Artificial Swarm Research Articles

Do nest sites limit wild honey bee colonies? Decoding swarm waggle dances to assess nest site availability

Abstract Nest sites are often considered to limit wild honey bee, Apis mellifera, colonies in Europe where wild colony densities are low (mean 0.26/km2). Nest site availability can be challenging to quantify directly, especially in urban areas and farmland where colonies nest in different substrates. Here we assess nest site availability indirectly across large areas (78.5 km2) of mixed habitat (67% farmland, 25% urban and 8% woodland) by decoding 3310 waggle dances produced by scouts on swarms. During summers of 2021 and 2022, 14 artificial swarms were set up in two study areas in East Sussex, England. Swarms advertised three to nine nest locations (mean of 5.5) at distances of 0.1–11.2 km (median 1.2 km) all within 0.4–15.2 daylight hours after dancing commenced (median 2.7). We estimated the total number of nest locations, including those not advertised, by quantifying the overlap in locations advertised by two swarms (a form of mark–recapture), which gave a mean density of approximately three nest sites per km2. The probability of swarms advertising nest sites per km2, calculated using simulations of dance variation, was an average of 42% higher in urban areas (0.018/km2), 78% higher in woodland (0.023/km2) and 12% lower in farmland (0.011/km2) than random expectation. After controlling for distance, swarms were still more likely than expected to advertise nest sites in woodland but only in one study area. Our results indicate that nest sites do not limit wild colonies in the study areas given that our conservative estimate of nest site density (3/km2) exceeds the density of wild colonies on nearby landed estates (2/km2) and other locations in Europe (0.26/km2).

Improved artificial bee colony algorithm for pressure source parameter inversion of Sakurajima volcano from InSAR data

A novel artificial bee colony algorithm was introduced for the eruption event of the Sakurajima volcano on August 9, 2020, to invert the magma source characteristics below the volcano based on the point source Mogi model. Considering that the Sakurajima volcano is surrounded by sea, all the deformation data are used to obtain the location and magma eruption volume of the volcano. In response to the weak local search capability of the artificial swarm algorithm, the difference between the global optimal individual and the un-roulette screened individual is introduced as the variance component in the onlooker stage. Detailed simulation experiments verify the improvement of the algorithm in terms of convergence speed. In real experiments, the Sakurajima volcano inversion shows closer fitting results and smaller residuals compared to the existing literature. Meanwhile, the convergence speed of the algorithm echoes with the simulation experiments.

Collective queuing motion of self-propelled particles with leadership and experience

The coordinated and ordered collective behavior arising from dispersed and local self-organizing interactions among individuals is widely observed in many biological communities. For populations engaging in group foraging or migration, complex societies with multiple hierarchical levels are common. A prevalent scenario involves two dominant levels, one corresponding to leaders and the other composed of followers. In this paper, we explore how a group can be influenced by a set of leaders to organize and move in a coordinated manner towards a preferred direction. Specifically, we balance social interactions among group members through two weighted factors, corresponding to the leadership and experience of leaders. Through computer simulations, we observe a linear relationship between the proportion of leaders guiding group movement and the group size, with larger groups requiring a higher proportion of leaders while maintaining initial density. Additionally, we identify an optimal leadership interval where group movement performance enhances with increasing leadership up to a certain point, beyond which it starts to decline. Similar patterns are observed concerning individual visual field, indicating the existence of an optimal interval for visual angle. Our findings not only contribute to understanding collective motion in natural biological systems but also provide new insights into effective leadership mechanisms in artificial swarm systems.

Hybrid Whale-fish swarm algorithm to optimize disassembly line balancing problem

The disassembly of waste products positively affects the environment, reduces the risk of environmental pollution, reduces the risk of spreading dangerous parts to the life of living entities, and provides the continuity of life on this planet. This research aims to contribute to the aforementioned of maintaining a clean environment by developing algorithms that help solve the problems of dismantling waste products and polluting the environment. In previous literature, researchers developed algorithms for artificial fish, improved artificial fish, and artificial whales to search for solutions with higher evidence instead From the local evidence,in this paper the artificial fish algorithm has been hybridized with the artificial whale algorithm for the purpose of developing solutions, and this method depends on the artificial fish flock first searching for food and here representing global solutions and avoiding predating traps (hooks with trap food), and after finding a flock The artificial swarm is the real food. The artificial whale sends bubbles to collect the swarm of artificial fish in the place of global high solutions, and gets them at once. The proposed algorithm was compared with the algorithms in the previous literature, and the results were as follows: Workstation was decreased by 0.5% to 22.2%, idle time was reduced by 82.5% to 84.0%, the demand index was reduced by 15% to 24.2%, and the hazardous index was reduced by 1.7% to 3.4%.

