Ant Agents Research Articles

A hierarchical heterogeneous ant colony optimization based oversampling algorithm using feature similarity for classification of imbalanced data

Imbalanced data classification is one of the challenging problems in machine learning. Oversampling is a promising technique that generates synthetic minority instances to balance the dataset. Inappropriate minority instances generated may deteriorate the performance of the classifier. Majority of the oversampling algorithms create new minority instances by choosing nearest neighbors for random interpolation. However, these methods do not provide new information to the dataset and therefore standard classifiers do not show good performance on such datasets. Therefore, it is necessary to generate diverse minority class instances to increase the performance of the classifier. Since, every feature of each minority class instance contribute valuable information, generating synthetic instances from the features of all minority instances would produce diverse minority instances, thereby increasing the performance of the classifier. This paper proposes a Hierarchical Heterogeneous Ant Colony Optimization based oversampling algorithm using Feature Similarity (HHACO-FSOTe) for generation of synthetic minority instances. Instead of choosing few neighbors for interpolation, the proposal considers all minority instances for generation of synthetic instances. HHACO-FSOTe generates new feature values by computing the minimum absolute difference between the features of a given minority instance and the corresponding features of the remaining minority instances. The features in the dataset are distributed among the ant agents enabling parallelism, thereby reducing the time taken for oversampling. HHACO-FSOTe do not require parameter tuning or training. The proposal is evaluated on 41 low dimensional, 11 high dimensional and 8 noisy datasets. Experiments reveal that HHACO-FSOTe is competent with the state-of-art oversampling techniques. Results were validated using non-parametric statistical tests.

An Operative IoT Grounded AEEBLR (Ant-Founded Efficient Energy and Balanced Load Routing) Method for Path Conjunction in Mobile Ad Hoc Networks Approach

An architecture for a wireless network that is constantly developing, decentralized, and multi-hop is called a mobile ad hoc network. MANETs are able to function in many different contexts where regular networks are unable. As can be seen from the advantages listed above, these networks are well-suited for a wide variety of applications, some of which include military and commercial use, as well as applications relating to disaster management, rescue operations, and defense. Energy conservation is a standard factor that indicates the overall network lifetime in mobile ad hoc networks that operate on rechargeable or replaceable battery. This is because usage, battery power consumption in relation to transmission range, type of application running on each device, location, and other influences all play a part in determining the overall network lifetime. An earlier study used a method called ant colony optimization, which is a form of swarm intelligence enthused by the activity of foraging ants in colonies. The best possible travel plan was found using this strategy. Current MANETS routing systems face difficulties in load balancing and energy efficiency that must be overcome if optimal path convergence is to be achieved. When deciding on the next hop node, the IoT based AEEBLR method is recommended. The latency, energy consumption, congestion, and connection quality are all taken into account before making a final decision. The likelihood of selecting the next-hop node as the neighbor node is determined using these metrics. It is the following hop's probability that determines which ant agent goes forward and which goes backward. This paves the door for the creation of many paths, from which the most effective might be chosen for transmission. The results of the implementation show that the suggested AEEBLR technique outperforms the existing AESR approach when the number of packets, the number of nodes, and the mobility of nodes are all varied.

Backpropagation-free 4D continuous ant-based neural topology search

Continuous Ant-based Topology Search (CANTS) is a previously introduced novel nature-inspired neural architecture search (NAS) algorithm that is based on ant colony optimization (ACO). CANTS utilizes a continuous search space to indirectly-encode a neural architecture search space. Synthetic ant agents explore CANTS’ continuous search space based on the density and distribution of pheromones, strongly inspired by how ants move in the real world. This continuous search space allows CANTS to automate the design of artificial neural networks (ANNs) of any size, removing a key limitation inherent to many current NAS algorithms that must operate within structures of a size that is predetermined by the user. This work expands CANTS by adding a fourth dimension to its search space representing potential neural synaptic weights. Adding this extra dimension allows CANTS agents to optimize both the architecture as well as the weights of an ANN without applying backpropagation (BP), which leads to a significant reduction in the time consumed in the optimization process: at least an average of 96% less time consumption with very competitive optimization performance, if not better. The experiments of this study – using real-world data – demonstrate that the BP-Free CANTS algorithm exhibits highly competitive performance compared to both CANTS and ANTS while requiring significantly less operation time.

