Behavior Of Social Insects Research Articles

A Novel Bio-Inspired Algorithm Based on Social Spiders for Improving Performance and Efficiency of Data Clustering

Abstract Since the last decade, the collective intelligent behavior of groups of animals, birds or insects have attracted the attention of researchers. Swarm intelligence is the branch of artificial intelligence that deals with the implementation of intelligent systems by taking inspiration from the collective behavior of social insects and other societies of animals. Many meta-heuristic algorithms based on aggregative conduct of swarms through complex interactions with no supervision have been used to solve complex optimization problems. Data clustering organizes data into groups called clusters, such that each cluster has similar data. It also produces clusters that could be disjoint. Accuracy and efficiency are the important measures in data clustering. Several recent studies describe bio-inspired systems as information processing systems capable of some cognitive ability. However, existing popular bio-inspired algorithms for data clustering ignored good balance between exploration and exploitation for producing better clustering results. In this article, we propose a bio-inspired algorithm, namely social spider optimization (SSO), for clustering that maintains a good balance between exploration and exploitation using female and male spiders, respectively. We compare results of the proposed algorithm SSO with K means and other nature-inspired algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO) and improved bee colony optimization (IBCO). We find it to be more robust as it produces better clustering results. Although SSO solves the problem of getting stuck in the local optimum, it needs to be modified for locating the best solution in the proximity of the generated global solution. Hence, we hybridize SSO with K means, which produces good results in local searches. We compare proposed hybrid algorithms SSO+K means (SSOKC), integrated SSOKC (ISSOKC), and interleaved SSOKC (ILSSOKC) with K means+PSO (KPSO), K means+genetic algorithm (KGA), K means+artificial bee colony (KABC) and interleaved K means+IBCO (IKIBCO) and find better clustering results. We use sum of intra-cluster distances (SICD), average cosine similarity, accuracy and inter-cluster distance to measure and validate the performance and efficiency of the proposed clustering techniques.

Underlying mechanisms and ecological context of variation in exploratory behavior of the Argentine ant, Linepithema humile.

Uncovering how and why animals explore their environment is fundamental for understanding population dynamics, the spread of invasive species, species interactions, etc. In social animals, individuals within a group can vary in their exploratory behavior, and the behavioral composition of the group can determine its collective success. Workers of the invasive Argentine ant (Linepithema humile) exhibit individual variation in exploratory behavior, which affects the colony's collective nest selection behavior. Here, we examine the mechanisms underlying this behavioral variation in exploratory behavior and determine its implications for the ecology of this species. We first establish that individual variation in exploratory behavior is repeatable and consistent across situations. We then show a relationship between exploratory behavior and the expression of genes that have been previously linked with other behaviors in social insects. Specifically, we found a negative relationship between exploratory behavior and the expression of the foraging (Lhfor) gene. Finally, we determine how colonies allocate exploratory individuals in natural conditions. We found that ants from inside the nest are the least exploratory individuals, whereas workers on newly formed foraging trails are the most exploratory individuals. Furthermore, we found temporal differences throughout the year: in early-mid spring, when new resources emerge, workers are more exploratory than at the end of winter, potentially allowing the colony to find and exploit new resources. These findings reveal the importance of individual variation in behavior for the ecology of social animals.

Using Local Searches Algorithms with Ant Colony Optimization for the Solution of TSP Problems

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and other animals. Ants, in particular, have inspired a number of methods and techniques among which the most studied and successful is the general-purpose optimization technique, also known as ant colony optimization, In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Ant Colony Optimization (ACO) algorithm is used to arrive at the best solution for TSP. In this article, the researcher has introduced ways to use a great deluge algorithm with the ACO algorithm to increase the ability of the ACO in finding the best tour (optimal tour). Results are given for different TSP problems by using ACO with great deluge and other local search algorithms.

Immature Stages of the Masked Birch Caterpillar, Drepana arcuata (Lepidoptera: Drepanidae) With Comments on Feeding and Shelter Building

