Honey Bee Swarm Research Articles

Honeybee-like collective decision making in a kilobot swarm

Drawing inspiration from honeybee swarms' nest-site selection process, we assess the ability of a kilobot robot swarm to replicate this captivating example of collective decision making. Honeybees locate the optimal site for their new nest by aggregating information about potential locations and exchanging it through their waggle dance. The complexity and elegance of solving this problem rely on two key abilities of scout honeybees: self-discovery and imitation, symbolizing and , respectively. We employ a mathematical model to represent this nest-site selection problem and program our kilobots to follow its rules. Our experiments demonstrate that the kilobot swarm can collectively reach consensus decisions in a decentralized manner, akin to honeybees. However, the strength of this consensus depends not only on the interplay between independence and interdependence but also on critical factors such as swarm density and the motion of kilobots. These factors enable the formation of a percolated communication network, through which each robot can receive information beyond its immediate vicinity. By shedding light on this crucial layer of complexity—the crowding and mobility conditions during the decision making—we emphasize the significance of factors typically overlooked but essential to living systems and life itself. Published by the American Physical Society 2024

Mitochondrial DNA variation in honey bee (Apis mellifera) colonies from Arkansas, U.S.

This study characterized the mitochondrial DNA (mtDNA) genetic variation in Arkansas honey bee, Apis mellifera L., by sequencing a portion of the mitochondrial cytochrome oxidase (COI-COII) intergenic region). The samples were primarily of hobbyist-managed origin (n = 180), as well as 32 feral colonies and two swarms. Of the 214 honey bee colonies and swarms sampled, 23 haplotypes were observed. The haplotypes were from the: A (African) (1.87%); C (South eastern European) (92.52%); M (Northern and Western European) (3.27%); and O (Near East and Middle East) lineages (2.34%). Six C lineage haplotypes were predominantly detected (n = 189, 88.31%), all of which are common in U.S. commercial queen breeder colonies. The remaining 27 honey bee samples represented 19 haplotypes, all of which are absent from commercial queen breeder colonies but have been observed in feral honey bee populations collected in other States. These haplotypes, particularly those from the M and O lineages, are likely hundred-year-old remnants of historical importations, surviving for generations despite the arrival of threats, such as Varroa mites. Understanding honey bee genetics and population structure are valuable for maintaining genetic diversity. Results from this study provide evidence that Arkansas honey bee populations differ from U.S. commercial queen breeder colonies. The 15 haplotypes detected in our Arkansas study absent from commercial queen breeder colonies could be important sources of genetic diversity in future honey bee breeding programs, highlighting the importance of State-level genetic surveys.

Optimising Routes Scheduling of Garbage Truck for Waste Collection using Artificial Bee Colony Algorithm

The issue of waste generation has attracted significant public attention in today’s societies, not only because waste production is increasing quantitatively but also because some products and components are becoming more complex. The growing problem of municipal solid waste management has become the most serious environmental issue. Population growth has resulted in the increase in the generation of solid waste, which has become a serious issue that must be resolved. Optimised route planning is one of the most important components of a smart waste management system. Route planning is a technological approach to determining the shortest routes through optimisation. The route optimisation problem commonly referred to as the vehicle routing problem (VRP), has been thoroughly studied to find the best possible route for a vehicle. This study aims to find the optimal routes and reduce the distance travelled by the garbage truck when collecting waste using Artificial Bee Colony algorithm. ABC algorithm is an optimization method based on the clever behaviour of honey bee swarms. The artificial bee colony in the ABC algorithm is divided into three groups which are employed bees, onlookers and scouts. To provide neighbouring solutions that are guaranteed to be possible, swap operator is used. Results show that by using 10 trucks in total as the vehicle, the optimal solution produced a total of optimal distance for 94.9542 km. The findings of this study fulfilled the intended objectives which are to find the optimal route and shortest distance for the vehicles.

