Showing papers on "Ant colony published in 2011"

Energy-Aware Ant Colony Based Workload Placement in Clouds

Abstract: With increasing numbers of energy hungry data centers energy conservation has now become a major design constraint. One traditional approach to conserve energy in virtualized data centers is to perform workload (i.e., VM) consolidation. Thereby, workload is packed on the least number of physical machines and over-provisioned resources are transitioned into a lower power state. However, most of the workload consolidation approaches applied until now are limited to a single resource (e.g., CPU) and rely on simple greedy algorithms such as First-Fit Decreasing (FFD), which perform resource-dissipative workload placement. Moreover, they are highly centralized and known to be hard to distribute. In this work, we model the workload consolidation problem as an instance of the multi-dimensional bin-packing (MDBP) problem and design a novel, nature-inspired workload consolidation algorithm based on the Ant Colony Optimization (ACO). We evaluate the ACO-based approach by comparing it with one frequently applied greedy algorithm (i.e., FFD). Our simulation results demonstrate that ACO outperforms the evaluated greedy algorithm as it achieves superior energy gains through better server utilization and requires less machines. Moreover, it computes solutions which are nearly optimal. Finally, the autonomous nature of the approach allows it to be implemented in a fully distributed environment.

A survey on parallel ant colony optimization

Abstract: Ant colony optimization (ACO) is a well-known swarm intelligence method, inspired in the social behavior of ant colonies for solving optimization problems. When facing large and complex problem instances, parallel computing techniques are usually applied to improve the efficiency, allowing ACO algorithms to achieve high quality results in reasonable execution times, even when tackling hard-to-solve optimization problems. This work introduces a new taxonomy for classifying software-based parallel ACO algorithms and also presents a systematic and comprehensive survey of the current state-of-the-art on parallel ACO implementations. Each parallel model reviewed is categorized in the new taxonomy proposed, and an insight on trends and perspectives in the field of parallel ACO implementations is provided.

VNE-AC: Virtual Network Embedding Algorithm Based on Ant Colony Metaheuristic

Abstract: In this paper, we address a virtual network embedding problem. Indeed, our objective is to map virtual networks in the substrate network with minimum physical resources while satisfying its required QoS in terms of bandwidth, power processing and memory. In doing so, we minimize the reject rate of requests and maximize returns for the substrate network provider. Since the problem is NP-hard and to deal with its computational hardness, we propound a new scalable embedding strategy named \texttt{VNE-AC} based on the Ant Colony metaheuristic. The intensive simulations and evaluation results show that our proposal enhances the substrate provider's revenue and outperforms the related strategies found in current literature.

Parameter Adaptation in Ant Colony Optimization

Abstract: This chapter reviews the approaches that have been studied for the online adaptation of the parameters of ant colony optimization (ACO) algorithms, that is, the variation of parameter settings while solving an instance of a problem. We classify these approaches according to the main classes of online parameter-adaptation techniques. One conclusion of this review is that the available approaches do not exploit an in-depth understanding of the effect of individual parameters on the behavior of ACO algorithms. Therefore, this chapter also presents results of an empirical study of the solution quality over computation time for Ant Colony System and MAX-MIN Ant System, two well-known ACO algorithms. The first part of this study provides insights on the behaviour of the algorithms in dependence of fixed parameter settings. One conclusion is that the best fixed parameter settings of MAX-MIN Ant System depend strongly on the available computation time. The second part of the study uses these insights to propose simple, pre-scheduled parameter variations. Our experimental results show that such pre-scheduled parameter variations can dramatically improve the anytime performance of MAX-MIN Ant System.

A parallel improved ant colony optimization for multi-depot vehicle routing problem

Abstract: This paper presents a method for solving multi-depot vehicle routing problem (MDVRP). First, a virtual central depot is added to transfer MDVRP to the multi-depot vehicle routing problem with the virtual central depot (V-MDVRP), which is similar to a vehicle routing problem (VRP) with the virtual central depot as the origin. An improved ant colony optimization with coarse-grain parallel strategy, ant-weight strategy and mutation operation, is presented for the V-MDVRP. The computational results for 23 benchmark problems are reported and compared to those of other ant colony optimizations.