A hierarchical heterogeneous ant colony optimization based fingerprint recognition system

Personal identification is crucial to secure data against cyber-attacks. With increasing identity theft, fingerprint recognition systems have a growing importance in enforcing security and reliable identification. Although, most fingerprint recognitions systems use minutiae features for fingerprint matching, they require extensive pre-processing of fingerprints when the image quality is poor. This may introduce false ridge patterns, degrading the performance of the system. Moreover, fingerprint matching over a large database can be inefficient due to high computation time of fingerprint matching algorithms. This demand for fingerprint recognition systems that are fast and reliable. This paper proposes a computationally intelligent fingerprint recognition system that extracts ridge patterns from the fingerprint for matching. Hierarchical Heterogeneous Ant Colony Optimization based Fingerprint Matching (HHACOFM) algorithm has ant agents at different levels in the hierarchy to find a match between the input and stored ridge patterns. The algorithm was evaluated over four databases: a synthetic database generated using SFinGe tool, an internal database, SOCOFing database and FVC2004 database. Experimental results indicate that the proposal achieves high recognition rate compared to the existing approaches. HHACOFM algorithm achieves less EER than the state-of-art approaches. The results were validated using statistical tests. HHACOFM enables parallelism and thus reduces the response time. The proposal is scalable and suitable for real time applications demanding fast fingerprint verification.

Energy based random repeat trust computation approach and Reliable Fuzzy and Heuristic Ant Colony mechanism for improving QoS in WSN

The furthermost important issues in WSN are to provide a multi-path and multi-constraint Qualify of Service (QoS) routing for uninterrupted communications between the nodes. On one hand, multi-constrained QoS routing deals with identifying paths that assure numerous autonomous QoS limitations, and on the other hand, multi-path refers to the identification of multiple paths. The EB-RRTC (Energy based random repeat trust computation) algorithm is employed for encountering trusted nodes with destination node. The proposed method also employs the Reliable Fuzzy and Heuristic Concurrent ACO (RF-HEACO) QoS Routing Protocol to improve QoS. The HEACO-RF QoS Routing Protocol includes two phases. In the first phase, the Ant Colony Optimization algorithm is modified to identify a deposit of candidate routes among a pair of source and destination. While identifying the candidate routes along with the QoS metrics, heuristic factors and Reliable Fitness Functions are considered. In the second phase, the reliability technique is used to measure each path, and the paths with high reliability is selected by the ant agents using Fuzzy logic based on the metrics link stability, residual energy, and packet loss rate. Simulation outcomes prove that RF-HEACO QoS routing protocol can decrease the energy consumption and improve the packet delivery ratio, and packet loss rate with the minimum delay when compared with the techniques. In other words, our method is well capable of offering improved QoS (Quality of Service).

Ant Colony Optimization based Light weight Binary Search for efficient signature matching to filter Ransomware

Ransomware is a form of malicious software which when deployed encrypts or locks the files and demands a ransom to have the files decrypted and accessible. In today’s digital world, devices connected to the network are vulnerable to ransomwares. Signature based methods are used to detect known ransomwares at an early stage to minimize the damage. This type of detection involves a predefined repository of static signatures which block the ransomwares based on the signatures in the repository. Fuzzy hashing method and Clam Av method for signature based ransomware detection have been proposed in literature. However, these methods raised false negatives and false positives respectively. Also, the speed of comparison in fuzzy hashing method was a limitation. In this paper an Ant Colony Optimization (ACO) based approach for filtering ransomwares by matching the incoming signature hash value with the signature database is proposed. Termed Ant Colony Optimization based Light weight Binary Search for Signature based Ransomware detection (ACOLBSR) algorithm, the ant agent finds the search space in the signature database. If a search space does not exist in the signature database, the incoming file is logged. If the search space exists in the database, the ant agent employs a binary search to match the incoming signature hash value with the signatures in the search space. If a match is found, the file is accepted or blocked if it is a goodware or ransomware respectively. If the incoming signature does not match with the signatures in the search space, the file is logged. The advantage of ACOLBSR algorithm is that the ransomwares are strictly filtered based on the signatures in the database. The computational complexity of ACOLBSR algorithm is less compared to binary search and sequential search. Also, it is shown that ACOLBSR algorithm do not generate false positives when compared to ClamAV, SplitScreen, fuzzy hashing and classification based approaches. Experimental results comparing the performance of the ACOLBSR scheme with the binary search scheme, sequential search scheme and classification based approaches are presented.

