Pheromone Concentration Research Articles

Application of Ant Colony Optimization Algorithm Based on Triangle Inequality Principle and Partition Method Strategy in Robot Path Planning

Path planning is an important area of mobile robot research, and the ant colony optimization algorithm is essential for analyzing path planning. However, the current ant colony optimization algorithm applied to the path planning of mobile robots still has some limitations, including early blind search, slow convergence speed, and more turns. To overcome these problems, an improved ant colony optimization algorithm is proposed in this paper. In the improved algorithm, we introduce the idea of triangle inequality and a pseudo-random state transfer strategy to enhance the guidance of target points and improve the search efficiency and quality of the algorithm. In addition, we propose a pheromone update strategy based on the partition method with upper and lower limits on the pheromone concentration. This can not only improve the global search capability and convergence speed of the algorithm but also avoid the premature and stagnation phenomenon of the algorithm during the search. To prevent the ants from getting into a deadlock state, we introduce a backtracking mechanism to enable the ants to explore the solution space better. Finally, to verify the effectiveness of the proposed algorithm, the algorithm is compared with 11 existing methods for solving the robot path planning problem, including several ACO variants and two commonly used algorithms (A* algorithm and Dijkstra algorithm), and the experimental results show that the improved ACO algorithm can plan paths with faster convergence, shorter path lengths, and higher smoothness. Specifically, the algorithm produces the shortest path length with a standard deviation of zero while ensuring the most rapid convergence and the highest smoothness in the case of the shortest path in four different grid environments. These experimental results demonstrate the effectiveness of the proposed algorithm in path planning.

Virtual Reality Collision Detection Based on Improved Ant Colony Algorithm

In order to improve the performance in terms of detecting objects colliding in virtual reality, the ant colony algorithm was used to detect collisions. In the preliminary detection stage, the OBB bounding box and the spherical bounding box were used to detect the collision of objects, and the objects that may collide were selected. In the accurate detection stage, the model was sampled, and the feature pairs were used as the set to be detected for detecting collisions, the collision detection problem of the three-dimensional model was transformed into a nonlinear optimization problem of the distance between the feature pairs in the two-dimensional discrete space. The ant colony algorithm was introduced to solve the problem, and the pheromone concentration and update rules of the ant colony algorithm were optimized to improve the efficiency of the algorithm. The simulation results showed that, compared with the commonly used collision detection algorithms, our algorithm had high accuracy in detecting collisions and was less time-consuming.

An improved heuristic mechanism ant colony optimization algorithm for solving path planning

With the development of artificial intelligence algorithms, researchers are attracted to intelligent path planning due to its broad applications and potential development. The ant colony optimization (ACO) algorithm is one of the most widely used methods to solve path planning. However, the traditional ACO has some shortcomings such as low search efficiency, easy stagnation, etc. In this study, a novel variant of ACO named improved heuristic mechanism ACO (IHMACO) is proposed. The IHMACO contains four improved mechanisms including adaptive pheromone concentration setting, heuristic mechanism with directional judgment, improved pseudo-random transfer strategy, and dynamic adjustment of the pheromone evaporation rate. In detail, the adaptive pheromone concentration setting and heuristic mechanism with directional judgment are presented to enhance the purposiveness and reduce turn times of planned path. The improved pseudo-random transfer strategy and dynamic adjustment of the pheromone evaporation rate are introduced to enhance search efficiency and global search ability, further avoiding falling into local optimum. Subsequently, a series of experiments are conducted to test effectiveness of the four mechanisms and verify the performance of the presented IHMACO. Compared with 15 existing approaches for solving path planning, including nine variants of ACO and six commonly used deterministic search algorithms. The experimental results indicate that the relative improvement percentages of the proposed IHMACO in terms of the path turn times are 33.33%, 83.33%, 35.29%, 38.46%, and 38.46% respectively, demonstrating the superiority of IHMACO in terms of the availability and high-efficiency.