Ant–plant interaction in the Neotropical savanna: direct beneficial effects of extrafloral nectar on ant colony fitness

Abstract: Current evidence suggests that ant–plant relationships may influence species composition, abundance, and interactions at the community scale. The main resource that plants offer to ants is extrafloral nectar (EFN) and the major part of published studies shown benefits from ants to plants possessing EFNs. However, the complementary question of whether and how ants benefit from EFNs is rarely addressed. Here, we present the results of a long-term study to demonstrate whether EFN has a positive effect on ant colony fitness. We quantified colony growth rate, survival and the final weight of individuals as measures of benefit derived from EFN. Our results provide clear evidence that EFN can have a significant positive impact on the survivorship, growth and reproduction of the Myrmicinae Cephalotes pusillus. In fact, a diet rich in EFN (providing at least 30 cal per day) resulted in five times more individuals per colony, greater body weights, and more eggs. These results have shed new light on the relationships between ants and EFN-bearing plants such as in tropical and temperate systems. The ant C. pusillus is the first case in which we have firm evidence that EFN improves colony growth and development, corroborating more than 100 years of experimental evidence of benefits to plants in these widespread relationships.

A fuzzy classification system based on Ant Colony Optimization for diabetes disease diagnosis

Abstract: Classification systems have been widely utilized in medical domain to explore patient's data and extract a predictive model. This model helps physicians to improve their prognosis, diagnosis or treatment planning procedures. The aim of this paper is to use an Ant Colony-based classification system to extract a set of fuzzy rules for diagnosis of diabetes disease, named FCS-ANTMINER. We will review some recent methods and describe a new and efficient approach that leads us to considerable results for diabetes disease classification problem. FCS-ANTMINER has new characteristics that make it different from the existing methods that have utilized the Ant Colony Optimization (ACO) for classification tasks. The obtained classification accuracy is 84.24% which reveals that FCS-ANTMINER outperforms several famous and recent methods in classification accuracy for diabetes disease diagnosis.

Time-ordered networks reveal limitations to information flow in ant colonies.

Abstract: Background: An important function of many complex networks is to inhibit or promote the transmission of disease, resources, or information between individuals. However, little is known about how the temporal dynamics of individuallevel interactions affect these networks and constrain their function. Ant colonies are a model comparative system for understanding general principles linking individual-level interactions to network-level functions because interactions among individuals enable integration of multiple sources of information to collectively make decisions, and allocate tasks and resources. Methodology/Findings: Here we show how the temporal and spatial dynamics of such individual interactions provide upper bounds to rates of colony-level information flow in the ant Temnothorax rugatulus. We develop a general framework for analyzing dynamic networks and a mathematical model that predicts how information flow scales with individual mobility and group size. Conclusions/Significance: Using thousands of time-stamped interactions between uniquely marked ants in four colonies of a range of sizes, we demonstrate that observed maximum rates of information flow are always slower than predicted, and are constrained by regulation of individual mobility and contact rate. By accounting for the ordering and timing of interactions, we can resolve important difficulties with network sampling frequency and duration, enabling a broader understanding of interaction network functioning across systems and scales.

Endmember Extraction of Hyperspectral Remote Sensing Images Based on the Ant Colony Optimization (ACO) Algorithm

Abstract: Spectral mixture analysis has been an important research topic in remote sensing applications, particularly for hyperspectral remote sensing data processing. On the basis of linear spectral mixture models, this paper applied directed and weighted graphs to describe the relationship between pixels. In particular, we transformed the endmember extraction problem in the decomposition of mixed pixels into an issue of optimization and built feasible solution space to evaluate the practical significance of the objective function, thereby establishing two ant colony optimization algorithms for endmember extraction. In addition to the detailed process of calculation, we also addressed the effects of different operating parameters on algorithm performance. Finally we designed two sets of simulation data experiments and one set of actual data experiments, and the results of those experiments prove that endmember extraction based on ant colony algorithms can avoid some defects of N-FINDR, VCA and other algorithms, improve the representation of endmembers for all image pixels, decrease the average value of root-mean-square error, and therefore achieve better endmember extraction results than the N-FINDR and VCA algorithms.

Ecosystem engineering and predation: the multi-trophic impact of two ant species.

