Automated Design of Metaheuristic Algorithms: A Survey

Metaheuristics have gained great success in academia and practice because their search logic can be applied to any problem with available solution representation, solution quality evaluation, and certain notions of locality. Manually designing metaheuristic algorithms for solving a target problem is criticized for being laborious, error-prone, and requiring intensive specialized knowledge. This gives rise to increasing interest in automated design of metaheuristic algorithms. With computing power to fully explore potential design choices, the automated design could reach and even surpass human-level design and could make high-performance algorithms accessible to a much wider range of researchers and practitioners. This paper presents a broad picture of automated design of metaheuristic algorithms, by conducting a survey on the common grounds and representative techniques in terms of design space, design strategies, performance evaluation strategies, and target problems in this field

AutoOptLib: Tailoring Metaheuristic Optimizers via Automated Algorithm Design

Metaheuristics are prominent gradient-free optimizers for solving hard problems that do not meet the rigorous mathematical assumptions of analytical solvers. The canonical manual optimizer design could be laborious, untraceable and error-prone, let alone human experts are not always available. This arises increasing interest and demand in automating the optimizer design process. In response, this paper proposes AutoOptLib, the first platform for accessible automated design of metaheuristic optimizers. AutoOptLib leverages computing resources to conceive, build up, and verify the design choices of the optimizers. It requires much less labor resources and expertise than manual design, democratizing satisfactory metaheuristic optimizers to a much broader range of researchers and practitioners. Furthermore, by fully exploring the design choices with computing resources, AutoOptLib has the potential to surpass human experience, subsequently gaining enhanced performance compared with human problem-solving. To realize the automated design, AutoOptLib provides 1) a rich library of metaheuristic components for continuous, discrete, and permutation problems; 2) a flexible algorithm representation for evolving diverse algorithm structures; 3) different design objectives and techniques for different optimization scenarios; and 4) a graphic user interface for accessibility and practicability. AutoOptLib is fully written in Matlab/Octave; its source code and documentation are available at https://github.com/qz89/AutoOpt and https://AutoOpt.readthedocs.io/, respectively

Amelioration of Hypertriglyceridemia with Hypo-Alpha-Cholesterolemia in LPL Deficient Mice by Hematopoietic Cell-Derived LPL

BACKGROUND: Macrophage-derived lipoprotein lipase (LPL) has been shown uniformly to promote atherosclerotic lesion formation while the extent to which it affects plasma lipid and lipoprotein levels varies in wild-type and hypercholesterolemic mice. It is known that high levels of LPL in the bulk of adipose tissue and skeletal muscle would certainly mask the contribution of macrophage LPL to metabolism of plasma lipoprotein. Therefore, we chose LPL deficient (LPL⁻/⁻) mice with severe hypertriglyceridemia as an alternative model to assess the role of macrophage LPL in plasma lipoprotein metabolism via bone marrow transplant, through which LPL will be produced mainly by hematopoietic cell-derived macrophages. METHODS AND RESULTS: Hypertriglyceridemic LPL⁻/⁻ mice were lethally irradiated, then transplanted with bone marrow from wild-type (LPL⁺/⁺) or LPL⁻/⁻ mice, respectively. Sixteen weeks later, LPL⁺/⁺ →LPL⁻/⁻ mice displayed significant reduction in plasma levels of triglyceride and cholesterol (408±44.9 vs. 2.7±0.5×10³ and 82.9±7.1 vs. 229.1±30.6 mg/dl, p<0.05, respectively), while a 2.7-fold increase in plasma high density lipoprotein- cholesterol (p<0.01) was observed, compared with LPL⁻/⁻→LPL⁻/⁻ control mice. The clearance rate for the oral fat load test in LPL⁺/⁺ →LPL⁻/⁻ mice was faster than that in LPL⁻/⁻→LPL⁻/⁻ mice, but slower than that in wild-type mice. Liver triglyceride content in LPL⁺/⁺→LPL⁻/⁻ mice was also significantly increased, compared with LPL⁻/⁻→LPL⁻/⁻ mice (6.8±0.7 vs. 4.6±0.5 mg/g wet tissue, p<0.05, n = 6). However, no significant change was observed in the expression levels of genes involved in hepatic lipid metabolism between the two groups. CONCLUSIONS: Hematopoietic cell-derived LPL could efficiently ameliorate severe hypertriglyceridemia and hypo-alpha-cholesterolemia at the compensation of increased triglyceride content of liver in LPL⁻/⁻ mice

Recent Advances in Studies of Genomic DNA Methylation and Its Involvement in Regulating Drought Stress Response in Crops