The masked birch caterpillar, Drepana arcuata (Lepidoptera: Drepanidae) is an excellent model for studying vibratory communication and sociality in larval insects. Vibratory communication occurs throughout development, but the functions of signals are reported to change as larvae change from gregarious to solitary lifestyles. To better understand the sensory ecology of these caterpillars, it is important to study their life history. Here, we describe the morphological and behavioral characteristics of larvae by confirming the number of instars, identifying their distinguishing morphological features, and noting changes in feeding and shelter construction. Five instars were confirmed based on the number of head capsules collected for individuals throughout development, and by using Dyar’s rule, which predicts the number of instars based on geometric growth patterns of head capsules. Frequency distributions of head capsule widths showed five separate peaks, indicating that this is a useful parameter for distinguishing between instars. Other morphological features including body length, shape, and banding patterns of head capsules, and morphology of thoracic verrucae are helpful in distinguishing among instars. Feeding behavior changes from leaf skeletonization in first and second instars to leaf cutting in fourth and fifth instars, with third instars transitioning between these feeding styles as they grow. Early instars typically construct communal silken shelters whereas late instars live solitarily in leaf shelters. These results provide essential life history information on the masked birch caterpillar that will enable future investigations on the proximate and ultimate mechanisms associated with social behavior and communication in larval insects.

Insights into the evolution, biogeography and natural history of the acorn ants, genus Temnothorax Mayr (hymenoptera: Formicidae)

BackgroundTemnothorax (Formicidae: Myrmicinae) is a diverse genus of ants found in a broad spectrum of ecosystems across the northern hemisphere. These diminutive ants have long served as models for social insect behavior, leading to discoveries about social learning and inspiring hypotheses about the process of speciation and the evolution of social parasitism. This genus is highly morphologically and behaviorally diverse, and this has caused a great deal of taxonomic confusion in recent years. Past efforts to estimate the phylogeny of this genus have been limited in taxonomic scope, leaving the broader evolutionary patterns in Temnothorax unclear. To establish the monophyly of Temnothorax, resolve the evolutionary relationships, reconstruct the historical biogeography and investigate trends in the evolution of key traits, I generated, assembled, and analyzed two molecular datasets: a traditional multi-locus Sanger sequencing dataset, and an ultra-conserved element (UCE) dataset. Using maximum likelihood, Bayesian, and summary-coalescent based approaches, I analyzed 22 data subsets consisting of 103 ingroup taxa and a maximum of 1.8 million base pairs in 2485 loci.ResultsThe results of this study suggest an origin of Temnothorax at the Eocene-Oligocene transition, concerted transitions to arboreal nesting habits in several clades during the Oligocene, coinciding with ancient global cooling, and several convergent origins of social parasitism in the Miocene and Pliocene. As with other Holarctic taxa, Temnothorax has a history of migration across Beringia during the Miocene.ConclusionsTemnothorax is corroborated as a natural group, and the notion that many of the historical subgeneric and species group concepts are artificial is reinforced. The strict form of Emery’s Rule, in which a socially parasitic species is sister to its host species, is not well supported in Temnothorax.

Entanglement-Gradient Routing for Quantum Networks

We define the entanglement-gradient routing scheme for quantum repeater networks. The routing framework fuses the fundamentals of swarm intelligence and quantum Shannon theory. Swarm intelligence provides nature-inspired solutions for problem solving. Motivated by models of social insect behavior, the routing is performed using parallel threads to determine the shortest path via the entanglement gradient coefficient, which describes the feasibility of the entangled links and paths of the network. The routing metrics are derived from the characteristics of entanglement transmission and relevant measures of entanglement distribution in quantum networks. The method allows a moderate complexity decentralized routing in quantum repeater networks. The results can be applied in experimental quantum networking, future quantum Internet, and long-distance quantum communications.

Genetics and Evolution of Social Behavior in Insects.

The study of insect social behavior has offered tremendous insight into the molecular mechanisms mediating behavioral and phenotypic plasticity. Genomic applications to the study of eusocial insect species, in particular, have led to several hypotheses for the processes underlying the molecular evolution of behavior. Advances in understanding the genetic control of social organization have also been made, suggesting an important role for supergenes in the evolution of divergent behavioral phenotypes. Intensive study of social phenotypes across species has revealed that behavior and caste are controlled by an interaction between genetic and environmentally mediated effects and, further, that gene expression and regulation mediate plastic responses to environmental signals. However, several key methodological flaws that are hindering progress in the study of insect social behavior remain. After reviewing the current state of knowledge, we outline ongoing challenges in experimental design that remain to be overcome in order to advance the field.

First record of lobed trace fossils in Brazil's Upper Cretaceous paleosols: Rhizoliths or evidence of insects and their social behavior?

