Showing papers on "Ant colony published in 2013"

Recurrent evolution of dependent colony foundation across eusocial insects.

Abstract: The spectacular success of eusocial insects can be attributed to their sophisticated cooperation, yet cooperation is conspicuously absent during colony foundation when queens are alone. Selection against this solitary stage has led to a dramatically different strategy in thousands of eusocial insect species in which colonies are started by groups of nestmates and the benefits of sociality are retained continuously. Dependent colony foundation (DCF) evolved recurrently multiple times across the ants, bees, and wasps, though its prevalence in termites remains unclear. We review adaptations at both the colony level (reproductive investment shifts from sexuals to workers) and the individual level (wingless queens evolve in ants), and other consequences for life history (invasiveness, parasite transmission). Although few studies have focused on DCF, the accumulated data from anecdotal reports, supported by indirect information including morphology, population genetics, and colony demographics, make it clear that this strategy is more diverse and widespread than is usually recognized.

The rewards of restraint in the collective regulation of foraging by harvester ant colonies

Abstract: Desert harvester ant colonies regulate their foraging activity and this collective behaviour appears to be under selection; colonies that forage less when conditions are poor have greater reproductive success, and the regulation of foraging behaviour appears to be inherited from parent to offspring colonies. Ant colonies are sometimes thought of as superorganisms, implying that they are subject to natural selection at a colony level. In a long-term (27-year) study of the association between collective behaviour and reproductive success in colonies of the red harvester ant, Pogonomyrmex barbatus, Deborah Gordon has found that, yes, they can show this superorganism characteristic. In times of drought, harvester ants tend not to forage as much as they do in times of plenty; they seem to bide their time until circumstances improve. This trait of restraint is passed on to daughter colonies, showing that it can indeed be regarded as a colony-level trait. Collective behaviour, arising from local interactions1, allows groups to respond to changing conditions. Long-term studies have shown that the traits of individual mammals and birds are associated with their reproductive success2,3,4,5,6, but little is known about the evolutionary ecology of collective behaviour in natural populations. An ant colony operates without central control, regulating its activity through a network of local interactions7. This work shows that variation among harvester ant (Pogonomyrmex barbatus) colonies in collective response to changing conditions8 is related to variation in colony lifetime reproductive success in the production of offspring colonies. Desiccation costs are high for harvester ants foraging in the desert9,10. More successful colonies tend to forage less when conditions are dry, and show relatively stable foraging activity when conditions are more humid. Restraint from foraging does not compromise a colony’s long-term survival; colonies that fail to forage at all on many days survive as long, over the colony’s 20–30-year lifespan, as those that forage more regularly. Sensitivity to conditions in which to reduce foraging activity may be transmissible from parent to offspring colony. These results indicate that natural selection is shaping the collective behaviour that regulates foraging activity, and that the selection pressure, related to climate, may grow stronger if the current drought in their habitat persists.

Electrical Load Pattern Grouping Based on Centroid Model With Ant Colony Clustering

Abstract: Load pattern clustering based on the shape of the electricity consumption is a key tool to provide enhanced knowledge on the nature of the consumption and assist meaningful customer partitioning. This paper presents new developments to group the load patterns using an initial set of centroids specified according to a user-defined centroid model. The original Electrical Pattern Ant Colony Clustering (EPACC) algorithm is illustrated, highlighting its characteristics and parameters, with centroids evolution during the iterative process until stabilization. The EPACC results are compared with those obtained from the classical k-means algorithm to group the representative load patterns taken from a set of non-residential customers in typical weekdays.

Ant colonies outperform individuals when a sensory discrimination task is difficult but not when it is easy

Abstract: “Collective intelligence” and “wisdom of crowds” refer to situations in which groups achieve more accurate perception and better decisions than solitary agents. Whether groups outperform individuals should depend on the kind of task and its difficulty, but the nature of this relationship remains unknown. Here we show that colonies of Temnothorax ants outperform individuals for a difficult perception task but that individuals do better than groups when the task is easy. Subjects were required to choose the better of two nest sites as the quality difference was varied. For small differences, colonies were more likely than isolated ants to choose the better site, but this relationship was reversed for large differences. We explain these results using a mathematical model, which shows that positive feedback between group members effectively integrates information and sharpens the discrimination of fine differences. When the task is easier the same positive feedback can lock the colony into a suboptimal choice. These results suggest the conditions under which crowds do or do not become wise.