As global arid conditions worsen and groundwater resources diminish, drought stress has emerged as a critical impediment to plant growth and development globally, notably causing declines in crop yields and even the extinction of certain cultivated species. Numerous studies on drought resistance have demonstrated that DNA methylation dynamically interacts with plant responses to drought stress by modulating gene expression and developmental processes. However, the precise mechanisms underlying these interactions remain elusive. This article consolidates the latest research on the role of DNA methylation in plant responses to drought stress across various species, focusing on methods of methylation detection, mechanisms of methylation pattern alteration (including DNA de novo methylation, DNA maintenance methylation, and DNA demethylation), and overall responses to drought conditions. While many studies have observed significant shifts in genome-wide or gene promoter methylation levels in drought-stressed plants, the identification of specific genes and pathways involved remains limited. This review aims to furnish a reference for detailed research into plant responses to drought stress through epigenetic approaches, striving to identify drought resistance genes regulated by DNA methylation, specific signaling pathways, and their molecular mechanisms of action.</p

The Ginger-shaped Asteroid 4179 Toutatis: New Observations from a Successful Flyby of Chang'e-2

On 13 December 2012, Chang'e-2 conducted a successful flyby of the near-Earth asteroid 4179 Toutatis at a closest distance of 770 $\pm$ 120 meters from the asteroid's surface. The highest-resolution image, with a resolution of better than 3 meters, reveals new discoveries on the asteroid, e.g., a giant basin at the big end, a sharply perpendicular silhouette near the neck region, and direct evidence of boulders and regolith, which suggests that Toutatis may bear a rubble-pile structure. Toutatis' maximum physical length and width are (4.75 $\times$ 1.95 km) $\pm$10$\%$, respectively, and the direction of the +$z$ axis is estimated to be (250$\pm$5$^\circ$, 63$\pm$5$^\circ$) with respect to the J2000 ecliptic coordinate system. The bifurcated configuration is indicative of a contact binary origin for Toutatis, which is composed of two lobes (head and body). Chang'e-2 observations have significantly improved our understanding of the characteristics, formation, and evolution of asteroids in general.Comment: 21 pages, 3 figures, 1 tabl

The improved stratified transformer for organ segmentation of Arabidopsis

Segmenting plant organs is a crucial step in extracting plant phenotypes. Despite the advancements in point-based neural networks, the field of plant point cloud segmentation suffers from a lack of adequate datasets. In this study, we addressed this issue by generating Arabidopsis models using L-system and proposing the surface-weighted sampling method. This approach enables automated point sampling and annotation, resulting in fully annotated point clouds. To create the Arabidopsis dataset, we employed Voxel Centroid Sampling and Random Sampling as point cloud downsampling methods, effectively reducing the number of points. To enhance the efficiency of semantic segmentation in plant point clouds, we introduced the Plant Stratified Transformer. This network is an improved version of the Stratified Transformer, incorporating the Fast Downsample Layer. Our improved network underwent training and testing on our dataset, and we compared its performance with PointNet++, PAConv, and the original Stratified Transformer network. For semantic segmentation, our improved network achieved mean Precision, Recall, F1-score and IoU of 84.20, 83.03, 83.61 and 73.11%, respectively. It outperformed PointNet++ and PAConv and performed similarly to the original network. Regarding efficiency, the training time and inference time were 714.3 and 597.9 ms, respectively, which were reduced by 320.9 and 271.8 ms, respectively, compared to the original network. The improved network significantly accelerated the speed of feeding point clouds into the network while maintaining segmentation performance. We demonstrated the potential of virtual plants and deep learning methods in rapidly extracting plant phenotypes, contributing to the advancement of plant phenotype research

