Showing papers on "Ant colony published in 2018"

An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing

Abstract: Virtual machine placement (VMP) and energy efficiency are significant topics in cloud computing research. In this paper, evolutionary computing is applied to VMP to minimize the number of active physical servers, so as to schedule underutilized servers to save energy. Inspired by the promising performance of the ant colony system (ACS) algorithm for combinatorial problems, an ACS-based approach is developed to achieve the VMP goal. Coupled with order exchange and migration (OEM) local search techniques, the resultant algorithm is termed an OEMACS. It effectively minimizes the number of active servers used for the assignment of virtual machines (VMs) from a global optimization perspective through a novel strategy for pheromone deposition which guides the artificial ants toward promising solutions that group candidate VMs together. The OEMACS is applied to a variety of VMP problems with differing VM sizes in cloud environments of homogenous and heterogeneous servers. The results show that the OEMACS generally outperforms conventional heuristic and other evolutionary-based approaches, especially on VMP with bottleneck resource characteristics, and offers significant savings of energy and more efficient use of different resources.

AntMapper: An Ant Colony-Based Map Matching Approach for Trajectory-Based Applications

Abstract: Many trajectory-based applications require an essential step of mapping raw GPS trajectories onto the digital road network accurately. This task, commonly referred to as map matching, is challenging due to the measurement error of GPS devices in critical environment and the sampling error caused by long sampling intervals. Traditional algorithms focus on either a local or a global perspective to deal with the problem. To further improve the performance, this paper develops a novel map matching model that considers local geometric/topological information and a global similarity measure simultaneously. To accomplish the optimization goal in this complex model, we adopt an ant colony optimization algorithm that mimics the path finding process of ants transporting food in nature. The algorithm utilizes both local heuristic and global fitness to search the global optimum of the model. Experimental results verify that the proposed algorithm is able to provide accurate map matching results within a relatively short execution time.

Modeling and simulation of dynamic ant colony’s labor division for task allocation of UAV swarm

Abstract: The problem of unmanned aerial vehicle (UAV) task allocation not only has the intrinsic attribute of complexity, such as highly nonlinear, dynamic, highly adversarial and multi-modal, but also has a better practicability in various multi-agent systems, which makes it more and more attractive recently. In this paper, based on the classic fixed response threshold model (FRTM), under the idea of “problem centered + evolutionary solution” and by a bottom-up way, the new dynamic environmental stimulus, response threshold and transition probability are designed, and a dynamic ant colony’s labor division (DACLD) model is proposed. DACLD allows a swarm of agents with a relatively low-level of intelligence to perform complex tasks, and has the characteristic of distributed framework, multi-tasks with execution order, multi-state, adaptive response threshold and multi-individual response. With the proposed model, numerical simulations are performed to illustrate the effectiveness of the distributed task allocation scheme in two situations of UAV swarm combat (dynamic task allocation with a certain number of enemy targets and task re-allocation due to unexpected threats). Results show that our model can get both the heterogeneous UAVs’ real-time positions and states at the same time, and has high degree of self-organization, flexibility and real-time response to dynamic environments.

Multi-objective dynamic virtual machine consolidation in the cloud using ant colony system

Abstract: In this paper, we present a novel multi-objective ant colony system algorithm for virtual machine (VM) consolidation in cloud data centres. The proposed algorithm builds VM migration plans, which are then used to minimise over-provisioning of physical machines (PMs) by consolidating VMs on under-utilised PMs. It optimises two objectives that are ordered by their importance. The first and foremost objective in the proposed algorithm is to maximise the number of released PMs. Moreover, since VM migration is a resource-intensive operation, it also tries to minimise the number of VM migrations. The proposed algorithm is empirically evaluated in a series of experiments. The experimental results show that the proposed algorithm provides an efficient solution for VM consolidation in cloud data centres. Moreover, it outperforms two existing ant colony optimization-based VM consolidation algorithms in terms of number of released PMs and number of VM migrations.

