Tootfinder

Multi-Level Damage-Aware Graph Learning for Resilient UAV Swarm Networks
Huan Lin, Chenguang Zhu, Lianghui Ding, Feng Yang
https://arxiv.org/abs/2506.09703

Multi-Level Damage-Aware Graph Learning for Resilient UAV Swarm Networks
Unmanned aerial vehicle (UAV) swarm networks leverage resilient algorithms to address communication network split issues and restore connectivity. However, existing graph learning-based resilient algorithms face over-aggregation and non-convergence problems caused by uneven and sparse topology under massive damage scenarios. To alleviate these problems, we propose a novel Multi-Level Damage-Aware Graph Learning (ML-DAGL) algorithm, which generates recovery trajectories by mining information fro…

A moped just went by that sounded like a swarm of angry bees.

Continuous-Time Output Feedback Adaptive Control for Stabilization and Tracking with Experimental Results
Mohammad Mirtaba, Ankit Goel
https://arxiv.org/abs/2506.08042

Continuous-Time Output Feedback Adaptive Control for Stabilization and Tracking with Experimental Results
This paper presents a continuous-time output feedback adaptive control technique for stabilization and tracking control problems. The adaptive controller is motivated by the classical discrete-time retrospective cost adaptive control algorithm. The particle swarm optimization framework automates the adaptive algorithm's hyper-parameter tuning. The proposed controller is numerically validated in the tracking problems of a double integrator and a bicopter system and is experimentally validated in…

Efficient Fireworks Algorithm Equipped with an Explosion Mechanism based on Student's T-distribution
Cen Shipeng, Tan Ying
https://arxiv.org/abs/2506.08484

Efficient Fireworks Algorithm Equipped with an Explosion Mechanism based on Student's T-distribution
Many real-world problems can be transformed into optimization problems, which can be classified into convex and non-convex. Although convex problems are almost completely studied in theory, many related algorithms to many non-convex problems do not work well and we need more optimization techniques. As a swarm intelligence optimization algorithm, the Fireworks Algorithm(FWA) has been widely studied and applied to many real-world scenarios, even including large language model fine-tuning. But th…

Aerial Shepherds: Enabling Hierarchical Localization in Heterogeneous MAV Swarms
Haoyang Wang, Jingao Xu, Chenyu Zhao, Yuhan Cheng, Xuecheng Chen, Chaopeng Hong, Xiao-Ping Zhang, Yunhao Liu, Xinlei Chen
https://arxiv.org/abs/2506.08408

Aerial Shepherds: Enabling Hierarchical Localization in Heterogeneous MAV Swarms
A heterogeneous micro aerial vehicles (MAV) swarm consists of resource-intensive but expensive advanced MAVs (AMAVs) and resource-limited but cost-effective basic MAVs (BMAVs), offering opportunities in diverse fields. Accurate and real-time localization is crucial for MAV swarms, but current practices lack a low-cost, high-precision, and real-time solution, especially for lightweight BMAVs. We find an opportunity to accomplish the task by transforming AMAVs into mobile localization infrastruct…

A limit on the mass of the Taurid Resonant Swarm at sub-100 meter sizes
Paul Wiegert, Denis Vida, David L. Clark, Auriane Egal, Richard Wainscoat, Robert Weryk
https://arxiv.org/abs/2506.03327

A limit on the mass of the Taurid Resonant Swarm at sub-100 meter sizes
We report on a pencil-beam survey of the Taurid Swarm, a possible concentration of bodies in the Taurid meteoroid stream associated with the 7:2 mean-motion resonance with Jupiter. Canada-France-Hawaii Telescope MegaCam observations reaching apparent magnitudes of 24.5 in the gri filter were taken over three nights. Rates of motion on the sky allowed for the quick elimination of main-belt objects from the over 1000 moving sources seen. Eight candidates with on-sky rates of motion consistent wit…

This https://arxiv.org/abs/2501.10306 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Micro-Macro Decomposition of Particle Swarm Optimization Methods
Solving non-convex minimization problems using multi-particle metaheuristic derivative-free optimization methods is still an active area of research. Popular methods are Particle Swarm Optimization (PSO) methods, that iteratively update a population of particles according to dynamics inspired by social interactions between individuals. We present a modification to include constrained minimization problems using exact penalization. Additionally, we utilize the hierarchical structure of PSO to in…

A Novel Coronary Artery Registration Method Based on Super-pixel Particle Swarm Optimization
Peng Qi, Wenxi Qu, Tianliang Yao, Haonan Ma, Dylan Wintle, Yinyi Lai, Giorgos Papanastasiou, Chengjia Wang
https://arxiv.org/abs/2505.24351

A Novel Coronary Artery Registration Method Based on Super-pixel Particle Swarm Optimization
Percutaneous Coronary Intervention (PCI) is a minimally invasive procedure that improves coronary blood flow and treats coronary artery disease. Although PCI typically requires 2D X-ray angiography (XRA) to guide catheter placement at real-time, computed tomography angiography (CTA) may substantially improve PCI by providing precise information of 3D vascular anatomy and status. To leverage real-time XRA and detailed 3D CTA anatomy for PCI, accurate multimodal image registration of XRA and CTA …

Last night 8 of us got together for Call of Cthulhu. First RPG for one of us, and a couple of us with decades of experience gaming together. Went off without a hitch. Other than my reporter getting killed by a swarm of vermin (one of two deaths).

