高原问题，并在 MedMNIST 多个数据集上以更少量子门取得与随机梯度下降相当或更优的分类性能。

用于生物医学图像与信号处理的自然启发进化群优化器——系统综述

Nature Inspired Evolutionary Swarm Optimizers for Biomedical Image and Signal Processing -- A Systematic Review

本文系统综述了自然启发进化群优化算法在生物医学图像与信号处理中的应用，通过分析28篇文献和26种算法并划分探索程度，填补了该领域缺乏系统综述的空白。

Optimizing Coordinative Schedules for Tanker Terminals: An Intelligent Large Spatial-Temporal Data-Driven Approach -- Part 1

In this study, a novel coordinative scheduling optimization approach is proposed to enhance port efficiency by reducing average wait time and turnaround time. The proposed approach consists of enhanced particle swarm optimization (ePSO) as kernel and augmented firefly algorithm (AFA) as global optimal search. Two paradigm methods of the proposed approach are investigated, which are batch method and rolling horizon method. The experimental results show that both paradigm methods of proposed approach can effectively enhance port efficiency. The average wait time could be significantly reduced by…

基于LSTM的多时间序列融合——在利用脑电图进行CAP A期分类中的应用

Multiple Time Series Fusion Based on LSTM An Application to CAP A Phase Classification Using EEG

本研究针对脑电图CAP A期分类任务，结合遗传算法与粒子群优化实现基于LSTM的多通道特征级融合，在高难度数据集上取得接近专家一致性的准确率，且具备抗噪与鲁棒性优势。