Ant Colony Optimization Technique Research Articles

A Virtual Machine Migration Mechanism Based on Firefly Optimization for Cloud Computing

Background: Cloud computing is one of the prominent technology revolutions around us. It is changing the ways the consumer expends services, changing the ways the organization develop and run applications and is completely reshaping the old business models in multiple industries. Cloud service providers need large-scale data centers for offering cloud resources to users, the electric power consumed by these data centers has become a concrete and prudential concern. Most of the energy is dissipated in these data centers due to under-utilized hosts, which also subsidies to global warming. The broadly adept technology is virtual machine migration in cloud computing, therefore, the main focus is to save energy. Objective: Virtual Machine (VM) migration can reap various objectives like load balancing, ubiquitous computing, power management, fault tolerance, server maintenance, etc. This paper presents an energy-oriented mechanism for VM migration based on firefly optimization that reduces energy consumption and the number of VM migrations to a great extent. Method: A Firefly Optimization (FFO) oriented VM migration mechanism has been proposed, which allocates tasks to the physical machines in cloud data centers. It strives to migrates high loaded VMs from one physical node to another, which induces minimum energy consumption after VM migration. Results: The empirical result shows that the FFO based mechanism, implemented in the CloudSim simulator, performs better in terms of the number of hosts saved up to 13.91% in contrast to the First Fit Decreasing (FFD) algorithm and 8.21% as compared to Ant Colony Optimization (ACO). It reduced energy consumption up to 12.76% as compared to FFD and 7.78% as compared to ACO and, ultimately lesser number of migrations up to 52.49% when compared to FFD and 44.51% as compared to ACO. Conclusion: The proposed scheme performs better in terms of saving hosts, reducing energy consumption, and decreasing the number of migrations in contrast to FFD and ACO techniques. The research paper also presents challenges and issues in cloud computing, VM migration process, VM migration techniques, their comparative review as well.

Design and Tuning of Digital Fractional-Order PID Controller for Permanent Magnet DC Motor

One of the central problems in control theory is related to the design of controllers for the improvement of system performance. The aim of this paper is to design an efficient digital fractional-order proportional-integral-derivative (FO-PID) controller for the speed control of buck converter fed permanent magnet DC (PMDC) motor. Speed control is achieved by a pulse width modulated control. The FO-PID controller parameters (gain and order) are obtained by using the ant colony optimization (ACO) technique. The effectiveness of the proposed control scheme is simulated and verified using MATLAB/Simulink results. The performance comparison shows that the speed control of buck converter fed PMDC motor is improved with proposed FO-PID controller than that of integer-order PID controller.

Assessment of performance metrics for fusion network

The arrangement which does not necessitate any infrastructure for doing discussion among nodes is called MANET. The direction-finding technique, a mixture of ACO (ant colony optimization) and particle swarm optimization (PSO) technique, is designed in the paper. The designed technique is judged against several alternatives of particle swarm optimization technique and ant colony optimization technique, i.e.,EDNR+PSO, AntChain Protocol, Improvised Ant Colony Routing (IACR), and ANTALG. Open-source simulator NS 2.3 is employed for carrying out the simulation. From the outcome, it has been verified that the designed technique is finest in contrast to other alternatives of PSO and ACO.Routing overheads of the designed algorithm are reduced in contrast to other methods.

Optimal Day-Ahead Scheduling for Campus Microgrid by using MILP Approach

In this paper, energy management system (EMS) is proposed for institution which helps in reducing the operational cost with the help of using different distributed generators (DGs) and electric vehicles. For this purpose, a scenario is created in which university is connected with national grid having self-powered PV plant, electric vehicles and a diesel generator. Impact of various distributed generators and optimally scheduled energy storage system (ESS) are analyzed for the university campus which helps in reducing operational cost of energy by using campus load data. The proposed model consist of different distributed generators and their effects are observed in various scenarios. Mix Integer linear programming (MILP) is used to get optimized result and then it is compared with Ant Colony Optimization and Linear programming (LP) techniques. Economic and environmental benefits are also discussed. Operational cost was measured and compared and found the role of ESS by using MILP in minimizing operational cost from $798.560 to $756.3850.

