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• Big headed ants (Pheidole megacephala) are able to forage in a Y-maze for food. • Previously, investigators modeled P. megacephala with a two-pheromone model. • We account for empirical results of P. megacephala foraging with a one-pheromone model. • Our one-pheromone model recapitulates key patterns and is biologically plausible. • This work demonstrates principles of sensory-cognitive modeling and ant foraging. In a 2009 paper, Dussutour et al. proposed that big headed ants (Pheidole megacephala) employ two attractant pheromones during foraging: one for exploration and another during food gathering. This claim was consistent with, and argued to be supported by, laboratory studies of ant exploration and food-gathering in a Y-maze apparatus. The authors measured foraging activity and colony foraging choice in terms of the number of ants choosing different branches over time, where experimental conditions modified the history of food availability at the end of each branch. They built a two-pheromone mathematical model to account for observed rates and proportions of ants traversing the left versus right branch. Here we show that the main reported experimental observations can be explained by a one-pheromone model. Our findings show that it is plausible, but unnecessary, to hypothesize that these ants employ two distinct pheromones in order to account for the two principal results of the Dussutour et al. study, and therefore, the study falls short of dispositive evidence for a two-pheromone model. More broadly, we highlight that patterns of animal behavior can be ambiguous with respect to sensory and cognitive mechanisms, hopefully motivating future modeling efforts that perform formal comparison across models with different structure. [ABSTRACT FROM AUTHOR]

Invasive ants threaten China's ecosystems and agriculture. Our review highlights the challenges faced and diverse strategies employed to combat these invaders, emphasizing the importance of international cooperation for safeguarding the environment and public well-being. [Display omitted] • Ecological & Economic Challenges of Exotic Ant Invasion in China. • In-Depth Analysis of China's Struggle with Exotic Ants. • Factors Behind Introduction & Establishment of Exotic Ants. • National Responses: Capacity Building, Research, Legislation. • Addressing Limitations, Enhancing Ant Invasion Strategies. The invasion of exotic ant species in China presents significant challenges to ecosystems, agriculture, and the economy. This paper provides an in-depth analysis of China's ongoing battle with exotic ants, examining the factors contributing to their introduction and establishment. It explores the responses implemented by the decision makers, including capacity building, establishment of research institutions, and legislative measures. Limitations and gaps in the current strategies are identified, highlighting the need for regional collaboration, increased awareness, and strengthened early detection and response systems. Furthermore, the paper emphasizes the importance of conducting research to understand various exotic ant species' ecological and economic impacts, facilitating the development of targeted and effective management approaches. By adopting a comprehensive and coordinated approach, China can mitigate the impacts of ant invasions and safeguard its biodiversity, agriculture, and economy. This analysis underscores the significance of collective efforts in addressing the ongoing battle with exotic ants and preserving the well-being of ecosystems, economies, and the society. [ABSTRACT FROM AUTHOR]

• Since the first description of the fairy circles 50 years ago, the cause is still hotly debated. • The first review paper in nearly 20 years on the fairy circles of the Namib Desert. • The contrasting main theories on the formation and maintenance of fairy circles are discussed. • The physical characteristics of fairy circles are described and critically reviewed. Since they were described for the first time 50 years ago, more than 30 peer-reviewed papers have been published on a number of extremely diverse theories as the cause of the formation of hundreds of thousands of nearly circular barren patches (fairy circles) found in southwestern Angola, Namibia and northwestern South Africa. However, scientists interested in finding an explanation for the cause and maintenance of these fairy circles in the driest parts of the world are still without agreement on the true reason(s) behind this intriguing phenomenon. In this review we firstly present the four main theories and explain their hypotheses on the formation and/or maintenance of fairy circles. Secondly, we summarise the previously described physical characteristics of the fairy circles, e.g. soil substrate, moisture content, shape, size, lifespan, dynamics and spatial patterning, and then present the supporting evidence as published by the proponents of the theories on vegetation self-arrangement, insects/ants, gas and Euphorbia allelopathy, as the causative drivers of this natural phenomenon. We also present the critical views of scientists on the theories they are not supporting. [ABSTRACT FROM AUTHOR]

Switching interacting particle systems studied in probability theory are the stochastic processes of hopping particles on a lattice made up of slow and fast particles, where the switching between these types of particles occurs randomly at a given transition rate. This paper explores how such stochastic processes involving multiple particles can model group behaviors of ants. Recent experimental research by the last author's group has investigated how ants switch between two types of primarily relied cues to select foraging paths based on the current situation. Here, we propose a discrete-time interacting random walk model on a square lattice, incorporating two types of hopping rules. Numerical simulation results demonstrate global changes in selected homing paths, transitioning from trailing paths of the 'pheromone road' to nearly optimal paths depending on the switching parameters. By introducing two types of order parameters characterizing the dependence of homing duration distributions on switching parameters, we discuss these global changes as phase transitions in ant path selections. We also study critical phenomena associated with continuous phase transitions. [ABSTRACT FROM AUTHOR]