Energy-Aware Virtual Machine Consolidation Algorithm Based on Ant Colony System

Abstract: Energy consumption has become a critical issue for data centers due to energy-associated costs and environmental effects. In this paper, we propose a new algorithm based on Ant Colony System to solve Virtual Machine Consolidation problem aims to save the energy consumption of cloud data centers. We consider the energy consumption during VMs migration as one of the primary factors which have not considered in the similar conventional algorithms. It significantly reduces the number of migrations and the active physical machines that result in the reduction of total energy consumption of data centers. The simulation results on the random workload in different scenarios demonstrate that the proposed algorithm outperforms the state-of-the-art VM Consolidation algorithm with regards to the number of migrations, number of sleeping PMs, number of SLA Violations, and energy consumption.

Two-generation Pareto ant colony algorithm for multi-objective job shop scheduling problem with alternative process plans and unrelated parallel machines

Abstract: The flexibilities of alternative process plans and unrelated parallel machines are benefit for the optimization of the job shop scheduling problem, but meanwhile increase the complexity of the problem. This paper constructs the mathematical model for the multi-objective job shop scheduling problem with alternative process plans and unrelated parallel machines, splits the problem into two sub-problems, namely flexible processing route decision and task sorting, and proposes a two-generation (father and children) Pareto ant colony algorithm to generate a feasible scheduling solution. The father ant colony system solves the flexible processing route decision problem, which selects the most appropriate process node set from the alternative process node set. The children ant colony system solves the sorting problem of the process task set generated by the father ant colony system. The Pareto ant colony system constructs the applicable pheromone matrixes and heuristic information with respect to the sub-problems and objectives. And NSGAII is used as comparison whose genetic operators are re-defined. The experiment confirms the validation of the proposed algorithm. By comparing the result of the algorithm to NSGAII, we can see the proposed algorithm has a better performance.

Individual crop loads provide local control for collective food intake in ant colonies

Abstract: In an ant society, a small group of workers, called foragers, feeds the rest of the colony. Each forager goes out of the nest to find food; any liquid food she collects is stored in her ‘crop’, a pouch located just upstream of her stomach. When a forager goes back to the nest, she unloads this liquid by mouth-to-mouth contact into the crops of other ants. The foragers need to adjust how often they go on foraging trips based on the amount of food the other ants require at any given time. However, it is still unclear how foragers can assess the changing needs of the colony. For example, it had been assumed that a forager would fully feed the individuals she encounters in the nest and then go for another foraging trip when her crop is empty. Yet, scientists had not managed to track food transfer at the level of the individual insect to confirm if this is the case. Greenwald, Baltiansky and Feinerman have now used laboratory ant colonies and fluorescently labeled food to monitor in real time how food is transferred between individual ants. Contrary to previous hypotheses, when a forager comes back to the nest, she gives small portions of food to many different ants. The insects in the colony are therefore being nourished through these repetitive interactions. As the experiments show, when a forager meets other ants in the nest, the fullness of their crops reliably represents how full the colony is as a whole. Moreover, te portion that the forager gives is, on average, proportional to the space available in the receiver’s crop: the emptier the crop, the more food is given. The amount of food in the crops of the receiving ants therefore controls how much food enters the colony, and the rate at which a forager unloads its crop. A possible mechanism for regulating foraging frequency is that a forager considers whether or not to go on a foraging trip only after she senses a substantial change in the amount of food in her crop. In this case, her decision is based on the fullness of her own crop: the smaller the amount of food left in her pouch, the more likely she is to decide to leave the nest to bring in more food. Because the rate at which the foragers’ crop empties is tied to the amount of food in the receiving ants’ crops, how often the forager goes for food changes with the hunger level of the whole colony, with more trips when the ants are hungrier. These experiments show that the amount of food in the crops of the receiving and foraging ants helps foragers adapt their behavior to the colony’s needs. This mechanism means the insects can achieve a common goal without explicitly knowing it. However, it remains to be explained how exactly the mechanical changes in the fullness of foragers’ crop underpin this decision-making process.

Robot Path Planning Based on Improved Ant Colony Algorithm

Abstract: In the robot path planning, the basic ant colony algorithm is used to find the optimal path, there are some questions of long search time, low efficiency, and easily falling into local optimum. In this paper, the ant colony algorithm is improved for these problems. The introduction of artificial potential field method as the main means of path planning puts forward the principle of unbalanced initial pheromone. Different grid positions assign different initial pheromone and join pheromone trajectory smoothing strategy. Comparing the two kinds of ant colony algorithm and carrying on the simulation analysis, the improved ant colony algorithm is better than the basic ant colony mainly embodied in algorithm in searching ability, more efficient in algorithm and shorter the searching path. The experimental results show that the improved algorithm can improve the efficiency of the algorithm and restrain the algorithm from falling into the local optimum and realize the optimal path search of the robot so that the robot can quickly avoid the obstacle safely reaching the target point.