RETRACTED ARTICLE: Heuristic ant colony and reliable fuzzy QoS routing for mobile ad hoc network

The furthermost important issues in mobile ad hoc network (MANET) are to provide a multi-path and multi-constraint qualify of service (QoS) routing for uninterrupted communication between the nodes. On one hand multi-constrained QoS routing deal with identifying paths that assure numerous autonomous QoS limitations and on the other hand, multi-path refers to the identification of multiple paths. Besides due to higher rate of topological changes and the energy constrained nodes in the network, literature survey provides us ant colony optimization (ACO), a unique variety of optimization procedure which was found to be extremely convenient for identifying reliable routing. To deal with following concerns, we suggest a multi-path multi-constraint energy-efficient routing method. In the projected method, calculate the metrics such as end to end delay and QoS monitoring agents collect, link stability, energy consumption, available bandwidth, and packet loss rate. The proposed method is called as the heuristic concurrent ACO and reliable fuzzy (HEACO–RF) QoS routing protocol for MANET. The HEACO–RF QoS routing protocol for MANET includes two phases. In the first phase, the ant colony optimization algorithm is modified to identify a deposit of candidate routes among a pair of source and destination. While identifying the candidate routes along with the QoS metrics, heuristic factors and reliable fitness functions are also considered. In the second phase, the reliability technique is used to measure each path and the paths with high reliability is selected by the ant agents using fuzzy logic based on the metrics link stability, residual energy, and packet loss rate. Simulation outcomes prove that HEACO–RF QoS routing protocol decreases the energy consumption and improves the packet delivery ratio, packet loss rate with minimum delay.

Ant Colony Optimization Using Common Social Information and Self-Memory

Ant colony optimization (ACO), which is one of the metaheuristics imitating real ant foraging behavior, is an effective method to find a solution for the traveling salesman problem (TSP). The rank-based ant system (ASrank) has been proposed as a developed version of the fundamental model AS of ACO. In the ASrank, since only ant agents that have found one of some excellent solutions are let to regulate the pheromone, the pheromone concentrates on a specific route. As a result, although the ASrank can find a relatively good solution in a short time, it has the disadvantage of being prone falling into a local solution because the pheromone concentrates on a specific route. This problem seems to come from the loss of diversity in route selection according to the rapid accumulation of pheromones to the specific routes. Some ACO models, not just the ASrank, also suffer from this problem of loss of diversity in route selection. It can be considered that the diversity of solutions as well as the selection of solutions is an important factor in the solution system by swarm intelligence such as ACO. In this paper, to solve this problem, we introduce the ant system using individual memories (ASIM) aiming to improve the ability to solve TSP while maintaining the diversity of the behavior of each ant. We apply the existing ACO algorithms and ASIM to some TSP benchmarks and compare the ability to solve TSP.