The Aeronautical Radio Incorporated (ARINC) 664 Avionics Full Duplex Switched Ethernet (AFDX) data bus has been developed based on the Ethernet standard Institute of Electrical and Electronics Engineers (IEEE) 802.3, including redundancy, guaranteed bandwidth, and deterministic timing of transmit interval time to meet the increasing amounts of data exchange needs among aircraft equipment while reducing the harness weight. Isolated data tunnels, called Virtual Links (VLs), are established to route Ethernet frames from a single source to one or more destinations using end systems and switches that contain a static routing mechanism specific to ARINC 664 AFDX. A limited number of algorithmic approaches are presented in the literature to route VLs; however, they are proven in uncomplicated topologies. Depending on the technological development, the node amount consisting of End Systems (ESs), Switches, and Remote Data Concentrators (RDCs) in the aircraft data bus structure will increase enormously. Bandwidth utilization (BWU), bandwidth flexibility limit for further extensions, port usage, total harness weight, latency, and redundancy should be considered in addition to number of nodes to determine VLs' route paths in ARINC 664 AFDX networks. In such cases, determining optimal paths of VLs in ARINC 664 AFDX networks becomes a challenging problem to solve with the existing methods in the literature. This paper proposes an Ant Colony Optimization (ACO) based algorithm to determine optimal VL path routes in the complex topology of ARINC 664 AFDX networks considering multi-effective parameters. Experimental simulations are executed to confirm the validity of the presented algorithm in different case studies and show its applicability in the industry. The obtained results of the proposed algorithm outperform conventional algorithms existing in the literature in terms of bandwidth usage depending on predefined expansion capacity and solution time. The proposed algorithm also introduces novelty by considering the redundancy, total harness weight, and delay limit, which is deficient in the existing literature. • Propose new algorithm for virtual links routing in highly complex networks. • Considering multiple constraints of avionics industry for optimal routing. • New method determines faster optimal routes with less processing power. • Detecting and removing unnecessary network elements for reducing overall cost/weight. • Proposed algorithm eliminates merger and integration problems in large networks. [ABSTRACT FROM AUTHOR]

The objective of this paper is to start the validation of the coupled code system of the nodal neutronics solver Ants and the system code TRACE. The process consists of two exercises of well-known VVER-1000 coolant transient benchmarks: a core-vessel simulation of a main steam line break transient of the V1000CT-2 benchmark and a full plant simulation of a main coolant pump trip of the Kalinin-3 benchmark. The Ants-TRACE results are compared against other code solutions in the V1000CT-2 benchmark and measurement data in the Kalinin-3 benchmark. The results show good agreement in both benchmarks, with the deviations ranging mostly within the measurement error or the range of deviations of other published solutions. The results confirm the correct implementation of the coupling and successful modeling of relevant phenomena in coolant transient events in a VVER-1000 reactor. • Validation of Ants-TRACE for VVER-1000 transient analyses is started. • Coolant transients from V1000CT-2 and Kalinin-3 benchmarks are simulated. • Transients consist of a main steam line break event and a main coolant pump trip. • Results are compared to measurement data and published results of similar codes. [ABSTRACT FROM AUTHOR]

Collective foraging is critical for the ecological and evolutionary success of social insect colonies. To evaluate the impact of environmental stochasticity on the collective foraging dynamics of social ants, this paper develops a collective foraging model based on stochastic differential equations, in which communication among workers mainly relies on physical interactions. By analyzing the stochastic dynamics of the constructed model, we obtain relaxation conditions for the existence of an ergodic stationary distribution. Theoretical results suggest that environmental stochasticity leads to a decrease in the effective recruitment rate and average foraging period of foragers, ultimately negatively affecting the average recruit recruitment ability of single returning forager. [ABSTRACT FROM AUTHOR]