Comparison of Five Evolutionary Algorithms for Optimization of Water Distribution Networks

Abstract: In this paper, five models based on evolutionary algorithms (EAs) are introduced and compared for the optimization of the design and rehabilitation of water distribution networks. These EAs...

A Comprehensive Comparison of Ant Colony and Hybrid Particle Swarm Optimization Algorithms Through Test Case Selection

Abstract: The focus of this paper is towards comparing the performance of two metaheuristic algorithms, namely Ant Colony and Hybrid Particle Swarm Optimization. The domain of enquiry in this paper is Test Case Selection, which has a great relevance in software engineering and requires a good treatment for the effective utilization of the software. Extensive experiments are performed using the standard flex object from SIR repository. Experiments are conducted using Matlab, where Execution time and Fault Coverage are considered as quality measure, is reported in this paper which is utilized for the analysis. The underlying motivation of this paper is to create awareness in two aspects: Comparing the performance of metaheuristic algorithms and demonstrating the significance of test case selection in software engineering.

A comparative study of several metaheuristic algorithms for optimizing complex 3-D well-path designs

Abstract: Considering the importance of cost reduction in the petroleum industry, especially in drilling operations, this study focused on the minimization of the well-path length, for complex well designs, compares the performance of several metaheuristic evolutionary algorithms. Genetic, ant colony, artificial bee colony and harmony search algorithms are evaluated to seek the best performance among them with respect to minimizing well-path length and also minimizing computation time taken to converge toward global optima for two horizontal wellbore cases: (1) a real well offshore Iran; (2) a well-studied complex trajectory with several build and hold sections. A primary aim of the study is to derive less time-consuming algorithms that can be deployed to solve a range of complex well-path design challenges. This has been achieved by identifying flexible control parameters that can be successfully adjusted to tune each algorithm, leading to the most efficient performance (i.e., rapidly locating global optima while consuming minimum computational time), when applied to each well-path case evaluated. The comparative analysis of the results obtained for the two case studies suggests that genetic, artificial bee colony and harmony search algorithms can each be successively tuned with control parameters to achieve those objectives, whereas the ant colony algorithm cannot.

Off-road Path Planning Based on Improved Ant Colony Algorithm

Abstract: Optimal vehicle off-road path planning problem must consider surface physical properties of terrain and soil. In this paper, we firstly analyse the comprehensive influence of terrain slope and soil strength to vehicle’s off-road trafficability. Given off-road area, the GO or NO-GO tabu table of terrain gird is determined by slope angle and soil remolding cone index (RCI). By applying tabu table and grid weight table, the influence of terrain slope and soil RCI are coordinated to reduce the search scope of algorithm and improve search efficiency. Simulation results based on tracked vehicle M1A1 in off-road environment show that, improved ant colony path planning algorithm not only considers the influence of actual terrain and soil, but also improves computation efficiency. The time cost of optimal routing computation is much lower which is essential for real time off-road path planning scenarios.

D-S evidence theory based trust ant colony routing in WSN

Abstract: This paper proposes a trust ant colony routing algorithm by introducing a node trust evaluation model based on the D-S evidence theory into the ant colony routing protocol to improve the security of wireless sensor networks. To reduce the influence of conflict evidences caused by malicious nods, the consistent intensity is introduced to preprocess conflict evidences before using the D-S combination rule to improve the reliability of the D-S based trust evaluation. The nodes with high trust values will be selected as the routing nodes to insure the routing security, and the trust values are used as heuristic functions of the ant colony routing algorithm. The simulation tests are conducted by using the network simulator NS2 to observe the outcomes of performance metrics of packets loss rate and average end-to-end delay etc. to indirectly evaluate the security issue under the attack of inside malicious nodes. The simulation results show that the proposed trust routing algorithm can efficiently resist malicious attacks in terms of keeping performances of the average end-to-end delay, the throughtput and the routing packet overhead under attacking from malicious nodes.