Modeling collective behaviors from optic flow and retinal cues

Animal collective behavior is often modeled with self-propelled particles, assuming each individual has “omniscient” knowledge of its neighbors. Yet, neighbors may be hidden from view and we do not know the effect of this information loss. To address this question, we propose a visual model of collective behavior where each particle moves according to bioplausible visual cues, in particular the optic flow. This visual model successfully reproduces three classical collective behaviors: swarming, schooling, and milling. This model offers a potential solution for controlling artificial swarms visually. Published by the American Physical Society 2024

Typhoon eye-shaped global convective flow field-induced colloidal motor swarm

In nature, living individuals such as fish, gather as much as possible in the wild to carry out life activities to improve their survival rate and environmental adaptability. Colloidal motors are often used as models to form various swarms and reveal their intrinsic mechanisms. Here, we rely on a global convective flow field to prompt the formation of a colloidal motor swarm that can absorb all motors in the range of light radiation without assembly limits. The peanut-shaped colloidal motor photocatalytically decomposes hydrogen peroxide and generates the chemical concentration gradient, which triggers convective flows at its waist in the x-z plane and attracts nearby motors. When motors converge towards the center of the light gradient field due to their positive phototaxis, the chemical gradient fluid resulting from motors’ non-uniform distribution in the entire domain induces a typhoon eye-shaped global convective flow field that flows from the edge of the light to the center. Compared with individuals, the global convective flow has a wider attraction range and gathers motors into a swarm. Moreover, with the number of collected motors increasing, the convective flow integrated in the x-z plane is stronger and ceaselessly accumulates more motors toward the z-axis. The formed swarm can also move to other areas under light control to collect more motors. Such collective phenomena provide a chance for understanding the emergence mechanism of biological swarms and applying artificial swarms to environmental governance, information collection, and drug release.

Kinematic and dynamic performances of artificial swarm systems: Aggregation, collision avoidance and compact formation

A novel control method for the artificial swarm system is proposed in this paper. To reflect the biological swarm performance (aggregation), a kinematic model of the artificial swarm system is established by introducing a special potential function. Then, the dynamic model is further obtained to satisfy more accurate dynamic swarm performances — collision avoidance and compact formation. The former can be realized by diffeomorphism transformation approach and a safety zone is guaranteed accordingly. As for the compact formation, we creatively convert it into a constraint-following problem between leader and followers. Based on the dynamic model, a novel control consisting of two parts is designed, including the model-based control and the adaptive robust control. On one hand, the trajectory tracking problem of the artificial swarm system is solved. On the other hand, the uncertainties in the artificial swarm system can be compensated perfectly. The deterministic performance, uniform boundedness and uniform ultimate boundedness, is guaranteed. The effectiveness of the collaborative control is verified by the proof based on Lyapunov approach and simulations of the artificial swarm system.

Review of Interdisciplinary Approach to Swarm Intelligence

Swarm intelligence is intelligence produced by multiple agents interacting with each other according to a simple set of rules, resulting in a system-wide intelligence. Such intelligence is found in a wide range of biological and social systems, and attempts have been made to understand the underlying principles through analytical approaches by biologists and sociologists and synthetic approaches by mathematical scientists and engineers. On the other hand, there are also attempts to construct artificial swarm intelligence systems that are not necessarily based on real-world phenomena. This review describes recent interdisciplinary research on swarm intelligence and its future prospects.

Micro/Nanorobotic Swarms: From Fundamentals to Functionalities.

Swarms, which stem from collective behaviors among individual elements, are commonly seen in nature. Since two decades ago, scientists have been attempting to understand the principles of natural swarms and leverage them for creating artificial swarms. To date, the underlying physics; techniques for actuation, navigation, and control; field-generation systems; and a research community are now in place. This Review reviews the fundamental principles and applications of micro/nanorobotic swarms. The generation mechanisms of the emergent collective behaviors among the micro/nanoagents identified over the past two decades are elucidated. The advantages and drawbacks of different techniques, existing control systems, major challenges, and potential prospects of micro/nanorobotic swarms are discussed.

Adaptive Robust Formation Control of Connected and Autonomous Vehicle Swarm System Based on Constraint Following.

This article proposes an adaptive robust formation control scheme for the connected and autonomous vehicle (CAV) swarm system by utilizing swarm property, diffeomorphism transformation, and constraint following. The control design is processed by starting from a 2-D dynamics model with (possibly fast) time varying but bounded uncertainty. The uncertainty bounds are unknown. For compact formation, the CAV system is treated as an artificial swarm system, for which the ideal swarm performance is taken as a desired constraint. By this, formation control is converted into a problem of constraint following and then a performance measure β is defined as the control object to evaluate the constraint following error. For collision avoidance, a diffeomorphism transformation on space measure between two vehicles is creatively performed, by which the space measure is positive restricted. For uncertainty handling, an adaptive robust control scheme is proposed to render the β-measure to be uniformly bounded and uniformly ultimately bounded, that is, drive the controlled (CAV) swarm system to follow the desired constraint approximatively. As a result, the system can achieve the ideal swarm performance; thereout, compact formation is realized, regardless of the uncertainty. The main contribution of this article is exploring a 2-D formation control scheme for (CAV) swarm system under the consideration of collision avoidance and time-varying uncertainty.

