Ant Intelligence Research Articles

A Routing Algorithm for Underwater Acoustic– Optical Hybrid Wireless Sensor Networks Based on Intelligent Ant Colony Optimization and Energy-Flexible Global Optimal Path Selection

A Routing Algorithm for Underwater Acoustic– Optical Hybrid Wireless Sensor Networks Based on Intelligent Ant Colony Optimization and Energy-Flexible Global Optimal Path Selection

An intelligent deep network for dental medical image processing system

Nowadays, many people are affected by oral health issues because of continuous changes in lifestyle such as personal speech that is affected by crooked teeth and malocclusion teeth. Moreover, a dental problem can cause bacterial infections, cavities, and many other diseases due to an improper lifestyle. In this research, a novel Intelligent Ant Lion-based Convolution Neural Model (IALCNM) is designed for segmenting affected parts in teeth and to classify the wear and periodontitis diseases from the collected dataset. Moreover, the developed technique is implemented in the python 3.8 environment and the attained results of the developed procedure are related to other existing techniques implemented for different diseases in standings of accuracy, precision, error rate, execution time, and so on. Hence the outcome indicates that the current research technique applied to self-created datasets has enhanced the accuracy of segmenting affected parts and disease prediction more than other techniques.

A Multi-Objective Optimization for Supply Chain Management using Artificial Intelligence (AI)

Supply chain management seeks to solve the complex problems of transporting goods from the suppliers to the end customers. Improving the differentiation between different paths to reduce costs and time may require smart systems. This paper proposes two new algorithms for determining, with Multi-Objective Optimization, the least cost and the most appropriate path between two nodes. First: Ant colony optimization (ACO) algorithm, working alongside with Multi Objective Optimization (MOO), is adopted to determine the shortest path and time between two nodes to reach with the least cost. Multi-Objective intelligent Ant Colony (MOIAC) algorithm improves supply chain management to achieve the optimal and the most appropriate solutions. Second: Particle Swarm Optimization (PSO) algorithm, also working alongside MOO, is adopted to determine the least cost, time, and shortest path. Multi Optimization Intelligent Particle Swarm (MOIPS) algorithm improves supply chain management by determining the shortest path with the least cost. These two proposed algorithms seek the optimal solution by MOO using a JAVA Program. The experimental results show the excellence of the first algorithm in determining the optimal and the most appropriate path while getting throw risks inherent in transporting goods. It also demonstrates excellence in transporting goods in the shortest possible time and with the least cost. The second algorithm also shows excellence in transporting goods with the least possible cost via the shortest path and in the shortest time.

Liquid temperature prediction in bubbly flow using ant colony optimization algorithm in the fuzzy inference system as a trainer

In the current research paper a novel hybrid model combining first-principle and artificial intelligence (AI) was developed for simulation of a chemical reactor. We study a 2-dimensional reactor with heating sources inside it by using computational fluid dynamics (CFD). The type of considered reactor is bubble column reactor (BCR) in which a two-phase system is created. Results from CFD were analyzed in two different stages. The first stage, which is the learning stage, takes advantage of the swarm intelligence of the ant colony. The second stage results from the first stage, and in this stage, the predictions are according to the previous stage. This stage is related to the fuzzy logic system, and the ant colony optimization learning framework is build-up this part of the model. Ants movements or swarm intelligence of ants lead to the optimization of physical, chemical, or any kind of processes in nature. From point to point optimization, we can access a kind of group optimization, meaning that a group of data is studied and optimized. In the current study, the swarm intelligence of ants was used to learn the data from CFD in different parts of the BCR. The learning was also used to map the input and output data and find out the complex connection between the parameters. The results from mapping the input and output data show the full learning framework. By using the AI framework, the learning process was transferred into the fuzzy logic process through membership function specifications; therefore, the fuzzy logic system could predict a group of data. The results from the swarm intelligence of ants and fuzzy logic suitably adapt to CFD results. Also, the ant colony optimization fuzzy inference system (ACOFIS) model is employed to predict the temperature distribution in the reactor based on the CFD results. The results indicated that instead of solving Navier–Stokes equations and complex solving procedures, the swarm intelligence could be used to predict a process. For better comparisons and assessment of the ACOFIS model, this model is compared with the genetic algorithm fuzzy inference system (GAFIS) and Particle swarm optimization fuzzy inference system (PSOFIS) method with regards to model accuracy, pattern recognition, and prediction capability. All models are at a similar level of accuracy and prediction ability, and the prediction time for all models is less than one second. The results show that the model’s accuracy with low computational learning time can be achieved with the high number of CIR (0.5) when the number of inputs ≥ 4. However, this finding is vice versa, when the number of inputs < 4. In this case, the CIR number should be 0.2 to achieve the best accuracy of the model. This finding could also highlight the importance of sensitivity analysis of tuning parameters to achieve an accurate model with a cost-effective computational run.