Query-based image tagging model using ensemble learning with enhanced artificial bee colony optimization

Digital images make up most multimedia data and are analysed in computer vision applications. Daily uploads of millions of pictures to Internet archives such as satellite image repositories complicate multimedia content and image graphs. As feature vectors, content based image retrieval (CBIR) and image classification models represent high-level image viewpoints. Observing photos recognizes objects and evaluates their significance for image enhancement. To access the visual information of big datasets, efficiently retrieve and query picture graphs. The artificial bee colony (ABC) algorithm is inspired by the foraging behaviour of honeybee swarms. ABC is susceptible to laziness in convergence and local optimums, just like other optimization methods. This study created an enhanced ABC (EABC) model to enhance precision. This study presents query-based image tagging model using ensemble learning with EABC (QbITM-ELEABC) for CBIR for appropriately tagging images based on the query image. We examine a number of convolutional neural network (CNNs) with varying topologies that can be trained on the dataset with varying degrees of similarity. As representations, each network extracts class probability vectors from images. The final image representation is created by combining the ensemble's class probability vectors with image.

Editorial for the special issue “Control‐theoretic approaches for systems in the life sciences”

Editorial for the special issue “Control<scp>‐theoretic</scp> approaches for systems in the life sciences”

Assessment of advanced lures through gel and spray application for the attraction of honey bee swarms

Swarm control is of major importance to beekeepers worldwide. Many techniques and manipulations have been applied to try to decrease the swarming tendencies of colonies, attract swarms, and catch swarms as they leave their hives. The attractiveness of three different lures, based on a blend of attractive substances, is described and assessed: a paraffin gel and spray applications in two doses, are compared to evaluate their attractiveness to primary and secondary swarms. A total of 76 of 81 swarms (93.82%) were attracted by the lures; the gel application being the most effective, attracting 69.13% of all the swarms.

Increased Efficiency of the Artificial Bee Colony Algorithm Using the Pheromone Technique

Artificial Bee Colony (ABC) is a powerful metaheuristic algorithm inspired by the behavior of a honey bee swarm. ABC suffers from poor exploitation and, in some cases, poor exploration. Ant Colony Optimization (ACO) is another metaheuristic algorithm that uses pheromones as a guide for an ant to find its way. This study used a pheromone technique from ACO on ABC to enhance its exploration and exploitation. The performance of the proposed method was verified through twenty instances from TSPLIB. The results were compared with the original ABC method and showed that the proposed method leverages the performance of ABC.

Phase transition of a nonlinear opinion dynamics with noisy interactions

In several real \emph{Multi-Agent Systems} (MAS), it has been observed that only weaker forms of\emph{metastable consensus} are achieved, in which a large majority of agents agree on some opinion while other opinions continue to be supported by a (small) minority of agents. In this work, we take a step towards the investigation of metastable consensus for complex (non-linear) \emph{opinion dynamics} by considering the famous \undecided dynamics in the binary setting, which is known to reach consensus exponentially faster than the \voter dynamics. We propose a simple form of uniform noise in which each message can change to another one with probability $p$ and we prove that the persistence of a \emph{metastable consensus} undergoes a \emph{phase transition} for $p=\frac 16$. In detail, below this threshold, we prove the system reaches with high probability a metastable regime where a large majority of agents keeps supporting the same opinion for polynomial time. Moreover, this opinion turns out to be the initial majority opinion, whenever the initial bias is slightly larger than its standard deviation.On the contrary, above the threshold, we show that the information about the initial majority opinion is "lost" within logarithmic time even when the initial bias is maximum.Interestingly, using a simple coupling argument, we show the equivalence between our noisy model above and the model where a subset of agents behave in a \emph{stubborn} way.

