Flocking Behavior Research Articles

An improved classification of mineralized zones using particle swarm optimization: A case study from Dagh-Dali Zn[sbnd]Pb (±Au) prospect, Northwest Iran

Classification of mineralized areas into different geochemical classes in terms of prospectivity is crucial in the optimal management of exploration risk and costs. Machine learning (ML) algorithms can be served as appropriate alternatives for separating ore-related anomalies due to avoiding the assumptions of statistical distribution and compatibility with the multivariate nature of geochemical features. By hybridizing the ML with a metaheuristic algorithm called particle swarm optimization (PSO), this contribution aims to provide an innovative approach to optimize the classification of geochemical anomalies within the study area. The algorithm, PSO, is inspired by simulating the social behavior of flocks of birds in search of food. The Dagh-Dali ZnPb (±Au) mineral prospect in northwest Iran was subjected as a case study to examine the integrity of the proposed method. Mineralization-related features were extracted by applying principal component analysis (PCA) on metallogenic elements analyzed in soil samples as PC1 and PC2 with elemental assemblages of AgAuPbZn and PbZn, respectively. The silhouette index was employed to estimate the number of underlying geochemical clusters within the adopted feature space. To constitute a comparative analysis, two k-means clustering and PSO-based learning (PSO-L) algorithms were implemented to classify the gridded data of PC1 and PC2 within the study area. The results indicated that the use of PSO has improved the cost function of the clustering problem (up to 4%). Adapting the mineralization classes with the metallogenic evidence demonstrated by boreholes drilled in the study area indicated that PSO-L was superior to the traditional k-means method, improving the accurate estimation of subsurface mineralization classes by 7%. By overcoming the drawbacks of conventional methods for trapping at the local optima, PSO-based learning possesses the potential to highlight weak mineralization signals that are numerically located in boundary conditions. The results show that the proposed approach can serve as an effective medium for optimal modeling of geochemical classes and management of detailed exploration operations.

A thermodynamic analysis of end-directed particle flocking in chemical systems

We discuss the thermodynamics behind self-organizing Benzoquinone (BQ) particles on air–water interface. Experiments (Satterwhite-Warden et al., 2015; Chen et al., 2019; Satterwhite-Warden et al., 2019) reveal that BQ particles undergo rapid transient flocking behavior as they move around on the liquid surface. Flocks are seen to vary in size and their formation and stability appears to be dependent upon their shape. It is hypothesized that self organization of particles is a result of surface tension gradients in the two dimensional liquid surface resulting from the slow dissolution of the BQ particles. The current paper uses a mass-action kinetic framework to study the flocking of particles. Two dynamical models, with and without a reservoir, are proposed and analyzed through the thermodynamic lens of free energy, which informs us about dominant and spontaneous ‘reactions’ or flock formations in the system. Results of the model are in good agreement with experiment, revealing that irregular shaped BQ particles do indeed show far greater propensity to form flocks compared with regularly shaped particles and validating the mass-action framework as an appropriate tool to investigate this system.

Cucker-Smale flocking over cooperation-competition networks

This paper offers a novel distributed control scheme for the Cucker-Smale model to examine the leader-follower flocking behavior on networks with both cooperative and competitive interactions among agents. The core idea of the proposed scheme is to characterize the influence of cooperation and competition between agents through positive and negative weight functions with respect to the interaction distance, respectively. Based on the tool of infinite products of super-stochastic matrices, we establish mathematical inequalities about the intensities of cooperation and competition, and further obtain the algebraic conditions that depend on factors such as the initial states of agents, topological structure and weight functions. Moreover, we also discuss the influence of these factors on the least exponential convergence rate and the final position errors among agents. The obtained theoretical results are illustrated through simulation examples.

Multi-Cluster Flocking Behavior Analysis for a Delayed Cucker-Smale Model with Short-Range Communication Weight

Multi-Cluster Flocking Behavior Analysis for a Delayed Cucker-Smale Model with Short-Range Communication Weight

Phase transitions in biology: from bird flocks to population dynamics.