This is the first report of trace fossils potentially associated with insect social behavior in sandy and well-drained paleosols of the Upper Cretaceous continental sequence of Brazil. The trace fossils consist of dozens of lobed and vertical structures cemented by CaCO3 and preserved mainly in full relief in paleosols of the Marilia Formation (Bauru Basin) in the state of Minas Gerais. The described ichnofossils are predominantly vertical, up to 2 m long, and are composed of horizontal lobed structures connected by vertical tunnel-like structures that intersect in the center and at the edges. The lobed structures range from 3 to 15 cm long and 2–6 cm thick. Two different hypotheses are analyzed to explain the origin of the trace fossils; the less probable one is that the structures are laminar calcretes associated with rhizoliths and rhizoconcretions. The hypothesis involving social insects was considered because the trace fossils described herein partially resemble a modern ant nest and the ichnofossil Daimoniobarax. The micromorphological analysis of the lobed and tunnel-like structures indicates modifications of the walls, such as the presence of inorganic fluidized linings, dark linings and oriented grains, supporting the hypothesis that they are chambers and shafts. The architecture and size of the reported nests suggest the possibility that social insect colonies existed during the Maastrichtian and are direct evidence of the social behavior and reproductive strategies of the Cretaceous pedofauna.

A COMPARISON OF OPTIMIZATION ALGORITHMS FOR STANDARD BENCHMARK FUNCTIONS

Optimization algorithms are the search methods that are inspired from the natural biological evolution and social behavior of animals, insects, birds and humans, etc. The need of introducing the optimization algorithm is to achieve a near optimal solutions for complex and non-linear problems for which numerical methods may fail. This paper compares the performance of ten popular optimization algorithms: Grasshopper Optimization Algorithm, Adaptive Cuckoo Search Algorithm, Bird Swarm Algorithm, Chaotic Ant Lion Optimization Algorithm, Adaptive Wind Driven Algorithm, Real Coded Genetic Algorithm, Particle Swarm Optimization, Teaching Learning Based Optimization, Whale Optimization Algorithm and Ant Colony Optimization. A performance comparison of these optimization algorithms has been made on basis of accuracy by using five standard benchmark functions and it has been found that Whale Optimization Algorithm is performing better than other algorithms.

The Neuropeptide Corazonin Controls Social Behavior and Caste Identity in Ants

Social insects are emerging models to study how gene regulation affects behavior because their colonies comprise individuals with the same genomes but greatly different behavioral repertoires. To investigate the molecular mechanisms that activate distinct behaviors in different castes, we exploit a natural behavioral plasticity in Harpegnathos saltator, where adult workers can transition to a reproductive, queen-like state called gamergate. Analysis of brain transcriptomes during the transition reveals that corazonin, a neuropeptide homologous to thevertebrate gonadotropin-releasing hormone, is downregulated as workers become gamergates. Corazonin is also preferentially expressed in workers and/or foragers from other social insect species. Injection of corazonin in transitioning Harpegnathos individuals suppresses expression of vitellogenin inthe brain and stimulates worker-like hunting behaviors, while inhibiting gamergate behaviors, such as dueling and egg deposition. We propose that corazonin is a central regulator of caste identity and behavior in social insects.

Deep evolutionary conservation of autism-related genes

E. O. Wilson proposed in Sociobiology that similarities between human and animal societies reflect common mechanistic and evolutionary roots. When introduced in 1975, this controversial hypothesis was beyond science's ability to test. We used genomic analyses to determine whether superficial behavioral similarities in humans and the highly social honey bee reflect common molecular mechanisms. Here, we report that gene expression signatures for individual bees unresponsive to various salient social stimuli are significantly enriched for autism spectrum disorder-related genes. These signatures occur in the mushroom bodies, a high-level integration center of the insect brain. Furthermore, our finding of enrichment was unique to autism spectrum disorders; brain gene expression signatures from other honey bee behaviors do not show this enrichment, nor do datasets from other human behavioral and health conditions. These results demonstrate deep conservation for genes associated with a human social pathology and individual differences in insect social behavior, thus providing an example of how comparative genomics can be used to test sociobiological theory.

Camel Herds Algorithm: a New Swarm Intelligent Algorithm to Solve Optimization Problems

Swarm Intelligence (SI) is a discipline that deals with artificial and naturalsystems which study the collective behaviors of social insects or animals. Camel HerdsAlgorithm (CHA) have been proposed as a new swarm intelligent algorithm in this work.The proposed algorithm depends on the behavior of the camel in the natural wild, takinginto consideration that there is a leader for each herd, food and water searchingdepending on humidity value with neighboring strategy. The Flexible Job Shop SchedulingProblem (FJSP) have been addressed as a case study to confirm the proposed algorithm.The trial result showed that the CHA is a good strategy to find the optimal solution inproblem space.