Strength-mass scaling law governs mass distribution inside honey bee swarms

To survive during colony reproduction, bees create dense clusters of thousands of suspended individuals. How does this swarm, which is orders of magnitude larger than the size of an individual, maintain mechanical stability? We hypothesize that the internal structure in the bulk of the swarm, about which there is little prior information, plays a key role in mechanical stability. Here, we provide the first-ever 3D reconstructions of the positions of the bees in the bulk of the swarm using x-ray computed tomography. We find that the mass of bees in a layer decreases with distance from the attachment surface. By quantifying the distribution of bees within swarms varying in size (made up of 4000–10,000 bees), we find that the same power law governs the smallest and largest swarms, with the weight supported by each layer scaling with the mass of each layer to the approx 1.5 power. This arrangement ensures that each layer exerts the same fraction of its total strength, and on average a bee supports a lower weight than its maximum grip strength. This illustrates the extension of the scaling law relating weight to strength of single organisms to the weight distribution within a superorganism made up of thousands of individuals.

Implementation of Voter by using Artificial Bee Colony Algorithm for Fault Tolerant System

In a fault tolerant system, voting algorithm plays a major role in decision making. Inexact majority voting, majority voting, and weighted voters are most popular voting algorithms. In each of these techniques, redundant modules produce unpredictable outputs, and finding an agreement among these outputs is the functionality of voter. In this proposed system, voter is implemented using soft computing technique called ABC algorithm. The basic ABC algorithm is a very modest, adaptable and clear model. There are two categories of searchers in the hive of ABC; namely, unemployed searchers and employed searchers. Based on the intelligent performance of honey bee swarm, ABC is one of the optimization algorithms. The behaviours of bees are used to optimize weights of Triple Modular Redundant system, where each module plays an imperative role in the selection of voter. For evaluating the performance of voting algorithms, some performance measures have been defined; they are, safety and availability measures. ABC algorithm has been proposed to improve the safety and availability of the existing system.

Mean-Field Game for Collective Decision-Making in Honeybees via Switched Systems

In this article, we study the optimal control problem arising from the mean-field game formulation of the collective decision-making in honeybee swarms. A population of homogeneous players (the honeybees) has to reach consensus on one of two options. We consider three states: the first two represent the available options (or strategies), and the third one represents the uncommitted state. We formulate the continuous-time discrete-state mean-field game model. The contributions of this article are the following: 1) we propose an optimal control model where players have to control their transition rates to minimize a running cost and a terminal cost, in the presence of an adversarial disturbance; 2) we develop a formulation of the micro–macro model in the form of an initial-terminal value problem with switched dynamics; 3) we study the existence of stationary solutions and the mean-field Nash equilibrium for the resulting switched system; 4) we show that under certain assumptions on the parameters, the game may admit periodic solutions; and 5) we analyze the resulting microscopic dynamics in a structured environment where a finite number of players interact through a network topology.

Swarm of Honey Bees for Association Rule Mining Using CUDA

Association Rule mining (ARM) is well studied and famous optimization problem which finds useful rules from given transactional databases. Many algorithms already proposed in literature which shows their efficiency when dealing with different sizes of datasets. Unfortunately, their efficiency is not enough for handling large scale datasets. In this case, Bees swarm optimization algorithm for association rule mining is more efficient. These kinds of problems need more powerful processors and are time expensive. For such issues solution can be provided by graphics processing units (GPUs) and are massively multithreaded processors. In this case GPUs can be used to increase speed of the computation. Bees swarm optimization algorithm for association rule mining can be designed using GPUs in multithreaded environment which will efficient for given datasets.

Computational mechanisms affecting the efficiency of resource use in the honey bee swarm

AbstractThe honey bee swarm carried out its best‐of‐N nest site computations by operating more than a dozen different information processing loops in parallel and by recruiting more resources to provide greater precision in loops evaluating the better quality sites. The positive feedback effects of recurrent recruitment by means of waggle dance signalling amplified the utilisation of the swarm's energy, memory and carrier resources. The relatively strong negative feedback effects of various attenuation mechanisms tended to reduce resource use and therefore counter‐balanced amplification for long enough for a meaningful nest site survey and quorum decision to be made. Some information processing mechanisms such as exploration tendency, waggle dance signalling, site non‐specific attenuation, noise reduction, independent site evaluation, energy efficient coding, mixed precision processing, self‐organising computation and quorum decision making were found to profoundly influence the efficiency of resource use. Significant insights were also gained into extended cognition, dark data processing, information quality and resource leakage. Finally, the energy cost of acquiring and processing sensory information was estimated.