Phase transitions are an important and extensively studied concept in physics. The insights derived from understanding phase transitions in physics have recently and successfully been applied to a number of different phenomena in biological systems. Here, we provide a brief review of phase transitions and their role in explaining biological processes ranging from collective behaviour in animal flocks to neuronal firing. We also highlight a new and exciting area where phase transition theory is particularly applicable: population collapse and extinction. We discuss how phase transition theory can give insight into a range of extinction events such as population decline due to climate change or microbial responses to stressors such as antibiotic treatment.

Emergent behaviors of relativistic flocks on Riemannian manifolds

We present a relativistic counterpart of the Cucker–Smale (CS) model on Riemannian manifolds (manifold RCS model in short) and study its collective behavior. For Euclidean space, the relativistic Cucker–Smale (RCS) model was introduced in Ha et al. (2020) via the method of a rational reduction from the relativistic gas mixture equations by assuming space-homogeneity, suitable ansatz for entropy and principle of subsystem. In this work, we extend the RCS model on Euclidean space to connected, complete and smooth Riemannian manifolds by replacing usual time derivative of velocity and relative velocity by suitable geometric quantities such as covariant derivative and parallel transport along length-minimizing geodesics. For the proposed model, we present a Lyapunov functional which decreases monotonically on generic manifolds, and show the emergence of weak velocity alignment on compact manifolds by using LaSalle’s invariance principle. As concrete examples, we further analyze the RCS models on the unit sphere Sd and the hyperbolic space Hd. More precisely, we show that the RCS model on Sd exhibits a dichotomy in asymptotic spatial patterns, and provide a sufficient framework leading to the velocity alignment of RCS particles in Hd. For the hyperbolic space Hd, we also rigorously justify smooth transition from the RCS model to the CS model in any finite time interval, as speed of light tends to infinity.

Overview of multiobjective particle swarm optimization algorithm

In the real world, the development model of optimization problems tends to be diversified and large scale. Therefore, optimization technologies are facing severe challenges in terms of nonlinearity, multi-dimensionality, intelligence, and dynamic programming. Multiobjective optimization problems have multiple conflicting objective functions, so the unique optimal solution is impossible to obtain when optimizing, and multiple objective values must be considered to obtain a compromise optimal solution set. When traditional optimization methods treat complex multiobjective problems, such as those with nonlinearity and high dimensionality, good optimization results are difficult to ensure or even infeasible. The evolutionary algorithm is a method that simulates the natural evolution process and is optimized via group search technology. It has the characteristics of strong robustness and high search efficiency. Inspired by the foraging behavior of bird flocks in nature, the particle swarm optimization algorithm has a simple implementation, fast convergence, and unique updating mechanism. With its outstanding performance in the single-objective optimization process, particle swarm optimization has been successfully extended to multiobjective optimization, and many breakthrough research achievements have been made in combinatorial optimization and numerical optimization. Consequently, the multiobjective particle swarm algorithm has far-reaching research value in theoretical research and engineering practice. As a meta-heuristic optimization algorithm, particle swarm optimization is widely used to solve multiobjective optimization problems. This paper summarized the advanced strategies of the multiobjective particle swarm optimization algorithm. First, the basic theories of multiobjective optimization and particle swarm optimization were reviewed. Second, the difficult problems involving multiobjective optimization were analyzed. Third, the achievements in recent years were summarized from five aspects: optimal particle selection strategies, diversity maintenance mechanisms, convergence improvement measures, coordination methods between diversity and convergence, and improvement schemes of iterative formulas, parametric and topological structure. Finally, the problems to be solved and the future research direction of the multiobjective particle swarm optimization algorithm were presented.

A bibliometric review of cryptocurrencies as a financial asset

ABSTRACT Within a decade, cryptocurrencies have captured significant attention. After Bitcoin's emergence in 2008, new cryptocurrencies started to enter the financial market. We use bibliometric analysis to explore the cryptocurrency literature in the areas of business and management. We review and analyze 464 research articles through bibliometric measurements and social network analysis using Biblioshiny in R. Our study highlights the most influential authors, institutions, countries, and studies. Using the results of the social network analysis of the authors and countries, we illustrate the co-authorship and collaboration among authors from various countries at the institutional level and demonstrate how they have expanded the knowledge in this area. We identify four streams in the current cryptocurrency literature: (i) the determinants of cryptocurrency returns, (ii) the efficiency of cryptocurrencies, (iii) tests of portfolio diversification and sheep flock behaviour and (iv) the regulation, governance, and socio-economic impact of cryptocurrencies. Finally, we present an agenda for future research in the field.