A Cooperative Ant Colony Optimization-Genetic Algorithm approach for construction of energy demand forecasting knowledge-based expert systems

Abstract: Knowledge-based expert systems are becoming one of the major tools for scientists and engineers nowadays, since they have many attractive features and can be called upon to deal with real/complex engineering application problems which are not easy to solve by orthodox methods. Meanwhile, increasing worldwide demand for different types of energy requires development of advanced intelligent forecasting tools to provide a basis from which decisions and plans can be made. This study presents a new approach called ''Cooperative Ant Colony Optimization-Genetic Algorithm'' (COR-ACO-GA), to construct expert systems with the ability to model and simulate fluctuations of energy demand under the influence of related factors. The proposed approach has two main stages, at the first stage it uses genetic algorithms to generate data base of the expert system, and at the second stage it adopts ant colony optimization to learn linguistic fuzzy rules such that degree of cooperation between data base and rule base increases and consequently performance of the algorithm improves. We evaluate capability of COR-ACO-GA by applying it on three case studies of annual electricity demand, natural gas demand and oil products demand in Iran. Results indicate that COR-ACO-GA provides more accurate-stable results than adaptive neuro-fuzzy inference systems (ANFISs) and artificial neural networks (ANNs), and can assist decision makers in making appropriate decisions and plans for a coming period.

Invasive alien plants affect grassland ant communities, colony size and foraging behaviour

Abstract: Ants are dominant members of many terrestrial ecosystems and are regarded as indicators of environmental changes. However, little is known about the effects of invasive alien plants on ant populations, particularly as regards the density, spatial distribution and size of ant colonies, as well as their foraging behaviour. We addressed these questions in a study of grassland ant communities on five grasslands invaded by alien goldenrods (Solidago sp.) and on five non-invaded grasslands without this plant. In each grassland, seven 100 m2 plots were selected and the ant colonies counted. Ant species richness and colony density was lower in the plots on the invaded grasslands. Moreover, both of these traits were higher in the plots near the grassland edge and with a higher number of plant species in the grasslands invaded by goldenrods but not in the non-invaded ones. On average, ant colony size was lower on the invaded grasslands than the non-invaded ones. Also, ant workers travelled for longer distances to collect food items in the invaded areas than they did in the non-invaded ones, even after the experimental removal of some ant colonies in order to exclude the effect of higher colony density in the latter. Our results indicate that invasive alien goldenrods have a profound negative effect on grassland ant communities which may lead to a cascade effect on the whole grassland ecosystem through modification of the interactions among species. The invasion diminishes a major index of the fitness of ants, which is a colony’s size, and probably leads to increased foraging effort of workers. This, in turn, may have important consequences for the division of labour and reproductive strategies within ant colonies.

Biogenic amines and collective organization in a superorganism: neuromodulation of social behavior in ants.

Abstract: The ecological dominance of ants has to a great extent been achieved through their collective action and complex social organization. Ants provide diverse model systems to examine the neural underpinnings of individual behavior and group action that contribute to their evolutionary success. Core elements of ant colony structure such as reproductive and ergonomic division of labor, task specialization, and social integration are beginning to be understood in terms of cellular neuroanatomy and neurochemistry. In this review we discuss the neuroethology of colony organization by focusing on the role of biogenic amines in the control of social behavior in ants. We examine the role of neuromodulation in significant sociobiological characteristics of ants, including reproductive hierarchies, colony foundation, social food flow, nestmate recognition, territoriality, and size- and age-related sensory perception and task performance as well as the involvement of monoamines in collective intelligence, the ultimate key to the global dominance of these remarkable superorganisms. We conclude by suggesting future directions for the analysis of the aminergic regulation of behavior and social complexity in ants.

