Showing papers on "Ant colony published in 2009"

A review of ant algorithms

Abstract: Ant algorithms are optimisation algorithms inspired by the foraging behaviour of real ants in the wild. Introduced in the early 1990s, ant algorithms aim at finding approximate solutions to optimisation problems through the use of artificial ants and their indirect communication via synthetic pheromones. The first ant algorithms and their development into the Ant Colony Optimisation (ACO) metaheuristic is described herein. An overview of past and present typical applications as well as more specialised and novel applications is given. The use of ant algorithms alongside more traditional machine learning techniques to produce robust, hybrid, optimisation algorithms is addressed, with a look towards future developments in this area of study.

Multiple objective ant colony optimisation

Abstract: Multiple Objective Optimisation is a fast growing area of research, and consequently several Ant Colony Optimisation approaches have been proposed for a variety of these problems. In this paper, a taxonomy for Multiple Objective Ant Colony Optimisation algorithms is proposed and many existing approaches are reviewed and described using the taxonomy. The taxonomy offers guidelines for the development and use of Multiple Objective Ant Colony Optimisation algorithms.

Radio-tagging reveals the roles of corpulence, experience and social information in ant decision making

Abstract: Ant colonies are factories within fortresses (Oster and Wilson 1978). They run on resources foraged from an outside world fraught with danger. On what basis do individual ants decide to leave the safety of the nest? We investigated the relative roles of social information (returning nestmates), individual experience and physiology (lipid stores/corpulence) in predicting which ants leave the nest and when. We monitored Temnothorax albipennis workers individually using passive radio-frequency identification technology, a novel procedure as applied to ants. This method allowed the matching of individual corpulence measurements to activity patterns of large numbers of individuals over several days. Social information and physiology are both good predictors of when an ant leaves the nest. Positive feedback from social information causes bouts of activity at the colony level. When certain social information is removed from the system by preventing ants returning, physiology best predicts which ants leave the nest and when. Individual experience is strongly related to physiology. A small number of lean individuals are responsible for most external trips. An individual’s nutrient status could be a useful cue in division of labour, especially when public information from other ants is unavailable.

Ant Colony Optimization Algorithm

Abstract: Hybrid algorithm is proposed to solve combinatorial optimization problem by using Ant Colony and Genetic programming algorithms. Evolutionary process of Ant Colony Optimization algorithm adapts genetic operations to enhance ant movement towards solution state. The algorithm converges to the optimal final solution, by accumulating the most effective sub-solutions.

For ant-protected plants, the best defense is a hungry offense

Abstract: Animal foraging has been characterized as an attempt to maximize the intake of carbon and nitrogen at appropriate ratios. Plant species in over 90 families produce carbohydrate-rich extrafloral nectar (EFN), a resource attractive to ants and other omnivorous insects. This attraction can benefit the plant if those arthropods subsequently attack herbivores. This protective response has been attributed to the increased visitation and "ownership" of plants that provide a predictable source of fuel. Here, we propose and test an alternative (but non-mutually exclusive) hypothesis, that access to C-rich carbohydrates increases the ants' desire for N-rich protein and hence the likelihood that they will attack herbivorous insects on the host plant. This "deficit hypothesis" would be rejected if (1) EFN were itself a sufficiently balanced food source in terms of C and N, (2) ant dietary preferences were similar in the presence vs. absence of EFN, (3) protein-hungry ants were not more predaceous, or (4) ants provided access to protein were more aggressive toward potential prey items than were ants provided access to carbohydrates. We test these predictions in a protective mutualism between a guild of desert ants and the barrel cactus Ferocactus wislizeni. C:N ratios of EFN exceeded that of ants or potential prey items by an order of magnitude (i.e., EFN is an N-poor food for ants). Baiting studies demonstrated that plant-tending ant species recruited more workers to N-rich protein baits than to C-rich sugar baits; this difference was more pronounced when the ants had access to F. wislizeni EFN. From these data, we infer that protein is a valuable resource and that its relative value increases when carbohydrates are readily available. Moreover, ant colonies provided access to supplemental carbohydrates responded more aggressively to surrogate herbivores than did control colonies (to which no additional resources were provided) or colonies provided protein. These results support the predictions of the "deficit" hypothesis, wherein plant protection is elicited by plant-mediated dietary imbalances.