Parrot Free-Flight as a Conservation Tool

The release of captive-raised parrots to create or supplement wild populations has been critiqued due to variable survival rates and unreliable flocking behavior. Private bird owners free-fly their parrots in outdoor environments and utilize techniques that could address the needs of conservation breed and release projects. We present methods and results of a free-flight training technique used for 3 parrot flocks: A large-bodied (8 macaws of 3 species and 2 hybrids), small-bodied (25 individuals of 4 species), and a Sun Parakeet flock (4 individuals of 1 species). Obtained as chicks, the birds were hand-reared in an enriched environment. As juveniles, the birds were systematically exposed to increasingly complex wildland environments, mirroring the learning process of wild birds developing skills. The criteria we evaluated for each flock were predation rates, antipredator behavior, landscape navigation, and foraging. No parrots were lost to predation or disorientation during over 500 months of free-flight time, and all birds demonstrated effective flocking, desirable landscape navigation, and wild food usage. The authors conclude that this free-flight method may be directly applicable for conservation releases, similar to the use of falconry methods for raptor conservation.

Loss of Cultural and Functional Diversity Associated With Birds Across the Urbanization Gradient in a Tropical City

Birds provide many ecosystem services to people, including provisioning, regulating and cultural services. People attribute multiple cultural values to ecosystems and biodiversity and the diversity of these cultural values can be considered as cultural diversity. While human-nature interactions occur more frequently in cities and urbanization negatively affects different facets of avian biodiversity, little is known about its consequence for cultural diversity. Here, we assess how the urbanization gradient in Campo Grande, a Brazilian city in the Cerrado biodiversity hotspot, affects functional and cultural diversity associated with birds and if functional and cultural diversity are congruent. We also investigate the relation between urbanization gradient with functional traits and cultural values, weighted by species abundance. We used a dataset based on bird surveyed in 61 landscapes along a gradient of impervious surface cover. To estimate functional and cultural diversity, we used indices that estimate richness and divergence of functional traits and cultural values. We found that urbanization affected functional and cultural richness negatively, while there was no effect on functional and cultural divergence. Functional and cultural richness and functional and cultural divergence were weakly, but significantly correlated. Bird species that nest on trees decreased and those that nest in artificial structures and on the ground increased along the impervious surface gradient. Body size, diet, habitat, mating system, flock behavior, and all cultural values (number of times the species was mentioned by football teams, music or poetry, city flags and anthems, and folklore tales) were not significantly affected by impervious surface. The negative relationship between impervious surface and bird cultural richness may indicate that people living in more urbanized areas experience nature less compared to people in less urbanized areas, which can affect their psychological well-being. In these highly urbanized areas, contact with culturally valued birds and cultural services provided by birds may also diminish. The negative relationship between functional richness and urbanization also indicate that highly urbanized areas may be losing important ecosystems services provided by birds.

Convergence of velocities for the short range communicated discrete-time Cucker–Smale model

Most existing literature about the discrete-time Cucker−Smale model focus on the asymptotic flocking behavior. When the communication weight has a long range, asymptotic flocking holds for any initial data. Actually, the velocity of every agent will exponentially converge to the same limit in this case. However, when the communication weight has a short range, asymptotic flocking does not exist for general initial data. In this note, we will prove the convergence of velocities for any initial data in the short range communication case. We first propose a new strategy about the convergence of velocities, and then show an important inequality about the velocity–position moment, according to which we will successfully prove the convergence of velocities and obtain the convergence rates for two kinds of communication weights. Besides, for some special initial data we show that the limits of velocities can be different from each other. Simulation results are given to validate the theoretical results.