A self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism

Background and objectiveAs a representative type of cardiovascular disease, persistent arrhythmias can often become life-threatening. In recent years, machine learning-based ECG arrhythmia classification aided methods have been effective in assisting physicians with their diagnosis, but these methods have problems such as complex model structures, poor feature perception ability, and low classification accuracy. MethodsIn this paper, a self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism is proposed. This method does not distinguish between subjects when establishing the dataset in order to reduce the effect of differences in ECG signal features between individuals, thus improving the robustness of the model. When the classification is achieved, a correction mechanism is introduced to correct outliers caused by the accumulation of errors in the classification process in order to improve the classification accuracy of the model. According to the principle that the flow rate of gas can be increased under the convergence channel, a dynamically updated pheromone volatilization coefficient ρ, namely the increased flow rate ρ, is introduced to help the model converge more stably and faster. As the ants move, the next transfer target is selected by a truly self-adjusting transfer method, and the transfer probability is dynamically adjusted according to the pheromone concentration and the path distance. ResultsBased on the MIT-BIH arrhythmia dataset, the new algorithm achieved classification of five heart rhythm types, with an overall accuracy of 99.00%. Compared to other experimental models, the classification accuracy of the proposed method represents a 0.2% to 16.6% improvement, and compared to other current studies, the classification accuracy of the proposed method is 0.65% to 7.5% better. ConclusionsThis paper addresses the shortcomings of ECG arrhythmia classification methods based on feature engineering, traditional machine learning and deep learning, and presents a self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism. Experiments demonstrate the superiority of the proposed method compared to basic models as well as those with improved partial structures. Furthermore, the proposed method achieves very high classification accuracy with a simple structure and fewer iterations than other current methods.

Aggregation pheromones have a non-linear effect on oviposition behavior in Drosophila melanogaster

Female fruit flies (Drosophila melanogaster) oviposit at communal sites where the larvae may cooperate or compete for resources depending on group size. This offers a model system to determine how females assess quantitative social information. We show that the concentration of pheromones found on a substrate increases linearly with the number of adult flies that have visited that site. Females prefer oviposition sites with pheromone concentrations corresponding to an intermediate number of previous visitors, whereas sites with low or high concentrations are unattractive. This dose-dependent decision is based on a blend of 11-cis-Vaccenyl Acetate (cVA) indicating the number of previous visitors and heptanal (a novel pheromone deriving from the oxidation of 7-Tricosene), which acts as a dose-independent co-factor. This response is mediated by detection of cVA by odorant receptor neurons Or67d and Or65a, and at least five different odorant receptor neurons for heptanal. Our results identify a mechanism allowing individuals to transform a linear increase of pheromones into a non-linear behavioral response.

Semi-Open Multi-Distribution Center Path Planning with Time Windows

A well-planned robot dispatching platform reduces costs and increases efficiency for companies while also reducing carbon emissions and achieving sustainable development. At the moment, the solution to the difficulty of warehouse logistics is use of multiple distribution centers with autonomous mobile robots (AMR). To solve this problem, this paper establishes a semi-closed model of multiple distribution centers, considering the number of cycles and the number of vehicles. An improved ant colony algorithm is proposed to improve the heuristic function based on the node distance relationship to improve the quality of path search. Dynamic variable pheromone concentration and volatility factors are set to accelerate the convergence speed of the algorithm while effectively reducing the problem of the premature algorithm. The traditional ant colony algorithm and the improved ant colony algorithm are used to solve the established model. In addition, the results show that the traditional ant colony algorithm has a certain rate of dominance in the single-day cost of the closed distribution model, but the overall comprehensive cost is lower than that of the improved ant colony algorithm. The single-day cost of the semi-open multi-distribution center logistics and distribution model is lower than that of the closed multi-distribution center logistics and distribution model, and the 7 day average cost is reduced by 12%. The improved ant colony algorithm can save about 119 kWh of electricity under the same target volume requirement, which achieves the company’s goals of cost reduction and increased efficiency, as well as green and sustainable development.

Design and Development of a Data Structure Visualisation System Using the Ant Colony Algorithm

Aim: A data structure visualisation system uses object-oriented thinking and COM technology to dynamically and interactively simulate and track data structure algorithms and realize the dynamic synchronisation and visualisation of abstract data structures and algorithms using the data modelling function and self-test function. Background: Teaching data structures and algorithms is difficult because of their abstraction and dynamics; the use of icons in classroom teaching can be partially abstracted into intuition, but analysing the instantaneous dynamic characteristics of the object and the dynamic execution process of the algorithm is difficult. Objective: A data structure visualisation system employs a data modelling function, visual data structures, a user-friendly and flexible interface, and multipath features for multilevel users. Such systems can be designed more effectively by using the ant colony algorithm. Methods: The more the ants pass by a certain path, the higher the concentration of residual pheromone, and the higher the probability of the subsequent ant selecting that path. Therefore, the individuals in an ant colony communicate messages and cooperate with each other for foraging. Results: The resulting speedup ratio indicates that the speedup is smaller when the number of nodes is 100 or more; the acceleration is higher when the node reaches a certain scale, and the speedup ratio does not change considerably. In this study, the traffic simulation software VISSIM was used to generate road network data; the generated traffic data were analysed and used to design a traffic network data structure. Conclusion: The traffic network data model oriented to the ant colony algorithm was established through abstraction. Accordingly, a parallel ant colony algorithm based on cloud computing was implemented. Finally, a Hadoop cloud computing platform was established and used to run and test the parallel ant colony algorithm program; several experiments were conducted, and the experimental results were analysed concurrently.