JEPM April 2017—Abstracts

There is variation in the extent to which childhood adverse experience affects adult individual differences in maternal behavior. Genetic variation in the animal foraging gene, which encodes a cGMP-dependent protein kinase, contributes to variation in the responses of adult fruit flies, Drosophila melanogaster, to early life adversity and is also known to play a role in maternal behavior in social insects. Here we investigate genetic variation in the human foraging gene (PRKG1) as a predictor of individual differences in the effects of early adversity on maternal behavior in two cohorts. We show that the PRKG1 genetic polymorphism rs2043556 associates with maternal sensitivity towards their infants. We also show that rs2043556 moderates the association between self-reported childhood adversity of the mother and her later maternal sensitivity. Mothers with the TT allele of rs2043556 appeared buffered from the effects of early adversity, whereas mothers with the presence of a C allele were not. Our study used the Toronto Longitudinal Cohort (N = 288 mother-16 month old infant pairs) and the Maternal Adversity and Vulnerability and Neurodevelopment Cohort (N = 281 mother-18 month old infant pairs). Our findings expand the literature on the contributions of both genetics and gene-environment interactions to maternal sensitivity, a salient feature of the early environment relevant for child neurodevelopment.

Vitellogenin in inflammation and immunity in social insects

Social insects, such as the honey bee and ants, form vast colonies that are only rivalled by human settlements. Living in dense, often stationary groups is prone to increase disease transmission. Yet, social insect queens and certain worker types can lead lengthy lives compared to the life span of most solitary insects. Social insects appear to have modified insulin/insulin like signaling pathway that regulates insect life history. This modification results in extremely elevated levels of the multifunctional lipoprotein vitellogenin in the individuals with longer life span. Vitellogenin is an egg-yolk precursor, but it also regulates caste-related behaviors in social insects, has shielding effects in inflammation and infection, and it is a mediator of transgenerational immunity. Here, we compile what is known about the life span and immune actions of vitellogenin and the evolution of this protein in the honey bee and ants. Recently we identified proteins homologous to vitellogenin in several Hymenopteran species, and showed that at least one of these vitellogenin-like proteins can have a protective role similar to vitellogenin in the honey bee. The newly identified vitellogenin homologs hint that the regulation of social insect life span can be more complex than thought before.

Tall trails: ants resolve an asymmetry of information and capacity in collective maintenance of infrastructure

Information asymmetry is common in many aspects of natural and economic systems. Collective self-organized behaviour in social insects may involve asymmetries in which an individual may possess information but only another individual is able to act on it. We examined this phenomenon on foraging trails of leaf-cutting ants (Atta colombica) to determine whether workers can resolve such an asymmetry. Cleared trails facilitate the transfer of resources and information but require constant maintenance to remove obstacles that arise in a dynamic environment. Overhead obstructions, which occur frequently along trails, present a specific asymmetry for collective behaviours. Returning foragers carrying leaf fragments above their heads may be hindered by such obstructions but must rely on unladen workers to remove them. Can leaf-cutting ants resolve this asymmetry? Do they do so in an indiscriminate or discriminate fashion? We created experimental overhead obstructions that hindered laden but not unladen ants. Clearing efforts by unladen workers were sensitive to the experience of their laden nestmates; they intensified attacks on a low barrier that impeded traffic but not on an equivalent barrier too high to strike leaf fragments. By contrast, a low barrier in the absence of laden ants or an ineffective visual ‘barrier’ did not elicit increased clearance attempts. Our results demonstrate that leaf-cutting ants can overcome an information asymmetry challenge, in which one group possesses the information that another must act upon. This allows the ants to adaptively modulate their trail-clearing efforts.

Implementation of Swarm Social Foraging Behavior in Unmanned Aerial Vehicle (UAV) Quadrotor Swarm

One of the novel approaches in multiple quadrotor control is swarm robotics. It aims to mimic social behaviors of animals and insects. This paper presents the physical implementation of the swarm social foraging behavior in unmanned aerial vehicle quadrotors. To achieve this, it first explores the basic behavior of aggregation. It is implemented over a quadrotor swarm test-bed that makes use of external motion capture cameras. The completed algorithm makes use of the artificial potential function model combined with the environment resource profile model. Results show successful demonstration of the social foraging algorithm with minimal error in position. Also, the proposed algorithm’s performance presents an increase in aggregation speed and time as the number of swarm member increases.