In view of various objectives and complex constraints in ship pipe route design (SPRD), the previous studies often convert the problem into a single objective optimization consisting of several weighted routing objectives, which cannot generate rich references for designers. In addition, the current CAD systems only provide an environment for manual design and simple automatic route design, resulting in low work performance and a huge workload. Therefore, this paper presents a new ship pipe route design method based on multi-objective ant colony optimization (MOACO). Firstly, the mathematical model and multi-objective definition of SPRD are formulated, and then the key technologies of the modified algorithm are introduced in detail, e.g., ant moving direction, heuristic information, pheromone update rules, tabu search and step backward strategy et al. On this basis, the MOACO framework and its internal ant routing process are presented. To route multiple pipes and branched pipes, a combination method based on Pareto solutions and human choice is given. Finally, the feasibility and efficiency of the proposed method are verified by the simulation and actual routing cases. • The MOACO can provide more reference solutions than the traditional methods, which satisfies pipe engineers better. • Compared with NSGA2, MOACO does not integrate other path-finding algorithm, nor does it need the help of connection points. • The idea of MOACO for solving SPRD have some generality, and it can be used to solve pipe route design in other fields. [ABSTRACT FROM AUTHOR]

In order to solve the shortcomings of ant colony algorithm in solving large-scale task scheduling problems in cloud computing, the convergence speed is slow and easy to fall into local optimal solutions. This paper designs an adaptive task scheduling algorithm for cloud computing based on ant colony algorithm. On the basis of the polymorphic ant colony algorithm, a pheromone adaptive update adjustment mechanism is added to improve the convergence speed of the algorithm and effectively avoid the emergence of local optimal solutions. The improved algorithm aims to solve a distribution plan with shorter execution time, lower cost and balanced load rate based on the tasks submitted by users. The traditional ant colony algorithm is compared with the improved adaptive ant colony algorithm through the cloud computing platform. Experimental data shows that the improved adaptive ant colony algorithm can quickly find the optimal solution to the cloud computing resource scheduling problem, shorten the task completion time, reduce the execution cost, and maintain the load balance of the entire cloud system center. The performance of this algorithm is better when solving large-scale task scheduling problems. [ABSTRACT FROM AUTHOR]

Obstacle avoidance behavior, as an important part of pedestrian evacuation, is of great significance to the study of evacuation efficiency. This paper investigates the definitions of desired direction in the social force model for obstacle avoidance of two types of pedestrians, including pedestrians with and without complete evacuation information. Ant colony optimization algorithm is adopted to navigate pedestrians with complete information. Herding behavior, individual preference affected by obstacles and walls are taken into consideration when defining the desired direction of pedestrians with local information. Simulation experiments are carried out to explore obstacle avoidance dynamics, the effects of herding behavior and visibility. Results indicate that pedestrians with complete information can not only avoid obstacles better, but also have the ability to choose the shorter route to the exit which could improve the leaving efficiency. Meanwhile, the trajectories of pedestrians with local information are always accompanied by some twists and turns, which could obviously lead to a waste of time. No matter whether the proportion of pedestrians with complete information is large or not, herding behavior can make the trajectories of pedestrians with local information smoother, and the individual behavior can make their trajectories more curved. Moreover, the larger the visibility radius is, the smoother trajectories become and the greater effective displacements for pedestrians with local information are. • The desired direction in the social force model for obstacle avoidance of pedestrian is defined. • Ant colony optimization algorithm is introduced to navigate pedestrians with complete information. • Obstacle avoidance dynamics, the effects of herding behavior and visibility are investigated. [ABSTRACT FROM AUTHOR]

Recently, Opposition-Inspired Learning strategies were proposed to improve the search process of ant-based algorithms to solve combinatorial problems. In this paper, we propose a collaborative framework of these strategies called Multiple Opposite Synergic Strategy for Ants (MOSSA). Because of each strategy has a different goal, we expect that the ants algorithm will benefit from their collaboration. The algorithm strongly uses the pheromone matrix for accomplishing stigmergy. To evaluate our framework, we use a recently proposed algorithm to solve Constraint Satisfaction Problems named Focused Ant Solver. Results and statistical analysis show that using MOSSA, Focused Ant Solver is able to solve more problems from the transition phase. • We propose an initialization strategy for ACO algorithms named MOSSA. • The strategy is a collaboration of three sub-colonies of ants. • Using our strategy Focused Ants Solver can solve more problems. • MOSSA-FAS and FAS are competitive to state-of-the-art ACO for CSPs. [ABSTRACT FROM AUTHOR]