Energy Efficient Hybrid Routing Protocol Based on the Artificial Fish Swarm Algorithm and Ant Colony Optimisation for WSNs

Abstract: Wireless Sensor Networks (WSNs) are a particular type of distributed self-managed network with limited energy supply and communication ability. The most significant challenge of a routing protocol is the energy consumption and the extension of the network lifetime. Many energy-efficient routing algorithms were inspired by the development of Ant Colony Optimisation (ACO). However, due to the inborn defects, ACO-based routing algorithms have a slow convergence behaviour and are prone to premature, stagnation phenomenon, which hinders further route discovery, especially in a large-scale network. This paper proposes a hybrid routing algorithm by combining the Artificial Fish Swarm Algorithm (AFSA) and ACO to address these issues. We utilise AFSA to perform the initial route discovery in order to find feasible routes quickly. In the route discovery algorithm, we present a hybrid algorithm by combining the crowd factor in AFSA and the pseudo-random route select strategy in ACO. Furthermore, this paper presents an improved pheromone update method by considering energy levels and path length. Simulation results demonstrate that the proposed algorithm avoids the routing algorithm falling into local optimisation and stagnation, whilst speeding up the routing convergence, which is more prominent in a large-scale network. Furthermore, simulation evaluation reports that the proposed algorithm exhibits a significant improvement in terms of network lifetime.

Transmission of fungal partners to incipient Cecropia-tree ant colonies

Abstract: Ascomycete fungi in the nests of ants inhabiting plants (= myrmecophytes) are very often cultivated by the ants in small patches and used as food source. Where these fungi come from is not known yet. Two scenarios of fungus recruitment are possible: (1) random infection through spores or hyphal fragments from the environment, or (2) transmission from mother to daughter colonies by the foundress queen. It is also not known at which stage of the colony life cycle fungiculture is initiated, and whether the- symbiont fungi serve as food for the ant queen. To clarify these questions, we investigated four Azteca ant species inhabiting three different Cecropia species (C. insignis, C. obtusifolia, and C. peltata). We analysed an rRNA gene fragment from 52 fungal patches produced by founding queens and compared them with those from established Azteca colonies (n = 54). The infrabuccal pockets of winged queens were dissected to investigate whether young queens carry fungi from their mother colony. Additionally, 15N labelling experiments were done to verify whether the queen feeds on the patches until she is nourished by her first worker offspring. We infer from the results that the fungi cultivated in hollow plant structures are transferred from the parental colony of the young queen. First, fungal genotypes/OTU diversity was not significantly different between foundress queen patches and established colonies, and second, hyphal parts were discovered in the infrabuccal pockets of female alates. We could show that fungiculture already starts before queens lay their eggs, and that the queens do not feed on fungal patch material but feed it to the larvae. Our findings suggest that fungiculture may be crucial for successful colony founding of arboreal ants in the tropics.

An emerging intelligent optimization algorithm based on trust sensing model for wireless sensor networks

Abstract: Due to the limitation of battery power, processing capacity, and storage, the sensor nodes are easy to be captured, destroyed, or attacked in an open environment. As a result, the security and reliability of data transmission cannot be guaranteed. In order to resist the internal attacks from malicious nodes, an ant colony optimization algorithm for secured routing based on trust sensing model (ACOSR) in wireless sensor networks is proposed. Firstly, a reliable evaluation model of trust perception is presented, which can estimate the node’s trust value derived from its behavior to identify or isolate the malicious nodes effectively. The penalty function and regulator function are applied to reflect the effect of state changes on the trust value according to the node’s behavior in the process of the communication. Secondly, the trust evaluation model is introduced into the ant colony routing algorithm to improve the security for data forwarding. The simulation results show that the proposed algorithm has improved the performance significantly in terms of packet loss rate, end to end delay, throughput, and energy consumption and demonstrates good resistance to black hole attack.

An Ant-Colony-Based Meta-Heuristic Approach for Load Balancing in Cloud Computing

Abstract: This book chapter proposes use of Ant Colony Optimization (ACO), a novel computational intelligence technique for balancing loads of virtual machine in cloud computing. Computational intelligence(CI), includes study of designing bio-inspired artificial agents for finding out probable optimal solution. So the central goal of CI can be said as, basic understanding of the principal, which helps to mimic intelligent behavior from the nature for artifact systems. Basic strands of ACO is to design an intelligent multi-agent systems imputed by the collective behavior of ants. From the perspective of operation research, it’s a meta-heuristic. Cloud computing is a one of the emerging technology. It’s enables applications to run on virtualized resources over the distributed environment. Despite these still some problems need to be take care, which includes load balancing. The proposed algorithm tries to balance loads and optimize the response time by distributing dynamic workload in to the entire system evenly.