Negative feedback in ants: crowding results in less trail pheromone deposition

Abstract: Crowding in human transport networks reduces efficiency. Efficiency can be increased by appropriate control mechanisms, which are often imposed externally. Ant colonies also have distribution networks to feeding sites outside the nest and can experience crowding. However, ants do not have external controllers or leaders. Here, we report a self-organized negative feedback mechanism, based on local information, which downregulates the production of recruitment signals in crowded parts of a network by Lasius niger ants. We controlled crowding by manipulating trail width and the number of ants on a trail, and observed a 5.6-fold reduction in the number of ants depositing trail pheromone from least to most crowded conditions. We also simulated crowding by placing glass beads covered in nest-mate cuticular hydrocarbons on the trail. After 10 bead encounters over 20 cm, forager ants were 45 per cent less likely to deposit pheromone. The mechanism of negative feedback reported here is unusual in that it acts by downregulating the production of a positive feedback signal, rather than by direct inhibition or the production of an inhibitory signal.

Ancient host shifts followed by host conservatism in a group of ant parasitoids

Abstract: While ant colonies serve as host to a diverse array of myrmecophiles, few parasitoids are able to exploit this vast resource. A notable exception is the wasp family Eucharitidae, which is the only family of insects known to exclusively parasitize ants. Worldwide, approximately 700 Eucharitidae species attack five subfamilies across the ant phylogeny. Our goal is to uncover the pattern of eucharitid diversification, including timing of key evolutionary events, biogeographic patterns and potential cophylogeny with ant hosts. We present the most comprehensive molecular phylogeny of Eucharitidae to date, including 44 of the 53 genera and fossil-calibrated estimates of divergence dates. Eucharitidae arose approximately 50 Ma after their hosts, during the time when the major ant lineages were already established and diversifying. We incorporate host association data to test for congruence between eucharitid and ant phylogenies and find that their evolutionary histories are more similar than expected at random. After a series of initial host shifts, clades within Eucharitidae maintained their host affinity. Even after multiple dispersal events to the New World and extensive speciation within biogeographic regions, eucharitids remain parasitic on the same ant subfamilies as their Old World relatives, suggesting host conservatism despite access to a diverse novel ant fauna.

Signals Can Trump Rewards in Attracting Seed-Dispersing Ants

Abstract: Both rewards and signals are important in mutualisms. In myrmecochory, or seed dispersal by ants, the benefits to plants are relatively well studied, but less is known about why ants pick up and move seeds. We examined seed dispersal by the ant Aphaenogaster rudis of four co-occurring species of plants, and tested whether morphology, chemical signaling, or the nutritional quality of fatty seed appendages called elaiosomes influenced dispersal rates. In removal trials, ants quickly collected diaspores (seeds plus elaiosomes) of Asarum canadense, Trillium grandiflorum, and Sanguinaria canadensis, but largely neglected those of T. erectum. This discrepancy was not explained by differences in the bulk cost-benefit ratio, as assessed by the ratio of seed to elaiosome mass. We also provisioned colonies with diaspores from one of these four plant species or no diaspores as a control. Colonies performed best when fed S. canadensis diaspores, worst when fed T. grandiflorum, and intermediately when fed A. canadense, T. erectum, or no diaspores. Thus, the nutritional rewards in elaiosomes affected colony performance, but did not completely predict seed removal. Instead, high levels of oleic acid in T. grandiflorum elaiosomes may explain why ants disperse these diaspores even though they reduce ant colony performance. We show for the first time that different elaiosome-bearing plants provide rewards of different quality to ant colonies, but also that ants appear unable to accurately assess reward quality when encountering seeds. Instead, we suggest that signals can trump rewards as attractants of ants to seeds.