Efficient spatial coverage by a robot swarm based on an ant foraging model and the Lévy distribution

This work proposes a control law for efficient area coverage and pop-up threat detection by a robot swarm inspired by the dynamical behavior of ant colonies foraging for food. In the first part, performance metrics that evaluate area coverage in terms of characteristics such as rate, completeness and frequency of coverage are developed. Next, the Keller–Segel model for chemotaxis is adapted to develop a virtual-pheromone-based method of area coverage. Sensitivity analyses with respect to the model parameters such as rate of pheromone diffusion, rate of pheromone evaporation, and white noise intensity then identify and establish noise intensity as the most influential parameter in the context of efficient area coverage and establish trends between these different parameters which can be generalized to other pheromone-based systems. In addition, the analyses yield optimal values for the model parameters with respect to the proposed performance metrics. A finite resolution of model parameter values were tested to determine the optimal one. In the second part of the work, the control framework is expanded to investigate the efficacy of non-Brownian search strategies characterized by Levy flight, a non-Brownian stochastic process which takes variable path lengths from a power-law distribution. It is shown that a control law that incorporates a combination of gradient following and Levy flight provides superior area coverage and pop-up threat detection by the swarm. The results highlight both the potential benefits of robot swarm design inspired by social insect behavior as well as the interesting possibilities suggested by considerations of non-Brownian noise.

The Drosophila foraging gene human orthologue PRKG1 predicts individual differences in the effects of early adversity on maternal sensitivity

There is variation in the extent to which childhood adverse experience affects adult individual differences in maternal behavior. Genetic variation in the animal foraging gene, which encodes a cGMP-dependent protein kinase, contributes to variation in the responses of adult fruit flies, Drosophila melanogaster, to early life adversity and is also known to play a role in maternal behavior in social insects. Here we investigate genetic variation in the human foraging gene (PRKG1) as a predictor of individual differences in the effects of early adversity on maternal behavior in two cohorts. We show that the PRKG1 genetic polymorphism rs2043556 associates with maternal sensitivity towards their infants. We also show that rs2043556 moderates the association between self-reported childhood adversity of the mother and her later maternal sensitivity. Mothers with the TT allele of rs2043556 appeared buffered from the effects of early adversity, whereas mothers with the presence of a C allele were not. Our study used the Toronto Longitudinal Cohort (N=288 mother-16 month old infant pairs) and the Maternal Adversity and Vulnerability and Neurodevelopment Cohort (N=281 mother-18 month old infant pairs). Our findings expand the literature on the contributions of both genetics and gene-environment interactions to maternal sensitivity, a salient feature of the early environment relevant for child neurodevelopment.

Experimental Study of Ant Movement in a Straight Passageway under Stress Conditions

In the present study, we investigated whether single-file traffic of the ant Camponotus japonicus would exhibit a transition to traffic-jamming with increasing density under stress conditions. Previous work indicated that this transition did not occur in non-stressed ants, in contrast to data from pedestrians and vehicular traffic. Citronella oil was used to elicit ant movement from one end of a unidirectional passage to the other. The movements were recorded with a video camera, and the speed and distance travelled were extracted using image processing. We provided fundamental diagrams of ant traffic for small and large ants under stress conditions. Examination of the relationship between flow rate and density revealed no evidence of jamming, different from pedestrian and vehicular traffic. Concerning the speed-density relationship, surprisingly, in contrast with human fundamental diagrams (vehicular and pedestrian), we found that speed seemed to be constant with density within the experimental error. This study provided novel experimental data on ant traffic and might inform future studies of collective behavior of social insects and traffic systems.

Robophysical study of excavation in confined environments

Soil-dwelling social insects build complex nests. Nest excavation is performed by multiple animals simultaneously and is governed by local interactions of the workers with other nest-mates and their surroundings. To investigate collective confined excavation challenges, we built groups of robotic excavators capable of performing hours of autonomous tunnel excavation in a model cohesive granular medium. Excavator behavior was governed by a simple set of rules triggered by interactions with the surrounding environment and other robots. The rate of tunnel growth and energetic costs of excavation were measured for groups of different numbers operating in wide and narrow tunnels. To extend the results to systems with large numbers of robots, we developed a cellular automata model. Experiments and simulations showed that in sufficiently wide tunnels an increase in the size of the excavating group increased the excavation rates without a significant increase in the energy consumption per robot. A decrease in the tunnel width resulted in a decrease in the excavation rates and increase in the energetic costs of excavation. We attribute this effect to the emergence of multiple time-consuming interactions (clogs) among excavating robots in the confined spaces. Although in all situations clogs were resolvable, clog resolution took longer in the systems with larger number of robots and narrower tunnels. We expect that our robotic system can be used to investigate the behavior of social insects in confined spaces as well as inspire more sophisticated search-and-rescue robotics.