Anisotropy safety potential field model under intelligent and connected vehicle environment and its application in car-following modeling

Potential field theory, as a theory that can also be applied to vehicle control, is an emerging risk quantification approach to accommodate the connected and self-driving vehicle environment. Vehicles have different risk impact effects on other road participants in each direction under the influence of road rules. This variability exhibited by vehicles in each direction is not considered in the previous potential field model. Therefore, this paper proposed a potential field model that takes the anisotropy of vehicle impact into account: (1) introducing equivalent distances to separate the potential field area in the different directions before and after the vehicle; (2) introducing co-virtual forces to characterize the effect of the side-by-side travel phenomenon on vehicle car-following travel; (3) introducing target forces and lane resistance, which regress the control of desired speed to control the acceptable risk of drivers. The Next Generation Simulation (NGSIM) dataset is used in this study to create the model's initial parameter values based on the artificial swarm algorithm. The simulation findings indicate that when the vehicle is given the capacity to perceive the surrounding traffic environment, the suggested the anisotropic safety potential field model (ASPFM) performs better in terms of driving safety.

Consensus decision-making in artificial swarms via entropy-based local negotiation and preference updating

AbstractThis paper presents an entropy-based consensus algorithm for a swarm of artificial agents with limited sensing, communication, and processing capabilities. Each agent is modeled as a probabilistic finite state machine with a preference for a finite number of options defined as a probability distribution. The most preferred option, called exhibited decision, determines the agent’s state. The state transition is governed by internally updating this preference based on the states of neighboring agents and their entropy-based levels of certainty. Swarm agents continuously update their preferences by exchanging the exhibited decisions and the certainty values among the locally connected neighbors, leading to consensus towards an agreed-upon decision. The presented method is evaluated for its scalability over the swarm size and the number of options and its reliability under different conditions. Adopting classical best-of-N target selection scenarios, the algorithm is compared with three existing methods, the majority rule, frequency-based method, and k-unanimity method. The evaluation results show that the entropy-based method is reliable and efficient in these consensus problems.

Learning Swarm Interaction Dynamics From Density Evolution

In this article, we consider the problem of understanding the coordinated movements of biological or artificial swarms. In this regard, we propose a learning scheme to estimate the coordination laws of the interacting agents from observations of the swarm's density over time. We describe the dynamics of the swarm based on pairwise interactions according to a Cucker–Smale flocking model, and express the swarm's density evolution as the solution to a system of mean-field hydrodynamic equations. We propose a new family of parametric functions to model the pairwise interactions, which allows for the mean-field macroscopic system of integro-differential equations to be efficiently solved as an augmented system of partial differential equations. Finally, we incorporate the augmented system in an iterative optimization scheme to learn the dynamics of the interacting agents from observations of the swarm's density evolution over time. The results of this work can offer an alternative approach to study how animal flocks coordinate, create new control schemes for large networked systems, and serve as a central part of defense mechanisms against adversarial drone attacks.

Mixed-Reality Based Multi-Agent Robotics Framework for Artificial Swarm Intelligence Experiments

Mixed-Reality Based Multi-Agent Robotics Framework for Artificial Swarm Intelligence Experiments

Decentralized Control of DC–DC Converters for Redundant Onboard Power Supply Based on Artificial Swarm Intelligence Approaches

Decentralized Control of DC–DC Converters for Redundant Onboard Power Supply Based on Artificial Swarm Intelligence Approaches

An Ontology Based Knowledge Representation for Coordinated SS-bots

Collective behaviour empowers biological species to achieve remarkable swarm level results through distributed actions. For example, social spiders achieve coordinated activities that can lead to plausible outcomes such as preying, mating, or building webs. Developers of simulated swarm systems are increasingly moving away from insinuating individualistic robotic devices, gravitating towards collective swarms to achieve common goals. However, for this to happen, it is imperative to understand the principles that underpin successful simulated coordination of robotic devices in swarms. In this article, we investigate the principles behind artificial swarm systems built on the instinctive behaviours of simulated social spider-like devices (SS-bots). We classify these principles into six interconnected knowledge domains, including (a) environment, (b) SS-bot architecture, (c) SS-bot mission planning, (d) SS-bot communication, (e) SS-bot operators, and (f) metadata and swarm-level data. The key features of each domain are discussed, and an SS-bot ontology is proposed.