Recent advances in army ant biology (Hymenoptera: Formicidae)

Abstract: Arm y ants are dominant social hunters of invertebrates and thereby play an integral role in tropical ecosystems. They are defined by a suite of evolutionarily interrelated physiological, behavioural and morphological traits, the army ant adaptive syndrome: they are obligate group predators, they frequently relocate their nests, and their permanently wingless queens found new colonies accompanied by workers. If this functional definition is applied rather than a taxonomic one, army ants have evolved repeatedly in distantly related groups of ants. In addition, army ants typically have extremely male-biased numerical sex-ratios, and the queens of the studied species are inseminated by many males. The aim of this review is to provide a synthesis of the most recent work on army ant biology, to outline an evolutionary scenario that connects the different aspects of army ant life-history, and to give some directions for future research.

Ant colony and particle swarm optimization for financial classification problems

Abstract: Financial decisions are often based on classification models which are used to assign a set of observations into predefined groups. Such models ought to be as accurate as possible. One important step towards the development of accurate financial classification models involves the selection of the appropriate independent variables (features) which are relevant for the problem at hand. This is known as the feature selection problem in the machine learning/data mining field. In financial decisions, feature selection is often based on the subjective judgment of the experts. Nevertheless, automated feature selection algorithms could be of great help to the decision-makers providing the means to explore efficiently the solution space. This study uses two nature-inspired methods, namely ant colony optimization and particle swarm optimization, for this problem. The modelling context is developed and the performance of the methods is tested in two financial classification tasks, involving credit risk assessment and audit qualifications.

A masterpiece of evolution – Oecophylla weaver ants (Hymenoptera: Formicidae)

Abstract: Oecophylla species are among the most iconic tropical ants, but a broad review of their biology has been lacking. The two living species of Oecophylla are widespread in the Old World tropics and are similar in presenting the most sophisticated nest-building activities of all weaver ants. Workers draw leaves together, often forming long chains, and glue them together with larval silk. Chain formation promises to provide a major subject for the development of models of the self-organization of complex behavior. The colonies are very large and highly polydomous. Queens are pre-dominantly though not exclusively once-mated and colonies are usually single-queened, but most Northern Territory (Australia) colonies are polygynous. The workers are highly polymorphic (seen also in a fossilized colony), show complex polyethism, and present a much-studied rich pheromonal repertoire for the colony's tasks. Colony odor is partly learned, showing a "nasty neighbor" effect in reactions to other colonies of this highly territorial ant, and partly intrinsic to each individual. The odor varies over time and differs between the nests of a colony. Not surprisingly, Oecophylla ants are hosts to a variety of inquilines, such as spiders, which mimic the colony odor to escape detection. In addition, a con-stellation of Homoptera benefit from ant protection, yet the activities of the ants in controlling pest species make these ants beneficial insects (they are also human food in some areas). We speculate that the existence of Oecophylla blocks other weaver ants from evolving highly complex social organization, an idea which could be tested with further knowl-edge on the timing of ant adaptive radiations.

Hydrocarbons in the ant Lasius niger: from the cuticle to the nest and home range marking.

Abstract: The cuticular hydrocarbons (CHCs) of the ant Lasius niger are described. We observe a high local colony specificity of the body cuticular profile as predicted for a monogynous and multicolonial species. The CHCs show a low geographical variation among different locations in France. The CHCs on the legs also are colony specific, but their relative quantities are slightly different from those on the main body. For the first time, we demonstrate that the inner walls of the ant nest are coated with the same hydrocarbons as those found on the cuticle but in different proportions. The high amount of inner-nest marking and its lack of colony-specificity may explain why alien ants are not rejected once they succeed in entering the nest. The cuticular hydrocarbons also are deposited in front of the nest entrance and on the foraging arena, with a progressive increase in n-alkanes relative amounts. Chemical marks laid over the substrate are colony specific only when we consider methyl-branched alkanes. Our data confirm that these "footprint hydrocarbons" are probably deposited passively by the contact of ant tarsae with the substrate. These results suggest that the CHCs chemical profiles used by ants in colony recognition are much more complex than a single template: ants have to learn and memorize odors that vary depending on their context of perception.