Mobile robot path planning method based on improvement of ant colony algorithm and particle swarm optimization

Abstract: The invention discloses a mobile robot path planning method based on an improvement of an ant colony algorithm and particle swarm optimization. The method mainly solves the problems that in the prior art, the operating speed of an algorithm is low, and frequency of turning of an optimized path is high. The planning method includes the steps that modeling is carried out on a work environment of a robot; the particle swarm optimization is utilized to quickly carry out path planning, pheromones more than those around an obtained path are scattered on the obtained path, and guiding is provided for an ant colony; an ant colony algorithm optimized by the principle of inertia is adopted, and optimization is conducted on the basis of the particle swarm optimization; the motion path of the robot is output according to an optimization result. According to the planning method, comprehensive consideration is given to stability and robustness of the algorithm, iterations can be effectively reduced, searching efficiency is improved, the path length is shortened, the frequency of turning is reduced, path quality is substantially improved, and the planning method accords with an artificial planning intention and is suitable for autonomous navigation of various mobile robots in a static environment.

Enough is enough: the effects of symbiotic ant abundance on herbivory, growth, and reproduction in an African acacia

Abstract: Understanding how cooperative interactions evolve and persist remains a central challenge in biology. Many mutualisms are thought to be maintained by "partner fidelity feedback," in which each partner bases their investment on the benefits they receive. Yet, we know little about how benefits change as mutualists vary their investment, which is critical to understanding the balance between mutualism and antagonism in any given partnership. Using an obligate ant-plant mutualism, we manipulated the density of symbiotic acacia ants (Crematogaster mimosae) and examined how the costs and benefits to Acacia drepanolobium trees scaled with ant abundance. Benefits of ants to plants saturated with increasing ant abundance for protection from branch browsing by elephants and attack by branch galling midges, while varying linearly for protection from cerambycid beetles. In addition, the risk of catastrophic whole-tree herbivory by elephants was highest for trees with very low ant abundance. However, there was no relationship between ant abundance and herbivory by leaf-feeding invertebrates, nor by vertebrate browsers such as giraffe, steinbuck, and Grant's gazelle. Ant abundance did not significantly influence rates of branch growth on acacias, but there was a significant negative relationship between ant abundance and the number of fruits produced by host plants, suggesting that maintaining high-density ant colonies is costly. Because benefits to plants largely saturated with increasing colony size, while costs to plant reproduction increased, we suggest that ant colonies may achieve abundances that are higher than optimal for host plants. Our results highlight the conflicts of interest inherent in many mutualisms, and demonstrate the value of examining the shape of curves relating costs and benefits within these globally important interactions.

An Improved Model of Ant Colony Optimization Using a Novel Pheromone Update Strategy

Abstract: The paper introduces a novel pheromone update strategy to improve the functionality of ant colony optimization algorithms. This modification tries to extend the search area by an optimistic reinforcement strategy in which not only the most desirable sub-solution is reinforced in each step, but some of the other partial solutions with acceptable levels of optimality are also favored. therefore, it improves the desire for the other potential solutions to be selected by the following artificial ants towards a more exhaustive algorithm by increasing the overall exploration. The modifications can be adopted in all ant-based optimization algorithms; however, this paper focuses on two static problems of travelling salesman problem and classification rule mining. To work on these challenging problems we considered two ACO algorithms of ACS (Ant Colony System) and AntMiner 3.0 and modified their pheromone update strategy. As shown by simulation experiments, the novel pheromone update method can improve the behavior of both algorithms regarding almost all the performance evaluation metrics. key words: Ant colony optimization, ant colony system, ant-miner, classification rule mining, learning automata, reinforcement learning

Introduced fire ants can exclude native ants from critical mutualist-provided resources.