Formation tracking control design for uncertain artificial swarm systems under inequality constraints: Leakage and dead-zone type

The paper proposes a formation tracking control method for the uncertain artificial swarm systems under the inequality constraints. Not only can the agents perform swarm behaviors (e.g., convergence, formation and avoidance of collision), but they can also track the fixed targets in a constrained area (which is formulated as the inequality constraints, such as unilateral constraint and bilateral constraint.). The swarm behaviors are creatively considered as the servo constraints or the control objectives for the swarm agents. Based on the Udwadia–Kalaba (U–K) equation, those prescribed behaviors are realized by a model-based control design (that is the servo constraints force model-based feedforward control). To deal with the inequality constraints in the formation tracking process, a differential homeomorphism transformation is used to relieve the environmental constraints for the swarm agents. Moreover, the uncertainty of the swarm agents (i.e., the parameter uncertainty in modeling and the external disturbances) is considered, which is time-varying and unknown (but bounded). An uncertainty estimation method with dead-zone and leakage term is designed to calculate the possible upper bound of the uncertainty. In virtue of the estimated upper bound of the uncertainty, a robust control is designed for the uncertain swarm agents to obey the prescribed swarm behaviors in the formation tracking task. The system performances of the artificial swarm systems under the proposed control are theoretically guaranteed by a range of rigorous theorems and numerically verified by the simulations of three agents.

Hybrid Whale-fish swarm algorithm to optimize disassembly line balancing problem

The disassembly of waste products positively affects the environment, reduces the risk of environmental pollution, reduces the risk of spreading dangerous parts to the life of living entities, and provides the continuity of life on this planet.This research aims to contribute to the aforementioned of maintaining a clean environment by developing algorithms that help solve the problems of dismantling waste products and polluting the environment. In previous literature, researchers developed algorithms for artificial fish, improved artificial fish, and artificial whales to search for solutions with higher evidence instead From the local evidence, in this paper the artificial fish algorithm has been hybridized with the artificial whale algorithm for the purpose of developing solutions, and this method depends on the artificial fish flock first searching for food and here representing global solutions and avoiding predating traps (hooks with trap food), and after finding a flock The artificial swarm is the real food. The artificial whale sends bubbles to collect the swarm of artificial fish in the place of global high solutions, and gets them at once. The proposed algorithm was compared with the algorithms in the previous literature, and the results were as follows:Workstation was decreased by 0.5\% to 22.2\%, idle time was reduced by 82.5\% to 84.0\%, the demand index was reduced by 15\% to 24.2\%, and the hazardous index was reduced by 1.7\% to 3.4\%.

Microrobotic swarms for selective embolization.

Inspired by the collective intelligence in natural swarms, microrobotic agents have been controlled to form artificial swarms for targeted drug delivery, enhanced imaging, and hyperthermia. Different from these well-investigated tasks, this work aims to develop microrobotic swarms for embolization, which is a clinical technique used to block blood vessels for treating tumors, fistulas, and arteriovenous malformations. Magnetic particle swarms were formed for selective embolization to address the low selectivity of the present embolization technique that is prone to cause complications such as stroke and blindness. We established an analytical model that describes the relationships between fluid viscosity, flow rate, branching angle, magnetic field strength, and swarm integrity, based on which an actuation strategy was developed to maintain the swarm integrity inside a targeted region under fluidic flow conditions. Experiments in microfluidic channels, ex vivo tissues, and in vivo porcine kidneys validated the efficacy of the proposed strategy for selective embolization.

Macroeconomic Forecast Model System Based on Digital Information and Blockchain Technology

Since the reform and opening up, China’s economy has developed rapidly, becoming the second largest economy in the world. The state of macroeconomic development has a great influence on the government’s policy introduction and the investment decisions of individual institutions. Therefore, forecasting the macroeconomy is of great significance to the country. This paper aims to study the macroeconomic forecasting model system based on digital information and blockchain technology. This paper proposes an artificial neural network prediction algorithm based on digital information and blockchain technology, and the artificial neural network and particle swarm algorithm are combined to become the hybrid artificial neural network algorithm, and the conception of the establishment of the macroeconomic forecast model is proposed. The experimental results of this paper show that according to the prediction results of artificial neural network, the prediction and actual error in 2010 is 0.10, while the new method proposed in this paper predicts the error of 0.056. In 2016, the prediction error based on artificial neural network was 0.14, while the prediction result of the new method proposed in this paper showed an error of 0.008. In each year’s economic forecast, the neural network model of the new method proposed in this paper has higher prediction accuracy and smaller error. It can be seen that the neural network model based on artificial neural network and PSO algorithm proposed in this paper is beneficial to macroeconomic forecasting.