The influence of the few: a stable 'oligarchy' controls information flow in house-hunting ants.

Abstract: Animals that live together in groups often face difficult choices, such as which food resource to exploit, or which direction to flee in response to a predator. When there are costs associated with deadlock or group fragmentation, it is essential that the group achieves a consensus decision. Here, we study consensus formation in emigrating ant colonies faced with a binary choice between two identical nest-sites. By individually tagging each ant with a unique radio-frequency identification microchip, and then recording all ant-to-ant 'tandem runs'-stereotyped physical interactions that communicate information about potential nest-sites-we assembled the networks that trace the spread of consensus throughout the colony. Through repeated emigrations, we show that both the order in which these networks are assembled and the position of each individual within them are consistent from emigration to emigration. We demonstrate that the formation of the consensus is delegated to an influential but exclusive minority of highly active individuals-an 'oligarchy'-which is further divided into two subgroups, each specialized upon a different tandem running role. Finally, we show that communication primarily occurs between subgroups not within them, and further, that such between-group communication is more efficient than within-group communication.

Comparative Study of Ant Colony Algorithms for Multi-Objective Optimization

Abstract: In recent years, when solving MOPs, especially discrete path optimization problems, MOACOs concerning other meta-heuristic algorithms have been used and improved often, and they have become a hot research topic. This article will start from the basic process of ant colony algorithms for solving MOPs to illustrate the differences between each step. Secondly, we provide a relatively complete classification of algorithms from different aspects, in order to more clearly reflect the characteristics of different algorithms. After that, considering the classification result, we have carried out a comparison of some typical algorithms which are from different categories on different sizes TSP (traveling salesman problem) instances and analyzed the results from the perspective of solution quality and convergence rate. Finally, we give some guidance about the selection of these MOACOs to solve problem and some research works for the future.

Small differences in learning speed for different food qualities can drive efficient collective foraging in ant colonies

Abstract: Social insects frequently make important collective decisions, such as selecting the best food sources. Many collective decisions are achieved via communication, for example by differential recruitment depending on resource quality. However, even species which only rarely recruit can respond to a changing environment on a collective level by tracking food source quality. We hypothesised that an apparent collective decision to focus on the highest quality food source can be explained by differential learning of food qualities. Overall, ants may learn the location of higher quality food faster, with most ants finally congregating at the best food source. To test the effect of reward quality and motivation on learning in Lasius niger, we trained individual ants to find sucrose molarities of varying concentrations on one arm of a T-maze in spring and in autumn after 1 or 4 days of food deprivation. As predicted, ants learned fastest in spring and lowest in autumn, with reduced food deprivation leading to slower learning. Surprisingly, the effect of food quality and motivation on the learning speed was small. However, persistence rates varied dramatically: All ants in spring made all (6) return visits to all food qualities, in contrast to only 33% of ants in autumn after 1 day of food deprivation. Fitting the empirical findings into an agent-based model revealed that even a weak tendency of ants to memorise routes to high-quality food sources faster can result in ecologically sensible colony-level behaviour. This collective-seeming decision is non-interactive, and thus resembles an annealing process. Collective decisions of insects are often achieved via communication and/or other interactions between individuals. However, animals can also make collective decisions in the absence of communication. We show that foraging motivation and food quality can affect both route memory formation speed and the likelihood to return to the food source and thus mediate selective food exploitation. An agent-based model, implemented with our empirical findings, demonstrates that at the collective level even small differences in learning lead to ecologically sensible behaviour: mildly food-deprived colonies are selective for high-quality food while highly food-deprived colonies exploit all food sources equally. We therefore suggest that non-interactive factors such as individual learning and the foraging motivation of a colony can mediate or even drive group-level behaviour in a process resembling annealing. Instead of accounting collective behaviour exclusively to social interactions, a possible contribution of individual processes should also be considered.