Abstract: Summary 1. Ants are ubiquitous ecosystem engineers and generalist predators and are able to affect ecological communities via both pathways. They are likely to influence any other terrestrial arthropod group either directly or indirectly caused by their high abundance and territoriality. 2. We studied the impact of two ant species common in Central Europe, Myrmica rubra and Lasius niger, on an arthropod community. Colony presence and density of these two ant species were manipulated in a field experiment from the start of ant activity in spring to late summer. 3. The experiment revealed a positive influence of the presence of one ant colony on densities of decomposers, herbivores and parasitoids. However, in the case of herbivores and parasitoids, this effect was reversed in the presence of two colonies. 4. Generally, effects of the two ant species were similar with the exception of their effect on Braconidae parasitoid densities that responded positively to one colony of M. rubra but not of L. niger. 5. Spider density was not affected by ant colony manipulation, but species richness of spiders responded positively to ant presence. This effect was independent of ant colony density, but where two colonies were present, spider richness was significantly greater in plots with two M. rubra colonies than in plots with one colony of each ant species. 6. To test whether the positive ecosystem engineering effects were purely caused by modified properties of the soil, we added in an additional experiment (i) the soil from ant nests (without ants) or (ii) unmodified soil or (iii) ant nests (including ants) to experimental plots. Ant nest soil on its own did not have a significant impact on densities of decomposers, herbivores or predators, which were significantly, and positively, affected by the addition of an intact nest. 7. The results suggest an important role of both ant species in the grassland food web, strongly affecting the densities of decomposers, herbivores and higher trophic levels. We discuss how the relative impact via bottom-up and top-down effects of ants depends on nest density, with a relatively greater top-down predatory impact at higher densities.

Optimal Image Watermarking Algorithm Based on LWT-SVD via Multi-objective Ant Colony Optimization

Abstract: In this paper, a new optimal watermarking scheme based on lifting wavelet transform (LWT) and singular value decomposition (SVD) using multi-objective ant colony optimization (MOACO) is presented. The singular values of the binary water- mark are embedded in a detail subband of host image. To achieve the highest possible robustness without losing watermark transparency, multiple scaling factors (MSF) are used instead of a single scaling factor (SSF). Determining the optimal values of the mul- tiple scaling factors (MSF) is a dicult problem. However, to determine these values, a multi-objective ant colony-based optimization method is used. Experimental results show much improved performances in terms of transparency and robustness for the proposed method compared to other watermarking schemes. Furthermore, the proposed scheme does not suer from the problem of high probability of false positive detections of the watermarks.

Parallelized genetic ant colony systems for solving the traveling salesman problem

Abstract: In this paper, we present a new method, called the parallelized genetic ant colony system (PGACS), for solving the traveling salesman problem. It consists of the genetic algorithm, including the new crossover operations and the hybrid mutation operations, and the ant colony systems with communication strategies. We also make an experiment with three classical data sets got from the TSP library to test the performance of the proposed method. The experiment results show that the performance of the proposed method is better than Chu et al.'s method (2004).

A new fuzzy optimal reconfiguration of distribution systems for loss reduction and load balancing using ant colony search-based algorithm

Abstract: All utility companies strive to achieve the well-balanced distribution systems in order to improve system voltage regulation by means of equal load balancing of feeders and reducing power loss. Optimal reconfiguration is one of the best solutions to reach this goal. This paper presents a new combined method for optimal reconfiguration using a multi-objective function with fuzzy variables. This method considers both objectives of load balancing and loss reduction in the feeders. Since reconfiguration is a nonlinear optimization problem, the ant colony algorithm is employed for the optimized response in search space. This method has been applied on two IEEE 33-bus and 69-bus distribution systems. Simulation results confirm the effectiveness of the proposed method in comparison with other techniques for optimal reconfiguration.

Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks

Abstract: In this paper, a family of ant colony algorithms called DAACA for data aggregation has been presented which contains three phases: the initialization, packet transmission and operations on pheromones. After initialization, each node estimates the remaining energy and the amount of pheromones to compute the probabilities used for dynamically selecting the next hop. After certain rounds of transmissions, the pheromones adjustment is performed periodically, which combines the advantages of both global and local pheromones adjustment for evaporating or depositing pheromones. Four different pheromones adjustment strategies are designed to achieve the global optimal network lifetime, namely Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data aggregation algorithms, DAACA shows higher superiority on average degree of nodes, energy efficiency, prolonging the network lifetime, computation complexity and success ratio of one hop transmission. At last we analyze the characteristic of DAACA in the aspects of robustness, fault tolerance and scalability.

Unequal area flexible bay facility layout using ant colony optimisation

Abstract: In this paper, an ant colony optimisation approach is proposed to solve the facility layout problem with unequal area departments and flexible bays, which is one of the commonly used layout representations in industry practice. Optimal approaches to the facility layout design can only solve problems with a limited number of departments. The proposed ant colony optimisation approach is tested on 21 fairly well-known unequal area facility layout problems from the literature with up to 62 departments, and the results are compared with the previously best known solutions. Different cases of these problems are also tested. The proposed ant colony optimisation approach is shown to be very effective in finding previously known best solutions in a very short amount of CPU times and making improvements up to 17.38%.