Ant-Inspired Recurrent Deep Learning Model for Improving the Service Flow of Intelligent Transportation Systems

Intelligent Transportation System (ITS) serves as the on-the wheel communication and service platform for the real-world driving users. Navigation service and traffic information flow among the connected vehicles relies on the available resources and infrastructure units. Appropriate sensing and selection of infrastructure units for seamless navigation responses and information flow in the dynamic environment is facilitated using bio-inspired learning in this article. This method named as ant-inspired recurrent learning model (ARLM) introduced in this article is focused to improve the sensing and response rate of the navigation-based services for the vehicles. This model relies on forward and backward ant agents and recurrent learning for maximizing the navigation service response rate of the vehicles. In this model, the training sets are differentiated on the basis of connection probability and learning depreciation to retain the service rate through different learning iterates. The conditional verification for sensing and retaining active link helps to reduce the sensing and response time irrespective of the varying vehicle and request densities. The performance of the proposed model is verified using suitable experimental analysis and the metrics information flow rate, service sensing, sensing time, response rate, and response time are analyzed for assessing ARLM.

Development of a Convolution-Based Multi-Directional and Parallel Ant Colony Algorithm Considering a Network with Dynamic Topology Changes

While network path generation has been one of the representative Non-deterministic Polynomial-time (NP)-hard problems, changes of network topology invalidate the effectiveness of the existing metaheuristic algorithms. This research proposes a new and efficient path generation framework that considers dynamic topology changes in a complex network. In order to overcome this issue, Multi-directional and Parallel Ant Colony Optimization (MPACO) is proposed. Ant agents are divided into several groups and start at different positions in parallel. Then, Gaussian Process Regression (GPR)-based pheromone update method makes the algorithm more efficient. While the proposed MPACO algorithm is more efficient than the existing ACO algorithm, it is limited in a network with topological changes. In order to overcome the issue, the MPACO algorithm is modified to the Convolution MPACO (CMPACO) algorithm. The proposed algorithm uses the pheromone convolution method using a discrete Gaussian distribution. The proposed pheromone updating method enables the generation of a more efficient network path with comparatively less influence from topological network changes. In order to show the effectiveness of CMPACO, numerical networks considering static and dynamic conditions are tested and compared. The proposed CMPACO algorithm is considered a new and efficient parallel metaheuristic method to consider a complex network with topological changes.

Ant-based efficient energy and balanced load routing approach for optimal path convergence in MANET

Ant colony optimization, a swarm intelligence technique, inspired by the foraging behavior of ants in colonies was used in the past research works to compute the optimal path. The existing works of routing using ant colony optimization of MANETS face challenges in load balancing and energy efficiency. The proposed A-EEBLR approach chooses the next hop node based on metrics like delay, energy drain rate, congestion, link quality. Based on these metrics the probability of choosing next hop node as neighbor node is determined. The next hop probability determines the forward and backward ant agents to establish multiple paths among which the most optimal path is selected for transmission. The implementation results shows that the proposed A-EEBLR approach outperforms the existing A-ESR approach when evaluated by varying the number of packets, number of nodes and node mobility.

Evolving ant colony system for large-sized integrated process planning and scheduling problem considering sequence-dependent setup times

This paper proposes a new ant colony optimization (ACO) algorithm suitable for integrated process planning and scheduling (IPPS) that optimizes both process planning and scheduling simultaneously. The IPPS covered in this study, when compared to the conventional IPPS, is more flexible and complicated because sequence-dependent setups and tool-related capacity constraints are additionally considered. Traditional ACOs have limitations in improving the solution quality and computation time for IPPS. The high flexibility and complexity of IPPS requires a large size of repository for pheromone trails and it causes the long computation time for updating them, excessive evaporation of pheromones, and unbalancing between pheromones and desirability. In the proposed ACO, each ant agent improves their own incumbent solution or finds a new solution using the pheromone trails that is composed of the experience information of the colony. Therefore, the proposed ACO conducts individual and cooperative evolving at the same time. Furthermore, we propose a simplified updating rule for pheromone trails and standardization of the transition probability to increase efficiency of the algorithm. Experimental results show that the proposed ACO is superior to recently proposed meta-heuristics for benchmark problems of different sizes in terms of both solution quality and computation time.