Canny edge detector improvement using an intelligent ants routing

Canny operator provides simple technique to extract the useful edge information from image. However, it suffers from two problems: a difficulty to choose the thresholds values and the presence of broken edges. This paper proposes to improve Canny detector in two steps: First, adaptive Otsu threshold is used to select appropriate thresholds. Then, a new variant of ant colony optimization (ACO) algorithm is used to recover missing edges. From binary image, extracted edge endpoints are used as starting pixels for an intelligent ants routing mechanism. Ants are directed to suitable contour areas and missing edges are retraced via the pheromone traces. Proposed improvements are evaluated with entropy and kappa methods. Experimental results are good and assert approach’s ability to recover most of the broken edges, particularly in noisy clinical images.

Load Balancing Mechanism in Agent-based Grid

Grid is a computing extensive computational environment that makes the enormous processing power for distributed processing. Grid resource management is important and for agent-based approach is a way to manage its resources. Load balancing is an important section resource management system. Paper model of multilevel load balancing in grid and ARMS environments provided, resource management model provided that the estimate is load node. In the First level of model, using genetic algorithm is used for load balancing nodes between internal resources. That is, in the second level, Add method of the neighborhood and overloaded nodes are added to the rainfall distribution in the limited area around the player. The third level is the ecosystem is one of the intelligent ants, each method is a method of load balancing. Picking methods In a multilevel structure is called MLBM that liminates the disadvantages and satisfies the majority of the load balancing properties. The results show that the model is superior to its predecessors gain in increasing productivity and reduction srbarartbaty.

SiCaL: a swarm inspired congestion aware probabilistically load balance routing in MANETs

The major cause of network congestion is the irrational allocation of network resources. The solution is to make more effective use of the network resources by adjusting the traffic probabilistically. In this paper, we propose SiCaL, an algorithm for routing in MANETs. The algorithm is based on nature inspired ant colony optimisation framework. Using SiCal, the algorithm will identify the congestion areas among nodes to the destination to avoid the congestion in the intermediate links and also minimise the packet loss in the network. In this scheme, we develop a mathematical model considering the swarm-based ant intelligence, which provides an efficient congestion control routing mechanism. With a set of simulation experiments, we compare SiCal with AntHocNet, a reference algorithm in this research area. We show that our algorithm SiCal can better perform AntHocNet over a broad range of possible network scenarios, like packet delivery ratio, and throughput.

An Improved LEACH Multi-hop Routing Protocol Based on Intelligent Ant Colony Algorithm for Wireless Sensor Networks

In view of limited node energy, how to reduce energy consumption and improve the network lifetime is an important issue in wireless sensor networks. In this paper, by integrating the theory of intelligent ant colony algorithm with LEACH, we proposed an improving routing algorithm LEACH-IACA (LEACHIntelligent Ant Colony Algorithm) based on multi-hop routing protocol. The core idea is to evaluate cluster heads’ current residual energy and location information from the perspective of overall network in real-time, and single ant traverses all nodes in one time, forming a dendritic multi-hop path. While the path is rebuilt, low-energy nodes select energy-saving path, high energy nodes increase energy consumption as consideration to prolong the network lifetime. The simulation shows that the new method is superior to tranditional LEACH routing algorithm, since it effectively improves the network lifetime, reduces average energy consumption.

An Improved Intelligent Ant Colony Algorithm for the Reliability Optimization Problem in Cyber-Physical Systems

In this paper the torsion bar optimization problem of reliability is considered. Since this problem is a difficult optimization problem, an improved intelligent ant colony algorithm is proposed to solve the problem. This algorithm comprises five stages. First stage is the initialization of pheromone and the sensor node configuration. The second stage is to select the next sensor node. The third stage is to update the pheromone of the sensor node path. The fourth stage is to acquire the best path by computing the shortest distance. The last stage is to output the global optimal solution. To evaluate performance of the proposed algorithm, it is compared with the ant colony optimization algorithm and the genetic algorithm. The experimental results show that the proposed algorithm performs better than them.