• A two-stage algorithm for high-dimensional feature selection is proposed. • A advanced hybrid ant colony optimization algorithm is proposed. • Our method has good selection performance with short running time. Ant colony optimization (ACO) is widely used in feature selection owing to its excellent global/local search capabilities and flexible graph representation. However, the current ACO-based feature selection methods are mainly applied to low-dimensional datasets. For thousands of dimensional datasets, the search for the optimal feature subset (OFS) becomes extremely difficult due to the exponential increase of the search space. In this paper, we propose a two-stage hybrid ACO for high-dimensional feature selection (TSHFS-ACO). As an additional stage, it uses the interval strategy to determine the size of OFS for the following OFS search. Compared to the traditional one-stage methods that determine the size of OFS and search for OFS simultaneously, the stage of checking the performance of partial feature number endpoints in advance helps to reduce the complexity of the algorithm and alleviate the algorithm from getting into a local optimum. Moreover, the advanced ACO algorithm embeds the hybrid model, which uses the features' inherent relevance attributes and the classification performance to guide OFS search. The test results on eleven high-dimensional public datasets show that TSHFS-ACO is suitable for high-dimensional feature selection. The obtained OFS has state-of-the-art performance on most datasets. And compared with other ACO-based feature selection methods, TSHFS-ACO has a shorter running time. [ABSTRACT FROM AUTHOR]

• An improved adaptive updating ant colony optimization algorithm (AU-ACO) is proposed. • Feature bands extracted by AU-ACO is taken as input to reduce the complexity of model. • A new hybrid prediction model (ELM-P) combining ELM with PLSR is proposed. • This model can estimate rice chlorophyll content with extracted feature band. • The propose method has better superiority compared with other modeling methods. Using spectral information to detect chlorophyll content in rice canopy leaves quickly, non-destructively and accurately has a great practical significance for rice growth evaluation, precise fertilization and scientific management. In this paper, Japonica rice in northeast China is taken as the research object, and rice canopy hyperspectral data of key growth stages are obtained through plot experiments. Firstly, the standard normal variate (SNV) and SG smoothing method is used to preprocess the hyperspectral data, and based on the processed spectral data and ant colony optimization algorithm (ACO), an improved adaptive updating ant colony optimization algorithm (AU-ACO) is proposed to select feature bands of chlorophyll content by introducing an adaptive adjustment strategy of volatilization coefficient and optimal pheromone updating strategy in different stages, and compared with the standard ACO algorithm and full-band modeling method. Then, taking the extracted feature band and full-band as input, considering the advantages of linear model and nonlinear model, a hybrid prediction model (ELM-P) combining extreme learning machine (ELM) with partial least squares regression (PLSR) is proposed. In this model, PLSR is used to obtain the preliminary prediction of the chlorophyll content in rice, and the linear trend is obtained, and then the ELM with strong nonlinear approximation ability is used to predict the deviation of PLSR model, the final prediction value is obtained by superposition of the two outputs. In order to verify the superiority of the proposed model, PLSR and ELM prediction models are also established by taking the full-band and the feature bands by different extraction methods as input. The simulation experiment results show that under the same prediction model conditions, the feature bands extracted by proposed AU-ACO algorithm as input can reduce the complexity of the model and improve the prediction performance of the model. The determination coefficient (R 2- P) of testing set and the determination coefficient (R 2- C) of training set for each model are greater than 0.6785, among them, the ELM-P model with the feature band extracted by AU-ACO algorithm as input has the highest prediction accuracy, R 2- C and R 2- P are 0.7969 and 0.7918 respectively, RMSE- C and RMSE- P are 1.2969 and 1.1293 mg/L respectively, which can provide valuable reference for detecting and evaluating chlorophyll content in Japonica rice. [ABSTRACT FROM AUTHOR]

• Product attribute design changes will produce change propagation phenomenon. • Propagation path optimization can provide the decision support for designers. • EPN can describe the propagation relationship of product attribute design change. • Using EP-ACOA to optimize the propagation path has high efficiency. An effective and controlled design change in product attributes can improve product quality, promote product innovation, and meet customer demands. During design changes, the links among product attributes are intricate. A change in the design of one attribute may lead to a series of changes in other attributes, which result in the change propagation as well as the re-planning and re-design of the propagation path. Therefore, an Expanded Petri Net (EPN) model is constructed to describe the propagation relationship of product attribute design change in this paper. Firstly, the attribute change propagation rules are established based on change propagation impact values. Secondly, to optimize the dynamic scheme of change propagation path, the ant colony path optimization algorithm is integrated into the Extended Petri Net model (Expanded Petri Ant Colony Optimization Algorithm, EP-ACOA). In addition, the constraint test factor and the upper and lower limits of pheromone values are introduced into the algorithm to ensure the whole network traversal of the change propagation path and accelerate the solving speed of the model. Finally, taking the attribute design change of Langevin piezoelectric transducer in an ultrasonic knife as an example, the Petri Net fusion ant colony algorithm was verified to be able to quickly and effectively provide the optimal solution or approximate optimal solution to the propagation path problem of product attribute design changes. [ABSTRACT FROM AUTHOR]