Formation Control of Swarm Robots Using Mobile Agents

In this paper, we propose an algorithm for controlling a fleet of swarm robots that construct three-dimensional forms. The swarm robots coordinate with each other through network communication, and compose formations such as polyhedrons presented as spherical coordinates. Our control algorithm achieves communication through mobile software agents, which introduce control programs to robots that initially have no information about the formation. Mobile software agents are autonomous objects that can migrate from one robot to other robots through a communication network and can deliver control programs as they are needed. We have made our swarm robot system to mimic the behaviors of the leafcutter ants. A leafcutter ant is a typical social insect and uses pheromone for communication. In our robot control system, we have implemented ants and pheromones as mobile software agents. We call the mobile software agents that drive the mobile robots as ant agents, and call the other agents that provide communication as pheromone agents. The ant agents drive the swarm robots to locations identified by the pheromone agents. Each ant agent has only partial information. There is no need for either a central control or an agent that has the entire design of the formation. In order to diffuse the partial information among the neighboring robots, each ant agent generates pheromone agents and dispatches them to the surrounding robots. Dispatched pheromone agent looks for a proper ant agent to influence towards a desired relative location. It is the ant agent that actually drives the robot by following the guidance of the pheromone agent, and the collective actions of ant agents and pheromone agents achieve the composition of the objective formation. We have implemented a simulator based on our algorithm and conducted numerical experiments. The results demonstrate that our mobile robot control system is feasible and efficient in practice in practical situations.

Integral patient scheduling in outpatient clinics under demand uncertainty to minimize patient waiting times.

This study addressed the problem of scheduling walk-in patients in real time. Outpatient clinics encounter uncertainty in patient demand. In addition, the disparate departments are locally (department-centric) organized, leading to prolonged waiting times for patients. The proposed integral patient scheduling model incorporates the status and information of all departments in the outpatient clinic along with all possible pathways to direct patients, on their arrival, to the optimal path. The developed hybrid ant agent algorithm identifies the optimal path to reduce the patient waiting time and cycle time (time from registration to exit). An outpatient clinic in Aravind Eye Hospital, Madurai, has a huge volume of walk-in patients and was selected for this study. The simulation study was performed for diverse scenarios followed by implementation study. The results indicate that integral patient scheduling reduced waiting time significantly. The path optimization in real time makes scheduling effective and efficient as it captures the changes in the outpatient clinic instantly.

Improved DV-Hop Algorithm Based on Ant Colony Algorithm and Particle Swarm Optimization for Wireless Sensor Network Location Problem

Wireless sensor network location problem is one of hot focus in the academic community recently and DV-Hop algorithm is commonly used to solve the typical location algorithms in wireless sensor Network. In order to improve the efficiency of the DV-Hop algorithm, ant colony algorithm and particle swarm optimization have been brought into DV-Hop algorithm for optimizing globally. The AP-DV-Hop algorithm makes use of the advantages of ACA and PSO so as to guarantee the globality of optimal solutions which avoids trapping in local optimum with the help of the particle nature of ant agents and initialized random solutions. Finally, the simulation experiment results have shown that AP-DV-Hop algorithm has smaller average location error than DV-Hop algorithm and genetic algorithm.

Anomaly detection in production facility network using ant agents

With the development of the information and communication technologies, the concept of Internet of Things has been popular. Production facilities in factories are connected to the Internet to instruct productions and to manage performance of them. Resulting from Internet connection of production facilities, it is increasing the risk of cyber-attacks targeting to the facilities. This paper proposes a method to detect anomaly behavior of production facilities influenced by cyber-attacks in which artificial ant agents are employed. Computer experiments are conducted to confirm the effectiveness of the proposed method.