Swarm Intelligence Based Self-organizing QoS Framework for Ever-changing Future Networks

We consider a new QoS framework for a wide range of multimedia services in ever-changing networks where traffic is dynamic and network topologies frequently change. In this paper, for such networks, we propose a new self-organizing QoS framework called AntQoS, which is inspired by recent works on ant colony optimization. Compared with previous QoS frameworks, AntQoS provides two quite promising capabilities: 1) the self-organized network QoS (not pre-defined QoS), which autonomously reconfigures itself by promptly adapting to changing network environments such as sudden arrivals of highly bursty traffic, and 2) the self-organized network controls, which autonomously resolve the network congestion and intrinsically resist the multiple network failures. These two promising capabilities enable the proposed AntQoS framework to efficiently and reliably support a wide range of multimedia services with various quality demands. Especially, these capabilities are provided by making use of swarm intelligence of artificial ants without any supervised control. For this purpose, AntQoS employs one single artificial ant colony on an ever-changing network; a number of artificial ants in the employed colony explore network and measure or gather the status information about the networks. Based on the gathered information, AntQoS organizes and maintains a small number of virtual sub colonies named QoS colonies. The QoS colony is an intelligent virtual colony to be capable of searching the path which guarantees the given quality demands of flows. In addition, it is autonomously generated, maintained, and deleted for promptly adapting to the ever-changing network status. Simulation results demonstrate that AntQoS successfully supports various multimedia services with diverse delay requirements while increasing the network throughput by approximately 20% compared to the well-known IntServ frameworks. Simulation results also show that AntQoS autonomously redistributes the congested traffic and resists the unexpected network failures.

Hybrid Ant Algorithm Based Query Processing with Multiagents in Sensor Networks

The wireless sensor networks are usually deployed in various application-specific contexts, which can be treated as distributed databases with big data. The event-involved query responses can be obtained by issuing query requests to this kind of database. However, the constraints of the energy and delay have had a great impact on the operation of wireless sensor networks. How to design the query-involved network model and the corresponding query processing algorithms is extremely challenging. This work investigates query processing problem in resource-constrained wireless sensor networks with the two-tier architecture and multiple query agents, where the multiple nodes of query agents are configured in the networks and the corresponding source cluster-heads send collected events to only one optimum query agent. To reduce the energy consumption and shorten the delivery delay, an efficient query processing algorithm inspired by the swarm intelligence of ants is proposed, which takes advantage of the beneficial clustering and routing emerging in a hybrid self-organized way from the positive interaction of ants. The experimental results demonstrated that the proposed algorithm can deliver collected events to the optimum query agents efficiently. Not only is the energy cost reduced but also the delivery delay is shortened significantly when transmitting the named events to the appropriate query agents.

Ant intelligence inspired blind data detection for ultra-wideband radar sensors

Given the computational complexity and sophisticated implementation of traditionally parametric channel estimators, it has been gradually recognized that the existing data detection methodologies based on the finite impulse response (FIR) propagation channel modeling may become infeasible for ultra-wideband (UWB) radar sensors, especially in some large-scale distributed scenarios. By exploiting the implicit information involved in the received signals, in this investigation, we present a non-parametric UWB data detection scheme for the distributed radar sensor networks. A novel characteristic representation is suggested first. From a pattern classification point of view, a group of quantitative features are then extracted by making full use of the inherent property of UWB propagations. Thus, UWB data detection is formulated as a pattern classification problem in a multidimensional feature space. By thoroughly utilizing the self-similarity of the representative patterns, the ant swarm intelligence inspired clustering algorithm, with the new designed ant movement strategy, is adopted to perform unsupervised data detections. The developed scheme is independent of any a priori modeling information, which essentially avoids the expensive parametric estimators and thus enables practically feasible realizations. To alleviate the computational burden, the principle component analysis (PCA) is further employed to compress the feature space. The simulation results validate the new algorithm, which is superior to the other popular non-parametric data analysis schemes.

Performance Evaluation of Intelligent Ant sense and AODV Routing Protocols for Personal Area Network in Different Motilities

Mobile ad hoc networks (MANET) have emerged as an important field in the wireless area. Due to its dynamic nature, a primary challenge of MANET is the design of effective routing algorithm that can adapt its behavior to frequent and rapid changes in the network. This paper presents a novel scheme for MANET routing enhancement called Intelligent Ant sense (INTANTSENSE) protocol based on ant colony optimization. It utilizes a collection of mobile agents to perform optimal routing activities. By combining the capability of reactive routing with distributed and multipath routing mechanism, Intelligent Ant Sense routing compared with AODV protocol using network simulator-2 (NS-2).INTANTSENSE protocol utilizing the benefits of pheromones, third party reply model and ring search model, to enable optimal path routing, fast route discovery and effective Sense routing route failure handling.