Structural topology optimization using ant colony optimization algorithm

Abstract: The ant colony optimization (ACO) algorithm, a relatively recent bio-inspired approach to solve combinatorial optimization problems mimicking the behavior of real ant colonies, is applied to problems of continuum structural topology design. An overview of the ACO algorithm is first described. A discretized topology design representation and the method for mapping ant's trail into this representation are then detailed. Subsequently, a modified ACO algorithm with elitist ants, niche strategy and memory of multiple colonies is illustrated. Several well-studied examples from structural topology optimization problems of minimum weight and minimum compliance are used to demonstrate its efficiency and versatility. The results indicate the effectiveness of the proposed algorithm and its ability to find families of multi-modal optimal design.

Performance of an ant colony optimisation algorithm in dynamic job shop scheduling problems

Abstract: The goal of the current study is to identify appropriate application domains of Ant Colony Optimisation (ACO) in the area of dynamic job shop scheduling problem. The algorithm is tested in a shop floor scenario with three levels of machine utilisations, three different processing time distributions, and three different performance measures for intermediate scheduling problems. The steady-state performances of ACO in terms of mean flow time, mean tardiness, total throughput on different experimental environments are compared with those from dispatching rules including first-in-first-out, shortest processing time, and minimum slack time. Two series of experiments are carried out to identify the best ACO strategy and the best performing dispatching rule. Those two approaches are thereafter compared with different variations of processing times. The experimental results show that ACO outperforms other approaches when the machine utilisation or the variation of processing times is not high.

Variable Strength Interaction Testing with an Ant Colony System Approach

Abstract: Interaction testing (also called combinatorial testing) is an cost-effective test generation technique in software testing. Most research work focuses on finding effective approaches to build optimal t-way interaction test suites. However, the strength of different factor sets may not be consistent due to the practical test requirements. To solve this problem, a variable strength combinatorial object and several approaches based on it have been proposed. These approaches include simulated annealing (SA) and greedy algorithms. SA starts with a large randomly generated test suite and then uses a binary search process to find the optimal solution. Although this approach often generates the minimal test suites, it is time consuming. Greedy algorithms avoid this shortcoming but the size of generated test suites is usually not as small as SA. In this paper, we propose a novel approach to generate variable strength interaction test suites (VSITs). In our approach, we adopt a one-test-at-a-time strategy to build final test suites. To generate a single test, we adopt ant colony system (ACS) strategy, an effective variant of ant colony optimization (ACO). In order to successfully adopt ACS, we formulize the solution space, the cost function and several heuristic settings in this framework. We also apply our approach to some typical inputs. Experimental results show the effectiveness of our approach especially compared to greedy algorithms and several existing tools.

Ant Colony Optimization for Air Traffic Conflict Resolution

Abstract: The n aircraft conflict resolution problem is highly combinatorial and can be optimally solved using classical mathe- matical optimisation techniques only for small problems involving less than 5 aircraft. This article applies an Ant Colony Optimiza- tion (ACO) algorithm in order to solve large problems involving up to 30 aircraft. In order to limit the number of pheromone trails to update, a n aircraft conflict resolution problem is not modeled by a single ant but by a bunch of n ants choosing their trajectories independantly. A relaxation process is also used in order to be able to handle difficult conflicts for which partial solutions can help finding a path toward the optimal solution. Two different sizes of a toy problem are solved and presented.

Optimization of Large Transport Networks Using the Ant Colony Heuristic

Abstract: Long-term transportation planning in larger regions encompasses more than the evaluation of individual infrastructure projects; it must also assess synergies and interference among sets of projects. The objective is the maximizing of the overall benefit within specific budget restrictions by finding the most favourable bundle of projects, i.e. solving the network design problem. For large numbers of projects, complete enumeration of all combinations, requiring time consuming equilibrium calculations is not feasible for detailed networks. The ant colony heuristic is suitable for this kind of problem. According to our knowledge, this paper presents the above-PHQWLRQH ^ ` ,  ^` ¦ � ! ! ! 