Real time medical image compression in embedded System

Dans le domaine des soins de santé, l'imagerie médicale a rapidement progressé et est aujourd'hui largement utilisés pour le diagnostic médical et le traitement du patient. La santé mobile devient une tendance émergente qui fournit des soins de santé et de diagnostic à distance. de plus, à l'aide des télécommunications, les données médicale incluant l'imagerie médicale et les informations du patient peuvent être facilement et rapidement partagées entre les hôpitaux et les services de soins de santé. En raison de la grande capacité de stockage et de la bande passante de transmission limitée, une technique de compression efficace est nécessaire. En tant que technique de compression d'image certifiée médicale, WAAVES fournit des taux de compression élevé, tout en assurant une qualité d'image exceptionnelle pour le diagnostic médical. Le défi consiste à transmettre à distance l'image médicale de l'appareil mobile au centre de soins de santé via un réseau à faible bande passante. Nos objectifs sont de proposer une solution de compression d'image intégrée à une vitesse de compression de 10 Mo/s, tout en maintenant la qualité de compression. Nous examinons d'abord l'algorithme WAAVES et évaluons sa complexité logicielle, basée sur un profilage précis du logiciel qui indique un complexité de l'algorithme WAAVES très élevée et très difficile à optimiser de contraintes très sévères en terme de surface, de temps d'exécution ou de consommation d'énergie. L'un des principaux défis est que les modules Adaptative Scanning et Hierarchical Enumerative Coding de WAAVES prennent plus de 90% du temps d'exécution. Par conséquent, nous avons exploité plusieurs possibilités d'optimisation de l'algorithme WAAVES pour simplifier sa mise en œuvre matérielle. Nous avons proposé des méthodologies de mise en œuvre possible de WAAVES, en premier lieu une mise en œuvre logiciel sur plateforme DSP. En suite, nous avons réalisé notre implémentation matérielle de WAAVES. Comme les FPGAs sont largement utilisés pour le prototypage ou la mise en œuvre de systèmes sur puce pour les applications de traitement du signal, leur capacités de parallélisme massif et la mémoire sur puce abondante permet une mise en œuvre efficace qui est souvent supérieure aux CPUs et DSPs. Nous avons conçu WAAVES Encoder SoC basé sur un FPGA de Stratix IV de chez Altera, les deux grands blocs coûteux en temps: Adaptative Scanning et Hierarchical Enumerative Coding sont implementés comme des accélérateurs matériels. Nous avons réalisé ces accélérateurs avec deux niveaux d'optimisations différents et les avons intégrés dans notre Encodeur SoC. La mise en œuvre du matérielle fonctionnant à 100MHz fournit des accélérations significatives par rapport aux implémentations logicielles, y compris les implémentations sur ARM Cortex A9, DSP et CPU et peut atteindre une vitesse de codage de 10 Mo/s, ce qui répond bien aux objectifs de notre thèse.In the field of healthcare, developments in medical imaging are progressing very fast. New technologies have been widely used for the support of patient medical diagnosis and treatment. The mobile healthcare becomes an emerging trend, which provides remote healthcare and diagnostics. By using telecommunication networks and information technology, the medical records including medical imaging and patient's information can be easily and rapidly shared between hospitals and healthcare services. Due to the large storage size and limited transmission bandwidth, an efficient compression technique is necessary. As a medical certificate image compression technique, WAAVES provides high compression ratio while ensuring outstanding image quality for medical diagnosis. The challenge is to remotely transmit the medical image through the mobile device to the healthcare center over a low bandwidth network. Our goal is to propose a high-speed embedded image compression solution, which can provide a compression speed of 10MB/s while maintaining the equivalent compression quality as its software version. We first analyzed the WAAVES encoding algorithm and evaluated its software complexity, based on a precise software profiling, we revealed that the complex algorithm in WAAVES makes it difficult to be optimized for certain implementations under very hard constrains, including area, timing and power consumption. One of the key challenges is that the Adaptive Scanning block and Hierarchical Enumerative Coding block in WAAVES take more than 90% of the total execution time. Therefore, we exploited several potentialities of optimizations of the WAAVES algorithm to simplify the hardware implementation. We proposed the methodologies of the possible implementations of WAAVES, which started from the evaluation of software implementation on DSP platforms, following this evaluation we carried out our hardware implementation of WAAVES. Since FPGAs are widely used as prototyping or actual SoC implementation for signal processing applications, their massive parallelism and abundant on-chip memory allow efficient implementation that often rivals CPUs and DSPs. We designed our WAAVES Encoder SoC based on an Altera's Stratix IV FPGA, the two major time consuming blocks: Adaptive Scanning and Hierarchical Enumerative Coding are designed as IP accelerators. We realized the IPs with two different optimization levels and integrated them into our Encoder SoC. The Hardware implementation running at 100MHz provides significant speedup compared to the other software implementation including ARM Cortex A9, DSP and CPU and can achieve a coding speed of 10MB/s that fulfills the goals of our thesis

AutoOpt: A Methodological Framework of Automatically Designing Metaheuristic Algorithms for Optimization Problems

Metaheuristics are gradient-free and problem-independent search methods. They have gained huge success in solving various optimization problems in academia and industry. Automated metaheuristic algorithm design is a promising alternative to human-made design. This paper proposes a methodological framework, AutoOpt, for automatically designing metaheuristic algorithms for optimization problems. AutoOpt consists of: (1) a bi-level criterion to evaluate the designed algorithms' performance; (2) a general schema of the decision space from where the algorithms will be designed; (3) a mixed graph- and real number-based representation to represent the designed algorithms; and (4) a model-free method to conduct the design process. AutoOpt benefits academic researchers and practical users struggling to design metaheuristic algorithms for optimization problems. A real-world case study demonstrates AutoOpt's effectiveness and efficiency