Ant-inspired Algorithm Research Articles

AntTrust: An Ant-Inspired Trust Management System for Peer-to-Peer Networks.

In P2P networks, self-organizing anonymous peers share different resources without a central entity controlling their interactions. Peers can join and leave the network at any time, which opens the door to malicious attacks that can damage the network. Therefore, trust management systems that can ensure trustworthy interactions between peers are gaining prominence. This paper proposes AntTrust, a trust management system inspired by the ant colony. Unlike other ant-inspired algorithms, which usually adopt a problem-independent approach, AntTrust follows a problem-dependent (problem-specific) heuristic to find a trustworthy peer in a reasonable time. It locates a trustworthy file provider based on four consecutive trust factors: current trust, recommendation, feedback, and collective trust. Three rival trust management paradigms, namely, EigenTrust, Trust Network Analysis with Subjective Logic (TNA-SL), and Trust Ant Colony System (TACS), were tested to benchmark the performance of AntTrust. The experimental results demonstrate that AntTrust is capable of providing a higher and more stable success rate at a low running time regardless of the percentage of malicious peers in the network.

Population Diversity in Ant-inspired Optimization Algorithms

Finding a balance between exploration and exploitation is very important in the case of metaheuristics optimization, especially in the systems leveraging population of individuals expressing (as in Evolutionary Algorithms, etc.) or constructing (as in Ant Colony Optimization) solutions. Premature convergence is a real problem and finding means of its automatic detection and counteracting are of great importance. Measuring diversity in Evolutionary Algorithms working in real-value search space is often computationally complex, but feasible while measuring diversity in combinatorial domain is practically impossible (cf. Closest String Problem). Nevertheless, we propose several practical and feasible diversity measurement techniques dedicated to Ant Colony Optimization algorithms, leveraging the fact that even though analysis of the search space is at least an NP problem, we can focus on the pheromone table, where the direct outcomes of the search are expressed and can be analyzed. Besides proposing the measurement techniques, we apply them to assess the diversity of several variants of ACO, and closely analyze their features for the classic ACO. The discussion of the results is the first step towards applying the proposed measurement techniques in auto-adaptation of the parameters affecting directly the exploitation and exploration features in ACO in the future.

Fast Transfer of Packets Through DB Routing Using Ant Colony Optimization

Routing in computer network is an essential functionality, which influences both the network management as the quality of services in global networks. The management of the traffic flows has to satisfy requirements for volume of traffic to be transmitted as avoidance of congestions for decreasing the transmission delays. These two requirements in general are contradictory. The optimal traffic management is a key issue for the quality of the information services. This Paper presents Depth wise Breadth wise (DB) algorithm, which provides a path to an incoming packet much faster than existing routing algorithms. This is basically an Artificial Intelligence (AI) Concept, which is useful to get from the source to the destination. Moreover, the proposed algorithm is a combination of Depth First search (DFS) and Breadth First Search (BFS) searching techniques. In this paper, DB Routing has been developed based on a general-purpose metaheuristic named Ant Colony Optimization, which is a framework for building ant-inspired algorithms. DB is applied as the routing algorithm in a simulated packet-switched point-to-point network. It is investigated whether DB is able to obtain an increase in throughput when packets are sent between two distinct nodes. Moreover, it is investigated how prioritizing different heuristics effect the quality of the routing performed. It is concluded that DB behaves differently depending on the relative priority of positive feedback negative feedback and local heuristics. Also it is possible to adjust the parameters to achieve best results by improving in routing time.

An Adapted Ant-Inspired Algorithm for Enhancing Web Service Composition

Web Service Composition (WSC) provides a flexible framework for integrating independent web services to satisfy complex user requirements. WSC aims to choose the best web service from a set of candidates. The candidates have the same functionality and different non-functional criteria such as Quality of Service (QoS). In this work, the authors propose an ant-inspired algorithm for such problem. They named it Flying Ant Colony Optimization (FACO). Flying ants inject pheromone not only on the nodes on their paths but also on neighboring nodes increasing their chances of being explored in future iterations. The amount of pheromone deposited on these neighboring nodes is inversely proportional to the distance between them and the nodes on the path. The authors believe that by depositing pheromone on neighboring nodes, FACO may consider a more diverse population of solutions, which may avoid stagnation. The empirical experiments show that FACO outperform Ant Colony Optimization (ACO) for the WSC problem, in terms of the quality of solutions but it requires slightly more execution time.