Do ants make direct comparisons

Abstract: Many individual decisions are informed by direct comparison of the alternatives. In collective decisions, however, only certain group members may have the opportunity to compare options. Emigrating ant colonies (Temnothorax albipennis) show sophisticated nest-site choice, selecting superior sites even when they are nine times further away than the alternative. How do they do this? We used radio-frequency identification-tagged ants to monitor individual behaviour. Here we show for the first time that switching between nests during the decision process can influence nest choice without requiring direct comparison of nests. Ants finding the poor nest were likely to switch and find the good nest, whereas ants finding the good nest were more likely to stay committed to that nest. When ants switched quickly between the two nests, colonies chose the good nest. Switching by ants that had the opportunity to compare nests had little effect on nest choice. We suggest a new mechanism of collective nest choice: individuals respond to nest quality by the decision either to commit or to seek alternatives. Previously proposed mechanisms, recruitment latency and nest comparison, can be explained as side effects of this simple rule. Colony-level comparison and choice can emerge, without direct comparison by individuals.

An Improved Excitation Matching Method Based on an Ant Colony Optimization for Suboptimal-Free Clustering in Sum-Difference Compromise Synthesis

Abstract: Dealing with an excitation matching method, this paper presents a global optimization strategy for the optimal clustering in sum-difference compromise linear arrays. Starting from a combinatorial formulation of the problem at hand, the proposed technique is aimed at determining the subarray configuration expressed as the optimal path inside a directed acyclic graph structure modelling the solution space. Towards this end, an ant colony metaheuristic is used to benefit of its hill-climbing properties in dealing with the non-convexity of the sub-arraying as well as in managing graph searches. A selected set of numerical experiments are reported to assess the efficiency and current limitations of the ant-based strategy also in comparison with previous local combinatorial search methods.

Cloud Computing Initiative using Modified Ant Colony Framework

Abstract: Scheduling of diversified service requests in distributed computing is a critical design issue. Cloud is a type of parallel and distributed system consisting of a collection of interconnected and virtual computers. It is not only the clusters and grid but also it comprises of next generation data centers. The paper proposes an initial heuristic algorithm to apply modified ant colony optimization approach for the diversified service allocation and scheduling mechanism in cloud paradigm. The proposed optimization method is aimed to minimize the scheduling throughput to service all the diversified requests according to the different resource allocator available under cloud computing environment. Keywords—Ant Colony, Cloud Computing, Grid, Resource allocator, Service Request.

Detrimental effects of two widespread invasive ant species on weight and survival of colonial nesting seabirds in the Hawaiian Islands

Abstract: Invasive ants are a significant conservation concern and can have far-reaching effects in ecosystems they invade. We used the experimental control of ant numbers on two pairs of small ( 20% of tissue on their feet) weighed significantly less than uninjured chicks and did not fledge. It is unclear if the chicks were being preyed upon or stung in defense of nearby ant colonies. Radical changes in invasive ant populations have been noted, and booming ant populations could cause short-term, but widespread damage to seabird colonies. The negative effects of invasive ants on seabirds may be difficult to detect, and therefore unknown or underestimated throughout the world where the two groups overlap.

Handling continuous attributes in Ant Colony Classification algorithms

Abstract: Most real-world classification problems involve continuous (real-valued) attributes, as well as, nominal (discrete) attributes. The majority of Ant Colony Optimisation (ACO) classification algorithms have the limitation of only being able to cope with nominal attributes directly. Extending the approach for coping with continuous attributes presented by cAnt-Miner (Ant-Miner coping with continuous attributes), in this paper we propose two new methods for handling continuous attributes in ACO classification algorithms. The first method allows a more flexible representation of continuous attributes' intervals. The second method explores the problem of attribute interaction, which originates from the way that continuous attributes are handled in cAnt-Miner, in order to implement an improved pheromone updating method. Empirical evaluation on eight publicly available data sets shows that the proposed methods facilitate the discovery of more accurate classification models.