Development of a two-stage method for segmenting the color images of urban terrain acquired from space optic-electronic observation systems based on the ant algorithm and the hough algorithm

The object of this study is the high level of errors of the first and second kind in the segmentation of images of urbanized areas acquired from space optoelectronic surveillance systems. The method of image segmentation of urbanized areas implies two stages and, unlike known ones: – takes into account each channel of brightness of the color space of the original image; – at the first stage, an ant algorithm is used; – image segmentation at the first stage is reduced to the calculation of the objective function, the areas of movement of ants, and the concentration of pheromone on the routes of ant movement. – at the second stage, the brightness and geometric shape of the elements of objects are taken into account; – contours and geometric primitives are defined in the Hough parameter space; – the objects of interest of the urbanized area in the space of the original image are determined. An experimental study into the segmentation of images of urbanized terrain acquired from space optoelectronic observation systems was carried out based on the ant algorithm and the Hough algorithm. The quality of image segmentation of the urbanized area was assessed. It was found that the error of the first kind when using the improved method of segmentation is reduced by 2.75 %. The error of the second kind is reduced by 3.91 % when using the improved method of segmentation. This reduction became possible due to the use of an improved method of segmenting the image of an urbanized area by the ant algorithm at the first stage. Compared to Canny's algorithm, the error of the first kind decreased by 8.9 %, and the error of the second kind decreased by 11.0 %. Methods for segmenting images of urbanized areas acquired from space optoelectronic surveillance systems can be implemented in software and hardware systems of image processing

Application of Swarm Intelligence Optimization Algorithm in Logistics Delivery Path Optimization under the Background of Big Data

Traditional logistics delivery route optimization algorithm has some problems such as long time to find the optimal route. Based on this, this paper discusses swarm intelligence optimization algorithm and logistics delivery route optimization. To solve the logistics vehicle routing problem, considering that the basic ACO (ant colony optimization) has the disadvantages of slow convergence speed and easy to fall into local optimum, this paper proposes a new hybrid population optimization algorithm and applies it to VRPTW (vehicle routing problem with time windows). In addition, the concept of crowding degree in AFSA (artificial fish swarm algorithm) is introduced into ACO. In the early stage of the optimization process, a strong crowding degree limit is set to ensure that most ants are not affected by pheromone concentration to conduct random optimization. The simulation results show that the AC (accuracy rate) of this algorithm is 95.08%, which is higher than the traditional PSO (particle swarm optimization) algorithm and general heuristic algorithm. The hybrid algorithm can effectively improve the optimization efficiency of VRPTW, lay a foundation for solving large-scale VRPTW, and provide new research ideas and methods. At the same time, the results fully show that the algorithm in this paper has certain advantages in performance, and it can be applied to logistics delivery route optimization.

Pheromones emitted by both female and male moths regulate coordination between the sexes for Agriphila aeneociliella (Lepidoptera: Crambidae).

The complex and efficient sex pheromone communication system in insects is essential for reproduction and for reproductive isolation of species. In moths, sex pheromone communication starts with male attraction to compounds emitted by females; only a few species act in the reverse. However, how the pheromones that are emitted by both sexes co-regulate and coordinate mate finding and mating remains unknown. Here, we identified both the male and female pheromones of Eastern Grass Veneer moth, Agriphila aeneociliella (Lepidoptera: Crambidae), and demonstrated their efficiency in manipulating behavioral responses of the opposite sex. Combining data from analysis of gas chromatography-electroantennogram detection, gas chromatography-mass spectrometry, and olfactory behavior assays, the female pheromone of A.aeneociliella was identified as (Z,Z,Z)-9,12,15-octadecatrienal and (Z)-9-hexadecenyl acetate, while the male pheromone was determined to be 1-nonanal. Both the 2 individual components of the female pheromone and their binary mixture were significantly attractive to males, and the 1-nonanal male pheromone induced strong electrophysiological responses in females and induced attraction of females in a Y-tube olfactory test. Depending on the concentration of 1-nonanal, its addition to the binary mixture of the female pheromone either enhanced (10-3 or 10-2 μg/μL) or reduced (1μg/μL) the aphrodisiac effect of the mixture on males. In wind-tunnel bioassays, different concentrations of pheromones, including the binary mixture of female pheromone and the mixture of male and female pheromones, had significant effects on male behavior. Our findings suggested that the blend of both female and male pheromones plays a significant role in the sexual communication system in some moths.