Second-Order Characterization of Micro Doppler Radar Signatures of Drone Swarms
Anders Malthe Westerkam, Alba Spliid Damkj{\ae}r, Rasmus Erik Villadsen, Magnus {\O}rum Bastrup Poulsen, Troels Pedersen
https://arxiv.org/abs/2506.00497

Second-Order Characterization of Micro Doppler Radar Signatures of Drone Swarms
We investigate the second-order characteristics of the radar return signal from a swarm of rotor drones. We consider the case of a swarm of identical drones, with each a number of rotors comprised of a number of rotor blades. By considering the orientation and speed of each rotor as stochastic variables, we derive expressions for the autocorrelation function (ACF) and power spectral density (PSD). The ACF and PSD are in the form of an infinite series with coefficients that drop to zero at a pre…

EvoGrad: Metaheuristics in a Differentiable Wonderland
Beatrice F. R. Citterio, Andrea Tangherloni
https://arxiv.org/abs/2506.06320 https://

EvoGrad: Metaheuristics in a Differentiable Wonderland
Differentiable programming has revolutionised optimisation by enabling efficient gradient-based training of complex models, such as Deep Neural Networks (NNs) with billions and trillions of parameters. However, traditional Evolutionary Computation (EC) and Swarm Intelligence (SI) algorithms, widely successful in discrete or complex search spaces, typically do not leverage local gradient information, limiting their optimisation efficiency. In this paper, we introduce EvoGrad, a unified different…

This https://arxiv.org/abs/2404.19564 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Time, Travel, and Energy in the Uniform Dispersion Problem
We investigate the algorithmic problem of uniformly dispersing a swarm of robots in an unknown, gridlike environment. In this setting, our goal is to study the relationships between performance metrics and robot capabilities. We introduce a formal model comparing dispersion algorithms based on makespan, traveled distance, energy consumption, sensing, communication, and memory. Using this framework, we classify uniform dispersion algorithms according to their capability requirements and performa…

This https://arxiv.org/abs/2505.17115 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

Swarm Intelligence Enhanced Reasoning: A Density-Driven Framework for LLM-Based Multi-Agent Optimization
Recently, many approaches, such as Chain-of-Thought (CoT) prompting and Multi-Agent Debate (MAD), have been proposed to further enrich Large Language Models' (LLMs) complex problem-solving capacities in reasoning scenarios. However, these methods may fail to solve complex problems due to the lack of ability to find optimal solutions. Swarm Intelligence has been serving as a powerful tool for finding optima in the field of traditional optimization problems. To this end, we propose integrating sw…

Investigating the 2024 swarm like activity offshore Kefalonia Island aided by Machine Learning algorithms
V. Anagnostou, E. Papadimitriou, V. Karakostas, T. Back
https://arxiv.org/abs/2505.17221

Investigating the 2024 swarm like activity offshore Kefalonia Island aided by Machine Learning algorithms
In March 2024, a swarm like seismic activity occurred north of Kefalonia Island, in the central Ionian Islands area. Following a machine-learning aided workflow, we compiled an enhanced seismic catalog of 2495 low to moderate magnitude earthquakes throughout a 2 month period. Spatiotemporal analysis reveals a narrow epicentral distribution of nearly E-W alignment, approximately 5km long, much longer than the length anticipated by common scaling laws for the aftershock area extension of the stro…

A Comprehensive Survey on Bio-Inspired Algorithms: Taxonomy, Applications, and Future Directions
Shriyank Somvanshi, Md Monzurul Islam, Syed Aaqib Javed, Gaurab Chhetri, Kazi Sifatul Islam, Tausif Islam Chowdhury, Sazzad Bin Bashar Polock, Anandi Dutta, Subasish Das
https://arxiv.org/abs/2506.04238…

A Comprehensive Survey on Bio-Inspired Algorithms: Taxonomy, Applications, and Future Directions
Bio-inspired algorithms (BIAs) utilize natural processes such as evolution, swarm behavior, foraging, and plant growth to solve complex, nonlinear, high-dimensional optimization problems. This survey categorizes BIAs into eight groups: evolutionary, swarm intelligence, physics-inspired, ecosystem and plant-based, predator-prey, neural-inspired, human-inspired, and hybrid approaches, and reviews their core principles, strengths, and limitations. We illustrate the usage of these algorithms in mac…

This https://arxiv.org/abs/2309.03678 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Fully Onboard SLAM for Distributed Mapping with a Swarm of Nano-Drones
The use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing in applications ranging from surveillance and first-aid missions to industrial automation involving cooperation with other machines or humans. To maximize area coverage and reduce mission latency, swarms of collaborating drones have become a significant research direction. However, this approach requires open challenges in positioning, mapping, and communications to be addressed. This work describes a distributed mapping system ba…

This https://arxiv.org/abs/2505.04364 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

Benchmarking LLMs' Swarm intelligence
Large Language Models (LLMs) show potential for complex reasoning, yet their capacity for emergent coordination in Multi-Agent Systems (MAS) when operating under strict swarm-like constraints-limited local perception and communication-remains largely unexplored. Existing benchmarks often do not fully capture the unique challenges of decentralized coordination when agents operate with incomplete spatio-temporal information. To bridge this gap, we introduce SwarmBench, a novel benchmark designed …

This https://arxiv.org/abs/2410.16295 has been replaced.
link: https://scholar.google.com/scholar?q=a

Universal Approximation of Mean-Field Models via Transformers
This paper investigates the use of transformers to approximate the mean-field dynamics of interacting particle systems exhibiting collective behavior. Such systems are fundamental in modeling phenomena across physics, biology, and engineering, including opinion formation, biological networks, and swarm robotics. The key characteristic of these systems is that the particles are indistinguishable, leading to permutation-equivariant dynamics. First, we empirically demonstrate that transformers are…