Omicron ACO. A New Ant Colony Optimization Algorithm

Abstract: Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies that has been successful in the resolution of hard combinatorial optimization problems like the Traveling Salesman Problem (TSP). This paper proposes the Omicron ACO (OA), a novel population-based ACO alternative originally designed as an analytical tool. To experimentally prove OA advantages, this work compares the behavior between the OA and the MMAS as a function of time in two well-known TSP problems. A simple study of the behavior of OA as a function of its parameters shows its robustness.

Hybrid energy-efficient APTEEN protocol based on ant colony algorithm in wireless sensor network

Abstract: Due to the limited energy of the sensor nodes, the unreasonable clustering routing algorithm will cause node premature death and low utilization of energy efficiency in wireless sensor network (WSN). In Adaptive Threshold-sensitive Energy Efficient Network (APTEEN), the assignments of the cluster head (CH) are much heavier than other nodes. The CH unbalanced energy dissipation between nodes that make them die prematurely. Ant colony algorithm can avoid this problem, so this paper presents a double cluster heads Adaptive Threshold-sensitive Energy Efficient Network based on ant colony (ADCAPTEEN). ADCAPTEEN optimizes the cluster head election method compared with APTEEN. It suggests that one master cluster head (MCH) and one vice cluster head (VCH) will be selected in each cluster. The double cluster heads (DCH) can co-work on data collection, fusion, transition, etc. To make routes more stable and energy efficient, this paper proposes a Multiple Adaptive Threshold-sensitive Energy Efficient Network based on Ant-colony (AMAPTEEN). It is the optimization of ADCAPTEEN. And CH selects intermediate node (IM_node) multiple times with ant colony algorithm per round in each cluster, and this way forms multiple route transmission data. Simulation in OPNET proves that compared with APTEEN, ADCAPTEEN reduces energy dissipation, improves node survival rate, and extends network life cycle. AMAPTEEN delays the time of node death, balances energy consumption, and extends network lifetime further operating in the same settings compared with ADCAPTEEN. The proposed two algorithms have good scalability, and they are suitable for large-scale network.

Ants: Ecology and Impacts in Dead Wood

Abstract: Although rarely considered as a saproxylic insect group, ants are an important, highly abundant insect taxon in dead wood environments worldwide. Ants directly impact the dead wood environment primarily through nesting in standing dead trees, logs, stumps, and coarse and fine woody materials, contributing to the physical breakdown of woody materials. Ants indirectly impact the dead wood environment through predation of a wide variety of arthropods, particularly termites, and by serving as a food source for other animals, particularly birds (woodpeckers) and bears that physically break down dead wood to prey upon ant colonies. The known impacts of ant nesting and predation in dead wood are reviewed with a case study that provides new information on the role of abiotic factors affecting nesting site location in dead wood in the eastern temperate US forests. Results showed horizontal and vertical nest stratification of ant nests that shifted with spatial scale. At broad scales, climate determines disparate ranges among species across a latitudinal gradient. At the scale of a forest floor, however, microsite temperature, moisture, and biotic interactions affect nesting locations in downed logs. Future research aimed at better understanding the interactions between ants and other organisms in dead wood environments is necessary to improve our understanding of the importance of ants in shaping dead wood communities and ecosystem processes like decomposition.

A Novel Algorithm for Bi-Level Image Coding and Lossless Compression based on Virtual Ant Colonies

Abstract: Ant colonies emerged as a topic of research and they are applied in different fields. In this paper, we develop an algorithm based on the concept of ant colonies and we utilize it for image coding and compression. To apply the algorithm on images, we represent each image as a virtual world which contains food and routes for ants to walk and search for it. Ants in the algorithm have certain type of movements depending on when and where they find food. When an ant finds food, it releases a pheromone, which allows other ants to follow the source of food. This increases the likelihood that food areas are covered. The chemical evaporates after a certain amount of time, which in turn helps ants move to cover another food area. In addition to the pheromone, ants use proximity awareness to detect other ants in the surrounding, which can help ants cover more food areas. When an ant finds food, it moves to that location and the movement and coordinates are recorded. If there is no food, an ant moves randomly to a location in the neighborhood and starts searching. We ran our algorithm on a set of 8 images and the empirical results showed that we could outperform many techniques in image compression including JBIG2.