A modified adaptive formation of UAV swarm by pigeon flock behavior within local visual field

UAV swarm track planning is crucial to ensure the smooth completion of the complex assigned tasks. UAV formation is of great significance to UAV swarm track planning. At present, UAV formation control methods have the defects of only set one formation type in the whole process, and poor adaptability in complex environment. This paper incorporates the formation decision function into the UAV control mode of virtual point formation, and classifies it at the expected angle based on the track planning model of pigeon swarm behavior, and puts forward a hierarchical early warning mechanism. In simulation experiment the method proposed in this paper can make UAV adjust its formation according to the environment, and avoid collisions between UAVs and various obstacles. In the adaptability of the algorithm, we fully consider the addition and deletions of UAV, static and dynamic obstacles, the interference of wind phenomenon and the diversity of tasks in the actual environment. The method proposed in this paper has made a beneficial attempt in the field of UAV control and expanded the application of UAV swarm.

Flocking Control of Multi-Agent Systems With Permanent Obstacles in Strictly Confined Environments

Abstract Considering the application of flocking control on connected and automated vehicle (CAV) systems, the persistent interactions between CAVs (flocking agents) and road boundaries (permanent obstacles) are critical due to flocking constraints in a strictly confined environment. However, the existing flocking theories attempt to model and animate natural flocks by considering temporary obstacles, which only describe interactions between agents and obstacles that would eventually disappear during flocking. This paper proposes a novel flocking control algorithm to extend existing flocking theories and guarantee the desired flocking coordination of multi-agent systems (e.g., CAV systems) with permanent obstacles (constraints). By analyzing comprehensive behaviors of flocks via Hamiltonian functions, a zero-sum obstacle condition is developed to ensure the satisfaction of permanent obstacle avoidance. Then, an additional control term representing the resultant forces of permanent obstacles is introduced to tackle interactions between agents and permanent obstacles. Demonstrated and compared through simulation results, a CAV system steered by the proposed flocking control protocol can successfully achieve the desired flocking behaviors with permanent obstacles avoidance in a three-lane traffic environment, which is failed by existing flocking control theories solely considering temporary obstacles.

On the Mathematics of Swarming: Emergent Behavior in Alignment Dynamics

We overview recent developments in the study of alignment hydrodynamics, driven by a general class of symmetric communication kernels. A main question of interest is to characterize the emergent behavior of such systems, which we quantify in terms of the spectral gap of a weighted Laplacian associated with the alignment operator. Our spectral analysis of energy fluctuation covers both long-range and short-range kernels and does not require thermal equilibrium (no closure for the pressure). In particular, in the prototypical case of metric-based short-range kernels, the spectral gap admits a lower-bound expressed in terms of the discrete Fourier coefficients of the radial kernel, which enables us to quantify an emerging flocking behavior for non-vacuous solutions. These large-time behavior results apply as long as the solutions remain smooth. It is known that global smooth solutions exist in one and two spatial dimensions, subject to sub-critical initial data. We settle the question for arbitrary dimension, obtaining non-trivial initial threshold conditions which guarantee existence of multiD global smooth solutions.

Event-triggered flocking control of multi-agent systems in presence of cyber attacks

We propose an event-triggered control system for multi-agent systems with the double-integrator network to achieve resilient flocking behavior in the presence of cyberattacks. The method can manage connectivity and increase the robustness of the topology graph via a three-state hybrid control. Each state of hybrid control has a distinct triggering condition. The developed event-triggered update rules can mitigate the influence of the noncooperative agents using the weighted mean subsequence reduced algorithm and reduce unnecessary communication among them. As a result, the performance of the system can effectively improve. Convergence of the velocity and orientation of the agents is guaranteed by using this type of control structure and resilient consensus protocol. We assume that the bound on the number of noncooperative agents in the neighbor sets of cooperative agents is known. The proposed scheme with the event-triggering rules can avoid Zeno behaviors inherently. Finally, a simulation example is worked out to demonstrate the effectiveness of the proposed approach.