Abstract: Animals frequently experience resource imbalances in nature. For ants, one resource that may be particularly valuable for both introduced and native species is high-carbohydrate honeydew from hemipteran mutualists. We conducted field and laboratory experiments: (1) to test if red imported fire ants (Solenopsis invicta) competed with native ants for access to mutualisms with aphids, and (2) to quantify the effects of aphid honeydew presence or absence on colony growth of native ants. We focused on native dolichoderine ants (Formicidae, Dolichoderinae) because they are abundant ants that have omnivorous diets that frequently include mutualist-provided carbohydrates. At two sites in the southeastern US, native dolichoderine ants were far less frequent, and fire ants more frequent, at carbohydrate baits than would be expected based on their frequency in pitfall traps. A field experiment confirmed that a native ant species, Dorymyrmex bureni, was only found tending aphids when populations of S. invicta were suppressed. In the laboratory, colonies of native dolichoderine ants with access to both honeydew and insect prey had twice as many workers and over twice as much brood compared to colonies fed only ad libitum insect prey. Our results provide the first experimental evidence that introduced ants compete for access to mutualist-provided carbohydrates with native ants and that these carbohydrates represent critical resources for both introduced and native ants. These results challenge traditional paradigms of arthropod and ant nutrition and contribute to growing evidence of the importance of nutrition in mediating ecological interactions.

An efficient solving of the traveling salesman problem: the ant colony system having parameters optimized by the Taguchi method

Abstract: Owing to its complexity, the traveling salesman problem (TSP) is one of the most intensively studied problems in computational mathematics. The TSP is defined as the provision of minimization of total distance, cost, and duration by visiting the n number of points only once in order to arrive at the starting point. Various heuristic algorithms used in many fields have been developed to solve this problem. In this study, a solution was proposed for the TSP using the ant colony system and parameter optimization was taken from the Taguchi method. The implementation was tested by various data sets in the Traveling Salesman Problem Library and a performance analysis was undertaken. In addition to these, a variance analysis was undertaken in order to identify the effect values of the parameters on the system. Implementation software was developed using the MATLAB program, which has a useful interface and simulation support.

An ant's-eye view of an ant-plant protection mutualism

Abstract: Ant protection of extrafloral nectar (EFN)-secreting plants is a common form of mutualism found in most habitats around the world. However, very few studies have considered these mutualisms from the ant, rather than the plant, perspective. In particular, a whole-colony perspective that takes into account the spatial structure and nest arrangement of the ant colonies that visit these plants has been lacking, obscuring when and how colony-level foraging decisions might affect tending rates on individual plants. Here, we experimentally demonstrate that recruitment of Crematogaster opuntiae (Buren) ant workers to the EFN-secreting cactus Ferocactus wislizeni (Englem) is not independent between plants up to 5 m apart. Colony territories of C. opuntiae are large, covering areas of up to 5,000 m2, and workers visit between five and 34 EFN-secreting barrel cacti within the territories. These ants are highly polydomous, with up to 20 nest entrances dispersed throughout the territory and interconnected by trail networks. Our study demonstrates that worker recruitment is not independent within large polydomous ant colonies, highlighting the importance of considering colonies rather than individual workers as the relevant study unit within ant/plant protection mutualisms.

A two-pheromone trail ant colony system--tabu search approach for the heterogeneous vehicle routing problem with time windows and multiple products

Abstract: This paper considers a practical variant of the Vehicle Routing Problem (VRP) known as the Heterogeneous Vehicle Routing Problem with Time Windows and Multiple Products (HVRPTWMP). As the problem is NP-hard, the resolution approach proposed here is a sequential Ant Colony System (ACS)--Tabu Search algorithm. The approach introduces a two pheromone trail strategy to accelerate agents' (ants) learning process. Its convergence to good solutions is given in terms of fleet size and travel time while completing tours and service to all customers. The proposed procedure uses regency and frequency memories form Tabu Search to further improve the quality of solutions. Experiments are carried out using instances from literature and show the effectiveness of this procedure.