Comparative Research Directions of Population Initialization Techniques using PSO Algorithm

In existing meta-heuristic algorithms, population initialization forms a huge part towards problem optimization. These calculations can impact variety and combination to locate a productive ideal arrangement. Especially, for perceiving the significance of variety and intermingling, different specialists have attempted to improve the presentation of meta-heuristic algorithms. Particle Swarm Optimization (PSO) algorithm is a populace-based, shrewd stochastic inquiry strategy that is motivated by the inherent honey bee swarm food search mechanism. Population initialization is an indispensable factor in the PSO algorithm. To improve the variety and combination factors, rather than applying the irregular circulation for the introduction of the populace, semi-arbitrary successions are more helpful. This examination presents a thorough overview of the different PSO initialization approaches which are dependent on semi-arbitrary successions systems. In this precise review, the best in class in the populace instatement is uncovered. The procedures are classified by utilizing a theoretical model that parts the cycle of populace introduction into two phases: that is, right now expressly or certainly utilized for reinstatement in every single present approach. The deliberate investigation unveils the potential examination zones of populace introduction and, furthermore, research holes, despite the fact that the fundamental center is to give the headings to future upgrade and advancement around there. This paper gives a deliberate study identified with this calculated model for the cutting edge of exploration, which is talked about in the predefined writing to date. The study is envisioned to be useful in examining the PSO algorithm in detail for the specialist. Likewise, the paper finds the proficiency of numerous quasi-random sequences (QRS) based on initialization approaches by looking at their exhibition analyzed for sixteen notable benchmark test problems.

On Parameter Identification for Reaction-Dominated Pore-Scale Reactive Transport Using Modified Bee Colony Algorithm

Parameter identification is an important research topic with a variety of applications in industrial and environmental problems. Usually, a functional has to be minimized in conjunction with parameter identification; thus, there is a certain similarity between the parameter identification and optimization. A number of rigorous and efficient algorithms for optimization problems were developed in recent decades for the case of a convex functional. In the case of a non-convex functional, the metaheuristic algorithms dominate. This paper discusses an optimization method called modified bee colony algorithm (MBC), which is a modification of the standard bees algorithm (SBA). The SBA is inspired by a particular intelligent behavior of honeybee swarms. The algorithm is adapted for the parameter identification of reaction-dominated pore-scale transport when a non-convex functional has to be minimized. The algorithm is first checked by solving a few benchmark problems, namely finding the minima for Shekel, Rosenbrock, Himmelblau and Rastrigin functions. A statistical analysis was carried out to compare the performance of MBC with the SBA and the artificial bee colony (ABC) algorithm. Next, MBC is applied to identify the three parameters in the Langmuir isotherm, which is used to describe the considered reaction. Here, 2D periodic porous media were considered. The simulation results show that the MBC algorithm can be successfully used for identifying admissible sets for the reaction parameters in reaction-dominated transport characterized by low Pecklet and high Damkholer numbers. Finite element approximation in space and implicit time discretization are exploited to solve the direct problem.