Rationality in collective decision-making by ant colonies

Abstract: Economic models of animal behaviour assume that decision-makers are rational, meaning that they assess options according to intrinsic fitness value and not by comparison with available alternatives. This expectation is frequently violated, but the significance of irrational behaviour remains controversial. One possibility is that irrationality arises from cognitive constraints that necessitate short cuts like comparative evaluation. If so, the study of whether and when irrationality occurs can illuminate cognitive mechanisms. We applied this logic in a novel setting: the collective decisions of insect societies. We tested for irrationality in colonies of Temnothorax ants choosing between two nest sites that varied in multiple attributes, such that neither site was clearly superior. In similar situations, individual animals show irrational changes in preference when a third relatively unattractive option is introduced. In contrast, we found no such effect in colonies. We suggest that immunity to irrationality in this case may result from the ants’ decentralized decision mechanism. A colony's choice does not depend on site comparison by individuals, but instead self-organizes from the interactions of multiple ants, most of which are aware of only a single site. This strategy may filter out comparative effects, preventing systematic errors that would otherwise arise from the cognitive limitations of individuals.

Finding groups in data: Cluster analysis with ants

Abstract: We present in this paper a modification of Lumer and Faieta's algorithm for data clustering. This approach mimics the clustering behavior observed in real ant colonies. This algorithm discovers automatically clusters in numerical data without prior knowledge of possible number of clusters. In this paper we focus on ant-based clustering algorithms, a particular kind of a swarm intelligent system, and on the effects on the final clustering by using during the classification different metrics of dissimilarity: Euclidean, Cosine, and Gower measures. Clustering with swarm-based algorithms is emerging as an alternative to more conventional clustering methods, such as e.g. k-means, etc. Among the many bio-inspired techniques, ant clustering algorithms have received special attention, especially because they still require much investigation to improve performance, stability and other key features that would make such algorithms mature tools for data mining. As a case study, this paper focus on the behavior of clustering procedures in those new approaches. The proposed algorithm and its modifications are evaluated in a number of well-known benchmark datasets. Empirical results clearly show that ant-based clustering algorithms performs well when compared to another techniques.

Using Ant Colony Optimization algorithm for solving project management problems

Abstract: Network analysis provides an effective practical system for planning and controlling large projects in construction and many other fields. Ant Colony System is a recent approach used for solving path minimization problems. This paper presents the use of Ant Colony Optimization (ACO) system for solving and calculating both deterministic and probabilistic CPM/PERT networks. The proposed method is investigated for a selected case study in construction management. The results demonstrate that - compared to conventional methods - ACO can produce good optimal and suboptimal solutions.

The intertwined population biology of two Amazonian myrmecophytes and their symbiotic ants

Abstract: A major question in ecology is: how do mutualisms between species affect population dynamics? For four years, we monitored populations of two Amazonian myrmecophytes, Cordia nodosa and Duroia hirsuta, and their symbiotic ants. In this system, we investigated how positive feedback between mutualistic plants and ant colonies influenced population processes at two scales: (1) how modular organisms such as plants and ant colonies grew, or η-demography, and (2) how populations grew, or N-demography. We found evidence of positive feedback between ant colony and plant growth rates. Plants with mutualistic ants (Azteca spp. and Myrmelachista schumanni) grew in a geometric or autocatalytic manner, such that the largest plants grew the most. By contrast, the growth of plants with parasitic ants (Allomerus octoarticulatus) saturated. Ant colonies occupied new domatia as fast as plants produced them, suggesting that mutualistic ant colonies also grew geometrically or autocatalytically to match plant growth. Plants became smaller when they lost ants. While unoccupied, plants continued to become smaller until they had lost all or nearly all their domatia. Hence, the loss of mutualistic ants limited plant growth. C. nodosa and D. hirsuta live longer than their ant symbionts and were sometimes recolonized after losing ants, which again promoted plant growth. Plant growth had fitness consequences for ants and plants; mortality and fecundity depended on plant size. Positive feedback between ants and plants allowed a few plants and ant colonies to become very large; these probably produced the majority of offspring in the next generation.