IATLR: Improved ACO and TOPSIS-Based Layering Routing Protocol for Underwater Acoustic Networks

Aiming at the challenges of underwater acoustic networks (UANs) such as low bandwidth, long propagation delay, dynamic network topology, and energy limitation, an improved ant colony optimization (IACO) and technique for order preference by similarity to an ideal solution (TOPSIS)-based layering routing (IATLR) protocol for UANs is proposed. In the IATLR protocol, the transfer probability of the ant colony optimization (ACO) is optimized by comprehensively considering multiple factors such as energy consumption, propagation distance, and forwarding angle. Moreover, the ACO is improved and the optimized ACO is combined with the TOPSIS method by using the pheromone concentration of ACO as the decision basis of TOPSIS. Furthermore, the multifactor decision ranking of the TOPSIS method is optimized by the transfer probability of the ACO. Consequently, the best next-hop node is determined. In addition, the energy consumption is balanced through the whole network by the node silence mechanism in the IATLR protocol. Simulation results show that the IATLR protocol has significant advantages compared with the depth-based routing for underwater sensor networks (DBR), layer-based energy-efficient routing (LEER), and depth and distance based layering routing protocol for underwater sensor networks (DDLR) routing protocols in terms of packet delivery rate (PDR), energy consumption, and end-to-end delay (EED). The PDR increases by 5%–30%, the EED decreases by about 1–7 s, and the average energy consumption (AEC) reduces by 0.5–6 J.

Improved ant colony algorithm for path planning of mobile robots based on compound prediction mechanism

For the issues of the ant colony algorithm (ACO) to solving the problems in mobile robot path planning, such as the slow optimization speed and the redundant paths in planning results, a high-precision improved ant colony algorithm (IPACO) with fast optimization and compound prediction mechanism is proposed. Firstly, aiming at maximizing the possibility of optimal node selection in the process of path planning, a composite optimal node prediction model is introduced to improve the state transition function. Secondly, a pheromone model with initialize the distribution and “reward or punishment” update mechanism is used to updates the global pheromone concentration directionally, which increases the pheromone concentration of excellent path nodes and the heuristic effect; Finally, a prediction-backward mechanism to deal with the “deadlock” problem in the ant colony search process is adopted in the IPACO algorithm, which enhance the success rate in the ACO algorithm path planning. Five groups of different environments are selected to compare and verify the performance of IPACO algorithm, ACO algorithm and three typical path planning algorithms. The experimental simulation results show that, compared with the ACO algorithm, the convergence speed and the planning path accuracy of the IPACO algorithm are improved by 57.69% and 12.86% respectively, and the convergence speed and the planning path accuracy are significantly improved; the optimal path length, optimization speed and stability of the IPACO algorithm are improved. Which verifies that the IPACO algorithm can effectively improve the environmental compatibility and stability of the ant colony algorithm path planning, and the effect is significantly improved.

A pedestrian evacuation model based on ant colony algorithm considering dynamic panic spread

<p style='text-indent:20px;'>To explain the evacuation behavior characteristics of people in panic, an improved cellular automata pedestrian evacuation model is proposed by combining with the SIS (Susceptible-Infected-Susceptible) epidemic model and ant colony algorithm. First, to explain the spread of panic in dynamic pedestrian evacuation, an improved SIS infection model that considers individual movement is proposed. At the same time, a quantitative formula for panic mood is given. The infection critical value of the SIS model is analyzed by mean field theory. Secondly, the ant colony algorithm is introduced based on considering exit distance, obstacles, panic, and other factors. These factors are mapped with heuristic function and pheromone concentration in the ant colony algorithm. Finally, the evacuation model and parameters such as evacuation time, pedestrian density, and panic spread are analyzed and discussed through simulation experiments. The simulation experiment results show that the evacuation model proposed in this research can better reflect the actual situation. Simultaneously, the critical rate of effective propagation is closely related to the crowd density in the evacuation area, and individual movement has a significant impact on the panic transmission behavior.</p>

