A Brain Storm Optimization with Multiinformation Interactions for Global Optimization Problems

The original BSO fails to consider some potential information interactions in its individual update pattern, causing the premature convergence for complex problems. To address this problem, we propose a BSO algorithm with multi-information interactions (MIIBSO). First, a multi-information interaction (MII) strategy is developed, thoroughly considering various information interactions among individuals. Specially, this strategy contains three new MII patterns. The first two patterns aim to reinforce information interaction capability between individuals. The third pattern provides interactions between the corresponding dimensions of different individuals. The collaboration of the above three patterns is established by an individual stagnation feedback (ISF) mechanism, contributing to preserve the diversity of the population and enhance the global search capability for MIIBSO. Second, a random grouping (RG) strategy is introduced to replace both the K-means algorithm and cluster center disruption of the original BSO algorithm, further enhancing the information interaction capability and reducing the computational cost of MIIBSO. Finally, a dynamic difference step-size (DDS), which can offer individual feedback information and improve search range, is designed to achieve an effective balance between global and local search capability for MIIBSO. By combining the MII strategy, RG, and DDS, MIIBSO achieves the effective improvement in the global search ability, convergence speed, and computational cost. MIIBSO is compared with 11 BSO algorithms and five other algorithms on the CEC2013 test suit. The results confirm that MIIBSO obtains the best global search capability and convergence speed amongst the 17 algorithms

A Vector Grouping Learning Brain Storm Optimization Algorithm for Global Optimization Problems

The original brain storm optimization (BSO) method does not rationally compromise global exploration and local exploitation capability, which results in the premature convergence when solving complicated optimization problems like the shifted or shifted rotated functions. To address this problem, the paper develops a vector grouping learning BSO (VGLBSO) method. In VGLBSO, the individuals’ creation based on vector grouping learning (IC-VGL) scheme is first developed to improve the population diversity and compromise the global exploration and local exploitation capability. Moreover, a hybrid individuals’ update (H-IU) scheme is established by reasonably combing two different individuals’ update schemes, which further compromises the global exploration and local exploitation capability. Finally, the random grouping (RG) scheme, instead of K-means grouping is allowed to shrink the computational cost and maintain the diversity of the information exchange between different individuals. Twenty-eight popular benchmark functions are used to compare VGLBSO with 12 BSO and nine swarm intelligence methods. Experimental results present that VGLBSO achieves the best overall performance including the global search ability, convergence speed, and scalability amongst all the compared algorithms

最適化問題に対するブレインストーム最適化アルゴリズムの改善

富山大学・富理工博甲第170号・于洋・2020/3/24富山大学202

進化的アルゴリズムにおける集団構造の研究

富山大学・富理工博甲第171号・王藝叡・2020/3/24富山大学202

Statistical Models for Single Molecule Localization Microscopy

Single-molecule localization microscopy (SMLM) has revolutionized the field of cell biology. It allowed scientists to break the Abbe diffraction limit for fluorescence microscopy and got it closer to the electron microscopy resolution but still it faced some serious challenges. Two of the most important of these are the sample drift and the measurement noise problems that result in lower resolution images. Both of these problems are generally unavoidable where the sample drift is a natural mechanical phenomenon that occurs during the long time of image acquisition required for SMLM (Geisler et al. 2012) while the measurement noise, which arises from combining localization uncertainty and counting error, is related to the number of photons collected from the fluorophore and affects the precision in locating the centroids of single molecules (Thompson, Larson, and Webb 2002). Previous work has tried to devise methods to deal with the sample drift problem but unfortunately, these methods either add too much complexity to the experimental setup or are just inefficient in correctly estimating the drift at the single frame level (Wang et al. 2014). As for measurement noise, all current regular image rendering algorithms treat every detection of the fluorophore as a separate event and hence, the localization uncertainty of every detection of the same molecule would give offset coordinates from the other detections leading to a distorted final image. In this thesis, I demonstrate two novel approaches based on statistical concepts to address each of these two problems. The algorithm for solving the sample drift problem is based on Bayesian inference and it showed efficiency in estimating drift at the single-frame level and proved superior and more straightforward than the available methods. The algorithm for addressing the measurement noise problem is based on Gibbs sampling and not only did it enhance resolution, but it also offers for the first time a means to quantify resolution uncertainty as well as uncertainty in cluster size measurement for clustering proteins. Therefore, this work offers a significant advancement in the field of SMLM and more generally, cell biology

Protein nanobarcodes enable single-step multiplexed fluorescence imaging

Multiplexed cellular imaging typically relies on the sequential application of detection probes, as antibodies or DNA barcodes, which is complex and time-consuming. To address this, we developed here protein nanobarcodes, composed of combinations of epitopes recognized by specific sets of nanobodies. The nanobarcodes are read in a single imaging step, relying on nanobodies conjugated to distinct fluorophores, which enables a precise analysis of large numbers of protein combinations. Fluorescence images from nanobarcodes were used as input images for a deep neural network, which was able to identify proteins with high precision. We thus present an efficient and straightforward protein identification method, which is applicable to relatively complex biological assays. We demonstrate this by a multicell competition assay, in which we successfully used our nanobarcoded proteins together with neurexin and neuroligin isoforms, thereby testing the preferred binding combinations of multiple isoforms, in parallel