Study on hybrid PS-ACO algorithm

Abstract: Ant colony optimization (ACO) algorithm is a recent meta-heuristic method inspired by the behavior of real ant colonies. The algorithm uses parallel computation mechanism and performs strong robustness, but it faces the limitations of stagnation and premature convergence. In this paper, a hybrid PS-ACO algorithm, ACO algorithm modified by particle swarm optimization (PSO) algorithm, is presented. The pheromone updating rules of ACO are combined with the local and global search mechanisms of PSO. On one hand, the search space is expanded by the local exploration; on the other hand, the search process is directed by the global experience. The local and global search mechanisms are combined stochastically to balance the exploration and the exploitation, so that the search efficiency can be improved. The convergence analysis and parameters selection are given through simulations on traveling salesman problems (TSP). The results show that the hybrid PS-ACO algorithm has better convergence performance than genetic algorithm (GA), ACO and MMAS under the condition of limited evolution iterations.

Genetic evolving ant direction HDE for OPF with non-smooth cost functions and statistical analysis

Abstract: This paper proposes an evolving ant direction hybrid differential evolution (EADHDE) algorithm for solving the optimal power flow problem with non-smooth and non-convex generator fuel cost characteristics. The EADHDE employs ant colony search to find a suitable mutation operator for hybrid differential evolution (HDE) where as the ant colony parameters are evolved using genetic algorithm approach. The Newton-Raphson method solves the power flow problem. The feasibility of the proposed approach was tested on IEEE 30-bus system with three different cost characteristics. Several cases were investigated to test and validate the robustness of the proposed method in finding optimal solution. Simulation results demonstrate that the EADHDE provides very remarkable results compared to classical HDE and other methods reported in the literature recently. An innovative statistical analysis based on central tendency measures and dispersion measures was carried out on the bus voltage profiles and voltage stability indices.

Predation by ants on arthropods and other animals

Abstract: Ants are the most widely distributed and most numerically abundant group of social insects. First, they were ground- or litter-dwelling predators or scavengers, and certain taxa evolved to adopt an arboreal way of life. Most ant species are generalist feeders, and only some ground-nesting and ground- foraging species are strictly predators. Ants are central-place foragers (with the exception of army ants during the nomadic phase) that may use different foraging strategies. Solitary hunting is the most common method employed by predatory ants. Cooperative hunting, considered more evolved than solitary hunting, is used by army ants and other ants such as Myrmicaria opaciventris, Paratrechina longicornis or the dominant arboreal Oecophylla. Army ants are predators with different levels of specialization, some of which focus on a particular genus or species, as is the case for Nomamyrmex esenbeckii which organizes subterranean raids on the very large colonies of the leaf-cutting species Atta colombica or A. cephalotes. Arboreal ants have evolved predatory behaviors adapted to the tree foliage, where prey are unpredictable and able to escape by flying away, jumping or

A hybrid ant colony optimization for continuous domains

Abstract: Research on optimization in continuous domains gains much of focus in swarm computation recently A hybrid ant colony optimization approach which combines with the continuous population-based incremental learning and the differential evolution for continuous domains is proposed in this paper It utilizes the ant population distribution and combines the continuous population-based incremental learning to dynamically generate the Gaussian probability density functions during evolution To alleviate the less diversity problem in traditional population-based ant colony algorithms, differential evolution is employed to calculate Gaussian mean values for the next generation in the proposed method Experimental results on a large set of test functions show that the new approach is promising and performs better than most of the state-of-the-art ACO algorithms do in continuous domains

Indirect benefits of symbiotic coccoids for an ant‐defended myrmecophytic tree

Abstract: The net benefits of mutualism depend directly on the costs and effectiveness of mutualistic services and indirectly on the interactions that affect those services We examined interactions among Cordia alliodora myrmecophytic trees, their symbiotic ants Azteca pittieri, coccoid hemipterans, and foliar herbivores in two Neotropical dry forests The tree makes two investments in symbiotic ants: it supplies nesting space, as domatia, and it provides phloem to coccoids, which then produce honeydew that is consumed by ants Although higher densities of coccoids should have higher direct costs for trees, we asked whether higher densities of coccoids can also have higher indirect benefits for trees by increasing the effectiveness of ant defense against foliar herbivores We found that trees benefited from ant defense against herbivores Ants defended trees effectively only when colonies reached high densities within trees, and ant and coccoid densities within trees were strongly positively correlated The benefits of reduced foliar herbivory by larger ant colonies were therefore indirectly controlled by the number of coccoids Coccoid honeydew supply also affected per capita ant aggression against tree herbivores Ants experimentally fed a carbohydrate-rich diet, analogous to sugar obtained from coccoids, were more aggressive against caterpillars per capita than ants fed a carbohydrate-poor diet Ant defense was more effective on more valuable and vulnerable young leaves than on older leaves Young domatia, associated with young leaves, contained higher coccoid densities than older domatia, which suggests that coccoids may also drive spatially favorable ant defense of the tree If higher investments by one mutualistic partner are tied to higher benefits received from the other, there may be positive feedback between partners that will stabilize the mutualism These results suggest that higher investment by trees in coccoids leads to more effective defense by ants against the tree's foliar herbivores