Ant Colony Routing algorithm for freeway networks

Abstract: Dynamic traffic routing refers to the process of (re)directing vehicles at junctions in a traffic network according to the evolving traffic conditions. The traffic management center can determine desired routes for drivers in order to optimize the performance of the traffic network by dynamic traffic routing. However, a traffic network may have thousands of links and nodes, resulting in a large-scale and computationally complex non-linear, non-convex optimization problem. To solve this problem, Ant Colony Optimization (ACO) is chosen as the optimization method in this paper because of its powerful optimization heuristic for combinatorial optimization problems. ACO is implemented online to determine the control signal – i.e., the splitting rates at each node. However, using standard ACO for traffic routing is characterized by four main disadvantages: 1. traffic flows for different origins and destinations cannot be distinguished; 2. all ants may converge to one route, causing congestion; 3. constraints cannot be taken into account; and 4. neither can dynamic link costs. These problems are addressed by adopting a novel ACO algorithm with stench pheromone and with colored ants, called Ant Colony Routing (ACR). Using the stench pheromone, the ACR algorithm can distribute the vehicles over the traffic network with less or no traffic congestion, as well as reduce the number of vehicles near some sensitive zones, such as hospitals and schools. With colored ants, the traffic flows for multiple origins and destinations can be represented. The proposed approach is also implemented in a simulation-based case study in the Walcheren area, the Netherlands, illustrating the effectiveness of the approach.

A max–min ant colony system for assembly sequence planning

Abstract: An improved ant colony optimization (ACO)-based assembly sequence planning (ASP) method for complex products that combines the advantages of ant colony system (ACS) and max–min ant system (MMAS) and integrates some optimization measures is proposed. The optimization criteria, assembly information models, and components number in case study that reported in the literatures of ACO-based ASP during the past 10 years are reviewed and compared. To reduce tedious manual input of parameters and identify the best sequence easily, the optimization criteria such as directionality, parallelism, continuity, stability, and auxiliary stroke are automatically quantified and integrated into the multi-objective heuristic and fitness functions. On the precondition of geometric feasibility based on interference matrix, several strategies of ACS and MMAS are combined in a max–min ant colony system (MMACS) to improve the convergence speed and sequence quality. Several optimization measures are integrated into the system, among which the performance appraisal method transfers the computing resource from the worst ant to the better one, and the group method makes up the deficiency of solely depending on heuristic searching for all parallel parts in each group. An assembly planning system “AutoAssem” is developed based on Siemens NX, and the effectiveness of each optimization measure is testified through case study. Compared with the methods of priority rules screening, genetic algorithm, and particle swarm optimization, MMACS is verified to have superiority in efficiency and sequence performance.

Synergy in ant foraging strategies: memory and communication alone and in combination

Abstract: Collective foraging is a canonical problem in the study of social insect behavior, as well as in biologically inspired engineered systems. Pheromone recruitment is a well-studied mechanism by which ants coordinate their foraging. Another mechanism for information use is the memory of individual ants, which allows an ant to return to a site it has previously visited. There is synergy in the use of social and private information: ants with poor private information can follow pheromone trails; while ants with private information can ignore trails and instead rely on memory. We developed an agent-based model of foraging by harvester ants, and optimized the model to maximize foraging rate using genetic algorithms. We found that ants' individual memory provided greater benefit in terms of increased foraging rate than pheromone trails in a variety of food distributions. When the two strategies are used together, they out-perform either strategy alone. We compare the behavior of these models to observations of harvester ants in the field. We discuss why individual memory is more beneficial in this system than pheromone trails. We suggest that individual memory may be an important addition to ant colony optimization and swarm robotics systems, and that genetic algorithms may be useful in finding an adaptive balance between individual foraging based on memory and recruitment based on communication.

Ant colony clustering with fitness perception and pheromone diffusion for community detection in complex networks

Abstract: Community structure detection in complex networks has been intensively investigated in recent years. In this paper, we propose an adaptive approach based on ant colony clustering to discover communities in a complex network. The focus of the method is the clustering process of an ant colony in a virtual grid, where each ant represents a node in the complex network. During the ant colony search, the method uses a new fitness function to percept local environment and employs a pheromone diffusion model as a global information feedback mechanism to realize information exchange among ants. A significant advantage of our method is that the locations in the grid environment and the connections of the complex network structure are simultaneously taken into account in ants moving. Experimental results on computer-generated and real-world networks show the capability of our method to successfully detect community structures.