Flow-mediated olfactory communication in honeybee swarms

Honeybee swarms are a landmark example of collective behavior. To become a coherent swarm, bees locate their queen by tracking her pheromones. But how can distant individuals exploit these chemical signals, which decay rapidly in space and time? Here, we combine a behavioral assay with the machine vision detection of organism location and scenting (pheromone propagation via wing fanning) behavior to track the search and aggregation dynamics of the honeybee Apis mellifera L. We find that bees collectively create a scenting-mediated communication network by arranging in a specific spatial distribution where there is a characteristic distance between individuals and directional signaling away from the queen. To better understand such a flow-mediated directional communication strategy, we developed an agent-based model where bee agents obeying simple, local behavioral rules exist in a flow environment in which the chemical signals diffuse and decay. Our model serves as a guide to exploring how physical parameters affect the collective scenting behavior and shows that increased directional bias in scenting leads to a more efficient aggregation process that avoids local equilibrium configurations of isotropic (nondirectional and axisymmetric) communication, such as small bee clusters that persist throughout the simulation. Our results highlight an example of extended classical stigmergy: Rather than depositing static information in the environment, individual bees locally sense and globally manipulate the physical fields of chemical concentration and airflow.

Regression test optimization and prioritization using Honey Bee optimization algorithm with fuzzy rule base

Regression testing is a maintenance level activity performed on a modified program to instill confidence in the software’s reliability. Prioritization of test case arranges the regression test suite to detect the faults earlier in the testing process. The test cases necessary for validating the recent changes and finding the maximum faults in minimum time are selected. In this manuscript, an optimization algorithm (Bee Algorithm) based on the intelligent foraging behavior of honey bee swarm has been proposed that can enhance the rate of fault detection in test case prioritization. The bee algorithm, along with the fuzzy rule base, reduces the test cases’ volume by selecting the test cases from the pre-existing test suite. The proposed algorithm developed for enhancing the fault detection rate in minimum time is inspired by the behavior of two types of worker bees, namely scout bees and forager bees. These worker bees are responsible for the maintenance, progress, and growth of the colony. The proposed approach is implemented on two projects. The prioritization result is quantified by using the average percentage of fault detection (APFD) metric. Compared with other existing prioritization techniques like no prioritization, reverse prioritization, random prioritization, and previous work, the proposed algorithm outperforms all in fault detection rate. The effectiveness of the proposed algorithm is represented by using the APFD graphs and charts.

Bio-Inspired Dynamic Collective Choice in Large-Population Systems: A Robust Mean-Field Game Perspective.

Inspired by the collective decision making in biological systems, such as honeybee swarm searching for a new colony, we study a dynamic collective choice problem for large-population systems with the purpose of realizing certain advantageous features observed in biology. This problem focuses on the situation where a large number of heterogeneous agents subject to adversarial disturbances move from initial positions toward one of the destinations in a finite time while trying to remain close to the average trajectory of all agents. To overcome the complexity of this problem resulting from the large population and the heterogeneity of agents, and also to enforce some specific choices by individuals, we formulate the problem under consideration as a robust mean-field game with non-convex and non-smooth cost functions. Through Nash equivalence principle, we first deal with a single-player H∞ tracking problem by taking the population behavior as a fixed trajectory, and then establish a mean-field system to estimate the population behavior. Optimal control strategies and worst disturbances, independent of the population size, are designed, which give a way to realize the collective decision-making behavior emerged in biological systems. We further prove that the designed strategies constitute ϵN -Nash equilibrium, where ϵN goes toward zero as the number of agents increases to infinity. The effectiveness of the proposed results are illustrated through two simulation examples.

Computational intelligence within a resource budget: The case of the honey bee (Apis mellifera) swarm

AbstractThis study investigated the resource budget available to a typical honey bee swarm as it gathered information and decided which was the best of some 13–34 potential nest sites to move into and become its new home. After a brief description of the decision‐making mechanisms involved, the utilization of the swarm's finite energy, memory and carrier resources is described. It was found that each individual scout decision maker only carried enough energy reserves to provide for 2.6 days of visits to evaluate distant sites and, effectively, only enough memory space to remember the location and quality of approximately one site. Scouts, who only formed 4.4% of the swarm bees, consumed 22.8% of the swarm's energy budget, showing that their best‐of‐N computations were energetically very expensive. A typical swarm of 12,000 bees only contained enough energy reserves to last for 13.5 days after which foraging had to restart.

Machine learning predicts honeybee swarms

Machine learning predicts honeybee swarms