Layout and size optimization of sanitary sewer network using intelligent ants

The incremental solution building capability of ant algorithm is exploited in this paper for the efficient layout and pipe size optimization of sanitary sewer network. Layout and pipe size optimization of sewer networks requires that the pipe locations, pipe diameters and pipe slopes are optimally determined. This problem is a highly constrained Mixed-Integer Nonlinear Programming (MINLP) problem presenting a challenge even to the modern heuristic search methods. In this paper, the Ant Colony Optimization Algorithm (ACOA) is equipped with a Tree Growing Algorithm (TGA) to efficiently solve the sewer network layout and size optimization problem and its performance is compared with the conventional application of the ACOA. The method is based on the assumption that a base layout including all possible links of the network is available. The TGA is used to construct feasible tree-like layouts out of the base layout defined for the sewer network, while the ACOA is used to optimally determine the pipe diameters of the constructed layout. An assumption of sewer flow at maximum allowable relative depth is made allowing for the calculation of the optimal pipe slopes in the absence of any pump and drop in the network. Two different formulations are, therefore, proposed and their performances are tested against hypothetical problems. In the first formulation, ACOA is used in a conventional manner for pipe size optimization while an ad hoc engineering concept for the layout determination. In the second formulation, however, ACOA equipped with TGA is used to simultaneously determine both the layout and pipe sizes of the network. Proposed formulations are used to solve three hypothetical test examples of different scales and the results are presented and compared. The results indicate the ability of the proposed method to optimally solve the problem of layout and size determination of sewer networks.

Ant colony based self-adaptive energy saving routing for energy efficient Internet

According to recent research, the current Internet wastes energy due to an un-optimized network design, which does not consider the energy consumption of network elements such as routers and switches. Looking toward energy saving networks, a generalized problem called the energy consumption minimized network (EMN) had been proposed. However, due to the NP-completeness of this problem, it requires a considerable amount of time to obtain the solution, making it practically intractable for large-scale networks.In this paper, we re-formulate the NP-complete EMN problem into a simpler one using a newly defined concept called ‘traffic centrality’. We then propose a new ant colony-based self-adaptive energy saving routing scheme, referred to as A-ESR, which exploits the ant colony optimization (ACO) method to make the Internet more energy efficient. The proposed A-ESR algorithm heuristically solves the re-formulated problem without any supervised control by allowing the incoming flows to be autonomously aggregated on specific heavily-loaded links and switching off the other lightly-loaded links. Additionally, the A-ESR algorithm adjusts the energy consumption by tuning the aggregation parameter β, which can dramatically reduce the energy consumption during nighttime hours (at the expense of tolerable network delay performance). Another promising capability of this algorithm is that it provides a high degree of self-organizing capabilities due to the amazing advantages of the swarm intelligence of artificial ants. The simulation results in real IP networks show that the proposed A-ESR algorithm performs better than previous algorithms in terms of its energy efficiency. The results also show that this efficiency can be adjusted by tuning β.

Chemical Recruitment for Foraging in Ants (Formicidae) and Termites (Isoptera): A Revealing Comparison

All termites secrete trail pheromones from their sternal gland, whereas ants use a variety of glands for this purpose. This and the diversity of chemical compounds that serve as trail pheromones among ants, and the uniformity of chemicals among termite trails, suggest a different evolutionary historical dynamics for the development of chemical mass recruitment in both taxa. Termites in addition show pheromonal parsimony. This suggest a single evolutionary origin of pheromone trails in Isoptera, whereas chemical mass recruitment among Formicidae seems to have evolved many times and in different ways. Despite these very different evolutionary histories, both taxa evolved chemical recruitment systems involving attractants and orientation signals, and at least two divergent decision making system for recruitment. This evolutionary analogy suggests that chemical mass recruitment is constraint by fundamental physical dynamic laws. Artificial intelligence including “mass intelligence” and “ant intelligence”, emulates mass recruitment in interacting virtual agents in search of optimal solutions. This approach, however, has copied only the “Democratic” recruitment dynamics with a single compound pheromone. Ant and termite evolution shows more sophisticated recruitment dynamics which, if understood properly, will improve our understanding of nature and applications of artificial “swarm intelligence”.