A Modified Bat Algorithm for Solving Large-Scale Bound Constrained Global Optimization Problems

In the last two decades, the field of global optimization has become very active, and, in this regard, many deterministic and stochastic algorithms were developed for solving various optimization problems. Among them, swarm intelligence (SI) is a stochastic algorithm that is more flexible and robust and has had the ability to find an optimum solution for high-dimensional optimization and search problems. SI-based algorithms are mainly inspired by the social behavior of fish schooling or bird flocking. Among the SI-based algorithms, Bat algorithm (BA) is one of the recently developed evolutionary algorithms. It employs an echolocation behavior of microbats by varying pulse rates of emission and loudness to perform their search process. In this paper, a modified Bat algorithm (MBA) is developed. The main focus of the MBA is to further enhance the exploration and exploitation search abilities of the original Bat algorithm. The performance of the modified Bat algorithm (MBA) is examined over the benchmark functions designed for evolutionary algorithms competition in the special session of 2005 IEEE Congress on Evolutionary Computation. The used benchmark functions include the unimodal, multimodal, and hybrid benchmark functions with high dimensionality. Furthermore, the impact analysis with respect to different values of temperatures is conducted by executing the proposed algorithm twenty-five times independently by using each benchmark function with different random seeds.

Piling behaviour in Swiss layer flocks: Description and related factors

Smothering is a major concern within the Swiss layer industry as it can lead to a high number of animal losses. The underlying cause is piling behaviour (PB), a phenomenon where hens densely cluster together in the litter area. The aim of this study was to describe PB and events preceding PB. Furthermore, we investigated the relation of the number of piles, pile duration, and number of hens involved in a pile with time of day, flock colour, flock age, environmental factors, and flock responses to behaviour tests. We video recorded the corners of litter areas (floor) inside the barn and winter garden of 13 commercial Swiss layer flocks (5 white, 5 brown, 3 mixed layer hybrids), which were known to previously experienced problems with smothering. We recorded environmental data (air speed, spot temperature) in the observed corners and assessed flock-level responses to two behaviour tests (novel object test, stationary person test). From the video recordings, events preceding piling and piling characteristics were assessed at 20 and 30 weeks of age (w) at three times per day (0−5 h, >5-10 h, >10−15 h after lights on). Statistical analyses included generalized and linear mixed-effects models and Spearman correlations. Results showed that piling events were mainly preceded by single hen activities (77.9%) and non- hysterical mass movements (7.6%). More piles and the largest numbers of animals involved in piling occurred in white and brown flocks at >5−10 h after lights came on. The number of piles was lower and the number of involved animals and pile duration higher in 20 w compared to 30 w. No correlation was found between environmental factors and flock behaviour test responses with piling characteristics. Potential underlying causes for PB are numerous, though we provide and discuss likely mechanisms, including response facilitation, individual stimulus response, and anti-predation behaviour, based on our findings. Furthermore, PB could relate to diurnal behaviours, for example, dustbathing and hens laying floor eggs in the litter area.

Flow-mediated organization of two freely flapping swimmers

Abstract

An equation of state for insect swarms

Collective behaviour in flocks, crowds, and swarms occurs throughout the biological world. Animal groups are generally assumed to be evolutionarily adapted to robustly achieve particular functions, so there is widespread interest in exploiting collective behaviour for bio-inspired engineering. However, this requires understanding the precise properties and function of groups, which remains a challenge. Here, we demonstrate that collective groups can be described in a thermodynamic framework. We define an appropriate set of state variables and extract an equation of state for laboratory midge swarms. We then drive swarms through “thermodynamic” cycles via external stimuli, and show that our equation of state holds throughout. Our findings demonstrate a new way of precisely quantifying the nature of collective groups and provide a cornerstone for potential future engineering design.

Flocking-based decentralised double auction for P2P energy trading within neighbourhoods

Renewable energy sources are highly unreliable and create unreliable energy surplus and energy demands. These energy surpluses and demands can be traded frequently within neighbourhoods. This paper presents a trading algorithm to trade unreliable renewable energy frequently within neighbourhoods. We considered the flocking behaviour of birds and distributed double auction, and developed a trading algorithm to perform peer-to-peer(P2P) energy trading within neighbourhoods. Peers are prosumers which can be energy co-operatives, micro-grids, electric vehicles, charging stations, etc. We implemented our flocking-based decentralised double auction algorithm in Java and tested using a residential PV data set from California state in the USA. Our mathematical analysis shows that the peers are converging to average positions of neighbourhoods while performing P2P trading. Our experimental results showed that the proposed algorithm has a higher success rate and faster convergence to the success rate than the centralised double auction and distributed double auction presented in the literature. Moreover, nodes are converging to neighbourhoods. This guarantees trading within neighbourhoods. Further, we have observed that successful trading reaches around 80%.