Ant-Inspired Mini-Community-Based Solution for Video-On-Demand Services in Wireless Mobile Networks

Highly efficient distribution and management of media resources and fast content discovery are key determinants for mobile peer-to-peer video-on-demand solutions, especially in wireless mobile networks. Virtual communities making use of users' common characteristics such as interest and interaction to describe the boundary of sharing content and objects are a promising avenue for high-efficiency resource sharing. In this paper, we propose a novel ant-inspired mini-community-based video sharing solution for on-demand streaming services in wireless mobile networks (AMCV). AMCV relies on a newly designed two layer architecture and on an algorithm inspired from the indirect communication between ants via pheromone trails which enables them to discover and use shortest paths. The architecture is composed of a mini-community network layer and a community member layer. The ant-inspired algorithm enables finding the common interest of users in video content within large amounts of pseudo disorderly interactive behavior data. AMCV proposes an ant colony optimization-based community communication strategy that dynamically bridges communities to support fast search for resources. AMCV achieves high scalability by making use of a designed community maintenance mechanism to uniformly distribute the maintenance cost of members and resources in the community, according to various member roles. Simulation-based testing shows how AMCV outperforms another state-of- the-art solution in terms of a wide set of performance metrics.

CELLULAR ANTOMATA

Problems involving path planning, exploration, and related activities abound in the world of computing. This paper develops a novel model for realizing ant-inspired algorithms that coordinate robots within fixed, geographically constrained environments ("factory/laboratory/warehouse floors") and studies the model via sample tasks that illustrate various aspects of path planning and exploration. The model, dubbed a Cellular ANTomaton (C-ANTomaton, for short), inverts the relationship between ant-robots and the environments that they navigate: "Intelligence" now resides in the "floor" rather than in the ant-robots. The C-ANTomaton model is illustrated via three proof-of-concept problems: (1) Parking requires ant-robots to congregate in their nearest corners of the "floor," in a maximally compact formation. (2) Food-seeking (both with and without impenetrable obstacles) requires each ant-robot to find its own "food" item, until either "foodless" ant-robots or unclaimed "food" items run out. (3) Maze-threading requires a single ant-robot to find the unique exit to a maze. "Unintelligent" C-ANTomaton-based robots accomplish all of these goals provably more efficiently than traditional "intelligent" ant-robots can; indeed, "intelligent" ant-robots cannot always park at all! All of the presented algorithms are scalable, in that they provably work for any number of ant-robots, within any finite-size "floor." In other words, the computers that collectively supply the system with "intelligence" never exploit any information about either the number of ant-robots or the size of the "floor."

An ant-inspired algorithm for detection of image edge features

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.

Self-Chord: A Bio-Inspired P2P Framework for Self-Organizing Distributed Systems

This paper presents Self-Chord, a peer-to-peer (P2P) system that inherits the ability of Chord-like structured systems for the construction and maintenance of an overlay of peers, but features enhanced functionalities deriving from ant-inspired algorithms, such as autonomous behavior, self-organization, and capacity to adapt to a changing environment. As opposed to the structured P2P systems deployed so far, resource indexing and placement is uncorrelated with network structure and topology, and resource keys are organized and managed by self-organizing mobile agents through simple local operations driven by probabilistic choices. Self-Chord has three main features that are particularly advantageous in Grid and Cloud Computing: 1) it is possible to give a semantic meaning to keys, which enables the execution of range queries; 2) the keys are fairly distributed over the peers, thus improving the balancing of storage responsibilities; 3) maintenance load is also limited because it is not necessary to reassign keys when new peers or resources are added to the system--the mobile agents will spontaneously reorganize the keys. The efficiency and effectiveness of Self-Chord were assessed both with a simulation framework and with an analytical model inspired by fluid dynamics.

COOPERATIVE SELF-COMPOSITION AND DISCOVERY OF GRID SERVICES IN P2P NETWORKS

The desirable global scalability of Grid systems has steered the research towards the employment of the peer-to-peer (P2P) paradigm for the development of new resource discovery systems. As Grid systems mature, the requirements for such a mechanism have grown from simply locating the desired service to compose more than one service to achieve a goal. In Semantic Grid, resource discovery systems should also be able to automatically construct any desired service if it is not already present in the system, by using other, already existing services. In this paper, we present a novel system for the automatic discovery and composition of services, based on the P2P paradigm, having in mind (but not limited to) a Grid environment for the application. The paper improves composition and discovery by exploiting a novel network partitioning scheme for the decoupling of services that belong to different domains and an ant-inspired algorithm that places co-used services in neighbouring peers.

Ant-like task allocation and recruitment in cooperative robots.

One of the greatest challenges in robotics is to create machines that are able to interact with unpredictable environments in real time. A possible solution may be to use swarms of robots behaving in a self-organized manner, similar to workers in an ant colony. Efficient mechanisms of division of labour, in particular series-parallel operation and transfer of information among group members, are key components of the tremendous ecological success of ants. Here we show that the general principles regulating division of labour in ant colonies indeed allow the design of flexible, robust and effective robotic systems. Groups of robots using ant-inspired algorithms of decentralized control techniques foraged more efficiently and maintained higher levels of group energy than single robots. But the benefits of group living decreased in larger groups, most probably because of interference during foraging. Intriguingly, a similar relationship between group size and efficiency has been documented in social insects. Moreover, when food items were clustered, groups where robots could recruit other robots in an ant-like manner were more efficient than groups without information transfer, suggesting that group dynamics of swarms of robots may follow rules similar to those governing social insects.