AntP2PR: An ant intelligence inspired routing scheme for Peer-to-Peer networks

Unstructured Peer-to-Peer networks consist of an infrastructure-less overlay on top of another network. Most of them use distributed algorithms for all operations, such as resource discovery or connectivity control. Research has shown that a considerable amount of the generated traffic is due to signaling messages. Furthermore, another challenge when implementing a Peer-to-Peer network is avoiding free riders, i.e. users trying to profit from the network without sharing their resources. In this paper a new approach to routing packets in such networks is presented using ant intelligence. Success messages are used as agents and the biological procedure of pheromone trails is used for forwarding new packets used in resource discovery. These agents carry an amount of pheromone which will be added to a pheromone table representing routes to other peers. This approach enables the network to adjust to the dynamic nature of Peer-to-Peer networks where new nodes connect and disconnect continuously. Peers that are free riding will be ultimately isolated from the rest of the network by limiting the number of messages directed to them. The authors have simulated an unstructured Peer-to-Peer network, such as Gnutella, that uses this method and the results are very promising. The amount of traffic used solely for resource discovery is greatly reduced enabling the users to use more bandwidth for transferring content.

Ant intelligence for solving optimal path‐covering problems with multi‐objectives

Conventional methods have difficulties in forming optimal paths when raster data are used and multi‐objectives are involved. This paper presents a new method of using ant colony optimization (ACO) for solving optimal path‐covering problems on unstructured raster surfaces. The novelty of this proposed ACO includes the incorporation of a couple of distinct features which are not present in classical ACO. A new component, the direction function, is used to represent the ‘visibility’ in the path exploration. This function is to guide an ant walking toward the final destination more efficiently. Moreover, a utility function is proposed to reflect the multi‐objectives in planning applications. Experiments have shown that classical ACO cannot be used to solve this type of path optimization problems. The proposed ACO model can generate near optimal solutions by using hypothetical data in which the optimal solutions are known. This model can also find the near optimal solutions for the real data set with a good convergence rate. It can yield much higher utility values compared with other common conventional models.

Genetic Programming Method of Evolving the Robotic Soccer Player Strategies with Ant Intelligence

This paper presents the evolved soccer player strategies with ant-intelligence through genetic programming. To evolve the code for players we used the Evolutionary Computation tool (ECJ simulator-Evolutionary Compuation in Java). We tested the evolved player strategies with already existing teams in soccerbots of teambots. This paper presents brief information regarding learning methods and ant behaviors. Experimental results depicts the performance of the evolved player strategies.

Ant-Based Reliable Multi-Constrained Anycast Routing for Sensor Networks

Wireless sensor networks (WSNs) are task-specific where the tasks are specified by the users or applications. Energy and delay constraints have had a great impact on the design and operation of the WSNs. As routing plays an important role in data-centric WSNs, there are numerous energy aware routing protocols proposed for WSNs with single sink, generally using multi-hop paths to use the energy more efficiently. But most of them do not consider the communication delay carefully. As many applications of WSNs require real-time communication, the end-to-end delivery delay is another point to be considered, especially for urgent events. When considering the multiple QoS constraints such as the delivery delay and the minimal energy required, the multi-constrained routing is NP-hard. Moreover, WSNs also suffer from a scalability problem as the total number of nodes can reach hundreds or thousands. Thus, sensors located near the sink consume large amounts of energy. The sink is then isolated once they deplete their batteries, and the network fails to function. To cope with the scalability problem, multi-sink configuration is adopted for large-scale WSNs. How to select the optimum sink for packets with different delay and energy constraints in large-scale WSNs is the main problem to be resolved in this paper, which is called the multi-constrained anycast (MCA) problem. Obviously, it is NP-hard. We investigate the MCA problem in resource-constrained WSNs with multiple sinks and two-tier architecture, where the source cluster-heads send collected data to only one optimum sink. To reduce the energy consumption and shorten the delivery delay, a distributed anycast algorithm ARMCA inspired by swarm intelligence of ants is proposed, which can also achieve reliable data gathering during the deployment period. The scheme takes advantage of beneficial clustering and routing emerging in a self-organized way from the positive interaction of ants. The idea of the ACO-based routing is adopted to achieve the good performance of energy-efficiency and delivery delay. Meanwhile, the ant-based clustering is utilized to ensure the reliability. By the combination of the ant-based routing and clustering models, the ARMCA protocol can solve the MCA problem efficiently and flexibly. The simulation results demonstrated that the proposed algorithm can reliably deliver collected events to the optimum sinks efficiently, and has self-recovery capabilities to deal with failures of sensors including the sinks. Not only is the energy cost reduced, but also the delivery delay is shortened significantly when transmitting packets of the named events to the sinks.