Multiple pheromone types and other extensions to the Ant-Miner classification rule discovery algorithm

Abstract: Ant-Miner is an ant-based algorithm for the discovery of classification rules. This paper proposes five extensions to Ant-Miner: (1) we utilize multiple types of pheromone, one for each permitted rule class, i.e. an ant first selects the rule class and then deposits the corresponding type of pheromone; (2) we use a quality contrast intensifier to magnify the reward of high-quality rules and to penalize low-quality rules in terms of pheromone update; (3) we allow the use of a logical negation operator in the antecedents of constructed rules; (4) we incorporate stubborn ants, an ACO variation in which an ant is allowed to take into consideration its own personal past history; (5) we use an ant colony behavior in which each ant is allowed to have its own values of the α and β parameters (in a sense, to have its own personality). Empirical results on 23 datasets show improvements in the algorithm’s performance in terms of predictive accuracy and simplicity of the generated rule set.

Reliability optimization of a series system with multiple-choice and budget constraints using an efficient ant colony approach

Abstract: This paper deals with a reliability optimization problem for a series system with multiple-choice and budget constraints. The objective is to choose one technology for each subsystem in order to maximize the reliability of the whole system subject to the available budget. This problem is NP-hard and could be formulated as a binary integer programming problem with a nonlinear objective function. In this paper, an efficient ant colony optimization (ACO) approach is developed for the problem. In the approach, a solution is generated by an ant based on both pheromone trails modified by previous ants and heuristic information considered as a fuzzy set. Constructed solutions are not guaranteed to be feasible; consequently, applying an appropriate procedure, an infeasible solution is replaced by a feasible one. Then, feasible solutions are improved by a local search. The proposed approach is compared with the existing metaheuristic available in the literature. Computational results demonstrate that the approach serves to be a better performance for large problems.

An ant-inspired algorithm for detection of image edge features

Abstract: This paper presents a technique inspired by swarm methodologies such as ant colony algorithms for processing simple and complicated images. It is shown that the proposed technique for image processing is capable of performing feature extraction for edge detection and segmentation, even in the presence of noise. Our proposed approach, Ant-based Correlation for Edge Detection (ACED), is tested on different samples and the results are compared to typical established non-swarm-based methods. The comparative analysis highlights the advantages of the proposed method which generates less distortion when noise is added to the test images. Both qualitative and quantitative evaluations support the claim, confirming the significance of our swarm-based method for image feature extraction and segmentation.

Differential Response of Ant Colonies to Intruders: Attack Strategies Correlate With Potential Threat

Abstract: Animals are often threatened by predators, parasites, or competitors, and attacks against these enemies are a common response, which can help to remove the danger. The costs of defense are complex and involve the risk of injury, the loss of energy ⁄time, and the erroneous identification of a friend as a foe. Our goal was to study the specificity of defense strategies. We analyzed the aggressive responses of ant colonies by confronting them with workers of an unfamiliar congeneric species, a non-nestmate conspecific, a co-occurring congeneric competitor species, and a social parasite—a slave-making ant. As expected, the latter species, which can inflict dramatic fitness losses to the colony, was treated with most aggression. A co-occurring competitor was also attacked, but the ants used different behaviors in their responses to both enemies. While the slavemaker was attacked by biting and stinging and was approached with spread mandibles, the competitor was dragged, a behavioral strategy only possible if the defending ant is similar in size and strength to the opponent. Non-nestmate conspecifics were treated aggressively as well, but less than the slavemaker and the co-occurring competitor, presumably because they are less easily recognized as enemies. An unfamiliar congeneric species was rarely attacked. This first detailed study comparing the aggressive responses of ant colonies toward slave-making ants to other species posing different threats indicates that the responses of ant colonies are adjusted to the risk each opponent poses to the colony.