Research on Global Path Planning of Robot Based on Ant Colony Algorithm and Gaussian Sampling

In response to the issue of the traditional ant colony algorithm (T‐ACO) with many iterations and slow convergence speed in robot global path planning, we propose an enhanced ant colony algorithm (S‐IACO) that incorporates Gaussian sampling. Firstly, the initial pheromone concentration contained in the raster map is preprocessed. Gaussian distribution sampling is adopted, and the sampling median value is used as the initial pheromone concentration of the raster map. Secondly, the heuristic function of the ant colony algorithm was improved. The number of ant colony iterations and the current path length were dynamically introduced into the heuristic function of the algorithm as influencing factors. Finally, this work redefined the pheromone update rule and introduced the concept of loss function by considering the influence of initialization pheromone on the results. By comparing with other improved ant colony algorithms, as well as PSO and GA algorithms, the S‐IACO algorithm proposed has fewer iterations and faster convergence speed.

A developed ant colony algorithm for cancer molecular subtype classification to reveal the predictive biomarker in the renal cell carcinoma

Background: Recently, researchers have been attracted in identifying the crucial genes related to cancer, which plays important role in cancer diagnosis and treatment. However, in performing the cancer molecular subtype classification task from cancer gene expression data, it is challenging to obtain those significant genes due to the high dimensionality and high noise of data. Moreover, the existing methods always suffer from some issues such as premature convergence. Methods: To address those problems, we propose a new ant colony optimization (ACO) algorithm called DACO to classify the cancer gene expression datasets, identifying the essential genes of different diseases. In DACO, first, we propose the initial pheromone concentration based on the weight ranking vector to accelerate the convergence speed; then, a dynamic pheromone volatility factor is designed to prevent the algorithm from getting stuck in the local optimal solution; finally, the pheromone update rule in the Ant Colony System is employed to update the pheromone globally and locally. To demonstrate the performance of the proposed algorithm in classification, different existing approaches are compared with the proposed algorithm on eight high-dimensional cancer gene expression datasets. Results: The experiment results show that the proposed algorithm performs better than other effective methods in terms of classification accuracy and the number of feature sets. It can be used to address the classification problem effectively. Moreover, a renal cell carcinoma dataset is employed to reveal the biological significance of the proposed algorithm from a number of biological analyses. Conclusion: The results demonstrate that CAPS may play a crucial role in the occurrence and development of renal clear cell carcinoma.

Polarization and cell-fate decision facilitated by the adaptor Ste50p in Saccharomyces cerevisiae.

In response to pheromone, many proteins localize on the plasma membrane of yeast cell to reform it into a polarized shmoo structure. The adaptor protein Ste50p, known as a pheromone signal enhancer critical for shmoo polarization, has never been explored systematically for its localization and function in the polarization process. Time-lapse single-cell imaging and quantitation shown here characterizes Ste50p involvement in the establishment of cell polarity. We found that Ste50p patches on the cell cortex mark the point of shmoo initiation, these patches could move, and remain associated with the growing shmoo tip in a pheromone concentration time-dependent manner until shmoo maturation. A Ste50p mutant impaired in patch localization suffers a delay in polarization. By quantitative analysis we show that polarization correlates with the rising levels of Ste50p, enabling rapid cell responses to pheromone that correspond to a critical level of Ste50p at the initial G1 phase. We exploited the quantitative differences in the pattern of Ste50p expression to correlate with the cell-cell phenotypic heterogeneity, showing Ste50p involvement in the cellular differentiation choice. Taken together, these findings present Ste50p to be part of the early shmoo development phase, suggesting that Ste50p may be involved with the polarisome in the initiation of polarization, and plays a role in regulating the polarized growth of shmoo during pheromone response.

Modified adaptive ant colony optimization algorithm and its application for solving path planning of mobile robot