On Comparative Study for Two Selective Ant Colony's Optimal Decisions Versus Reconstruction Problem Solving by a Mouse Inside Figure of Eight (8) Maze

This piece of research addresses an interdisciplinary comparative study of two environmental challenging phenomenal issues. Both were associated to two nonhuman creatures characterized by their behavioral intelligent performance concerned with optimal diverse decisions paradigms. More specifically, this paper deals with the comparison study for analogical behavioral learning of social insects (Ants) colony performance, versus performed behavioral learning achievement by an animal considering a mouse inside Figure of eight (8) maze via its brain hippocampus time cells neurons. In more precise details, this paper firstly have demonstrated for Ant Colony System (ACS) the two effective optimal selectivity decisions for : a) The best source location between two food sources that are equidistantly sited away from the original home nest, based upon pheromone trails and following the tandem running regulation & b) The balanced selection performance with the migration speed, in order to minimize exposure to a hostile environment to avoid vulnerability to presumable danger. Secondly, the optimal decisional issue is demonstrated related to mouse's behavioral learning intelligent approach which observed in practice following its active sequential trials aiming to reach the optimal solution for a reconstruction problem during its movement inside figure of eight (8) maze. Finally, after running of realistic Artificial Neural Networks' (ANNS) simulation programs. Interestingly, the obtained results considering models of both of suggested intelligent behavioral learning issues characterized by relevant functional analogy considering changed number of artificial ant agents versus the various number of neurons inside mouse's hippocampus brain area.

Cellular Automata-based Improved Ant Colony-based Optimization Algorithm for mitigating DDoS attacks in VANETs

In Vehicular Ad hoc NETworks (VANETs), reliable data dissemination between vehicular nodes necessitate maximum degree of collaboration as they play a significant role for ensuring the core objective of communication. But the malicious action of vehicular nodes may disrupt the established degree of co-operation as they lead to poor performance in spite of high resource utilization. The malicious activity of vehicular nodes like DDoS attack must be detected and resolved in a potential way by identifying optimal nodes and optimal paths using cellular automata that modifies the search ability in a global way for increasing the speed of convergence. To prevent the search from falling into local optimum point and to enhance the convergence speed, global searching potential, an enhanced version of ant colony optimization (ACO) algorithm called Cellular Automata-based Improved Ant Colony-based Optimization Algorithm (CA-IACOA) is propounded. In CA-IACOA, the update rules and dynamic adaptive adjustment technique in pheromones of the traditional ACO is enhanced to a significant level based three threshold level of tolerance. This enhancement in the adopted pheromones mainly for attaining better quality solution through reliable and dynamic increment of pheromone that considers the past history related to the visited paths of the ant agents. Further significant tradeoff that exists between solving quality and solving efficiency is resolved using updated dynamic evaporation factor strategy that aids in rapid convergence. Furthermore, trusted boundary symmetric mutation strategy is incorporated in CA-IACOA for improving mutation and to strengthen mutation efficiency. The simulation experiments of CA-IACOA infers that they are potential in handling DDoS attacks as they obtain quality solutions in identifying optimal nodes and optimal paths for reliable routing.

Ant-TSL System Algorithm using New Ant Agents with Intensification and Diversification Strategies( Industrial Management)

Ant-TSL System Algorithm using New Ant Agents with Intensification and Diversification Strategies( Industrial Management)

Improving multi-agent manufacturing control system by indirect communication based on ant agents

Manufacturing control systems are facing difficulty on adopting agility in today’s dynamic environment. Much research has been conducted on the use of multi-agent systems to facilitate this transition. In our previous work, a radio frequency identification–enabled multi-agent-based manufacturing control system was introduced for flexible manufacturing systems. In that control system, a direct coordination mechanism coordinates the agents. However, the decision-making response of the control system to disturbances was low and caused the agents to spend more time on processing messages than doing the actual job. Thus, in this article, we propose an indirect coordination mechanism based on ant colony intelligence aiming to improve the self-organization and processing time of the system. The new system utilizes the stigmergy mechanism for cooperation among the agents. Furthermore, ant agents are created by the existing agents to enable communication among them. The proposed new mechanism was implemented in a flexible assembly line. The simulation results of a case study considering shop floor problems indicates, for most performance measures, that the radio frequency identification–enabled multi-agent-based manufacturing control system integrated with indirect coordination mechanism performs better in comparison with the radio frequency identification–enabled multi-agent-based manufacturing control system integrated with direct coordination mechanism.