As the key point for auto-navigation of mobile robot, path planning is a research hotspot in the field of robot. Generally, the ant colony optimization algorithm (ACO) is one of the commonly used approaches aiming to solve the problem of path planning of mobile robot. Nevertheless, the traditional ACO has the shortcomings such as slow convergence speed, inefficiency and easily fall into local optimal values. Thus, a novel variant of ACO is proposed in this study. In detail, a new heuristic mechanism with orientation information is firstly introduced to add direction guidance during the iteration process, further to advance the convergence speed of algorithm. Secondly, an improved heuristic function is presented to enhance the purposiveness and reduce the number of turn times of planned path. Then, an improved state transition probability rule is introduced to improve the search efficiency significantly and increase the swarm diversity. Moreover, a new method for unevenly distributing initial pheromone concentration is proposed to avoid blind searching. After integrating the four improvements, the new variation of ACO called modified adaptive ant colony optimization algorithm (MAACO) is formed. Subsequently, parameter optimization of MAACO is carried out. For verifying the effectiveness of the proposed MAACO, a series of experiments are conducted based on five static space environment modes and one dynamic environment mode. Comparing with 13 existing approaches for solving the problem of path planning of mobile robot, including several variants of ACO and two commonly used algorithms (A* algorithm and Dijkstra algorithm), the experimental results demonstrate the merits of MAACO in terms of decreasing the path length, reducing the number of turn times, and promoting the convergence speed. In detail, in all the static simulation experiments, the proposed MAACO generates the shortest path length with a standard deviation of zero, and achieves the least number of turn times within the smallest convergence generation. In terms of the five experiments, the average number of reducing turn times is two with a generally reduction ratio of 22.2% compared with the best existing results. The obtained results of MAACO prove its practicality and high-efficiency for path planning.

Heterogeneous communication network planning based on ant colony algorithm in smart grid

With the development of smart grids, highly automated power systems increasingly rely on communication networks, and research on the reliability, safety, and economy of heterogeneous communication networks is particularly important. Traditional network planning methods do not take into account network load problems, resulting in serious network congestion and long delays. Aiming at the above problems, this paper proposes a heterogeneous communication network planning method for a smart grid based on an improved ant colony algorithm. This method improves the original ant colony algorithm by updating the pheromone concentration of the network node in real time and realizes the planning of heterogeneous communication networks in the smart grid. Simulation results show that the proposed method has the advantages of avoiding local network congestion, reducing the average delay, and improving network stability.

An Optimal BP Neural Network Track Prediction Method Based on a GA–ACO Hybrid Algorithm

Ship position prediction is the key to inland river and sea navigation warning. Maritime traffic control centers, according to ship position monitoring, ship position prediction and early warning, can effectively avoid collisions. However, the prediction accuracy and computational efficiency of the ship’s future position are the key problems to be solved. In this paper, a path prediction model (GA–ACO–BP) combining a genetic algorithm, an ant colony algorithm and a BP neural network is proposed. The model is first used to perform deep pretreatment of raw AIS data, with the main body of the BP neural network as a prediction model, focused on the complementarity between genetic and ant colony algorithms, to determine the ant colony initialization pheromone concentration by the genetic algorithm, design the hybrid genetic–ant colony algorithm, and optimize this to the optimal weight and threshold of the BP neural network, in order to improve the convergence speed and effect of the traditional BP neural network. The test results show that the model greatly improves the fitness of track prediction, with higher accuracy and within a shorter time, and has a certain real-time and extensibility for track prediction of different river segments.

Extracting individual characteristics from population data reveals a negative social effect during honeybee defence.

Honeybees protect their colony against vertebrates by mass stinging and they coordinate their actions during this crucial event thanks to an alarm pheromone carried directly on the stinger, which is therefore released upon stinging. The pheromone then recruits nearby bees so that more and more bees participate in the defence. However, a quantitative understanding of how an individual bee adapts its stinging response during the course of an attack is still a challenge: Typically, only the group behaviour is effectively measurable in experiment; Further, linking the observed group behaviour with individual responses requires a probabilistic model enumerating a combinatorial number of possible group contexts during the defence; Finally, extracting the individual characteristics from group observations requires novel methods for parameter inference. We first experimentally observed the behaviour of groups of bees confronted with a fake predator inside an arena and quantified their defensive reaction by counting the number of stingers embedded in the dummy at the end of a trial. We propose a biologically plausible model of this phenomenon, which transparently links the choice of each individual bee to sting or not, to its group context at the time of the decision. Then, we propose an efficient method for inferring the parameters of the model from the experimental data. Finally, we use this methodology to investigate the effect of group size on stinging initiation and alarm pheromone recruitment. Our findings shed light on how the social context influences stinging behaviour, by quantifying how the alarm pheromone concentration level affects the decision of each bee to sting or not in a given group size. We show that recruitment is curbed as group size grows, thus suggesting that the presence of nestmates is integrated as a negative cue by individual bees. Moreover, the unique integration of exact and statistical methods provides a quantitative characterisation of uncertainty associated to each of the inferred parameters.