Improvement of Traveling Salesman Problem Solution Using Hybrid Algorithm Based on Best-Worst Ant System and Particle Swarm Optimization

This work presents a novel Best-Worst Ant System (BWAS) based algorithm to settle the Traveling Salesman Problem (TSP). The researchers has been involved in ordinary Ant Colony Optimization (ACO) technique for TSP due to its versatile and easily adaptable nature. However, additional potential improvement in the arrangement way decrease is yet possible in this approach. In this paper BWAS based incorporated arrangement as a high level type of ACO to upgrade the exhibition of the TSP arrangement is proposed. In addition, a novel approach, based on hybrid Particle Swarm Optimization (PSO) and ACO (BWAS) has also been introduced in this work. The presentation measurements of arrangement quality and assembly time have been utilized in this work and proposed algorithm is tried against various standard test sets to examine the upgrade in search capacity. The outcomes for TSP arrangement show that initial trail setup for the best particle can result in shortening the accumulated process of the optimization by a considerable amount. The exhibition of the mathematical test shows the viability of the proposed calculation over regular ACO and PSO-ACO based strategies.

Improvisation of optimization technique and AODV routing protocol in VANET

The vehicular ad-hoc network (VANET) is a dynamic network with various applications in intelligent transportation systems. There are several routing protocols for setting up a path between sender node and receiver node. AODV is a reactive routing protocol which broadcast the route request to all neighbouring node and then those nodes will forward the request to their neighbouring nodes. This process helps in selecting the path from sender to receiver. AODV protocol is the best performing routing protocol when number of nodes for communication is more in number. In this research, AODV and Ant Colony Optimization (ACO) technique is applied and data is transmitted. Ant colony optimization algorithm is applied which takes into the initial coordinates of each node. The path which is established using reactive routing protocol has high chances of congestion in the network and also consume large amount of network bandwidth. This research is based on to establish path from source to destination using multicasting approach and also reduce the chances of congestion in the network. It will generate a path with shortest distance and leads to improve throughput, reduce packet loss and delay when compared with the existing protocol.

CARE-Share: A Cooperative and Adaptive Strategy for Distributed Taxi Ride Sharing

Given the fast growth of on-demand transportation services and ride-sharing platforms, the concept of private vehicle ownership is rapidly declining. Although there are multiple fully-grown ride-sharing systems, they are proprietary and centrally controlled. Facilitating ride-sharing using a localized distributed coordination between the riders and the drivers is in need. However, fully distributed systems deal with a large number of variables and objectives and are often sub-optimal. In this paper, we propose a distributed ride-sharing system with multiple objectives which are often conflicting to each other. Therefore, we model it as a multi-objective optimization problem and solve it using the Ant Colony optimization technique which sports a multi-agent behavior. We critically analyze the spatio-temporal challenges posed by the ride sharing problem and define novel performance metrics to capture the underlying subtlety of the distributed system performance. An in-depth experimentation with recent large-scale single-ride taxi trip data from Chicago shows that our solution can ensure up to 79.65% success rate of ride sharing. We have shown that ride sharing is more successful during non-peak traffic hours due to less contention and a healthy balance in passenger and taxi numbers. Further, it has been observed that ride-sharing always reduces the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">total distance travelled</i> by all the taxis and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">total number of taxis</i> on-road; both of which positively impact road congestion and environment. The results obtained from the experiments are very much comparable to real time behaviour of taxi networks. Finally, a revenue framework is proposed to analyse nuances of the operating environment.

Voltage Profile Improvement of Distribution System via Integration of Distributed Generation Resources

This study attempts to identify the causes and possible solutions for voltage profile issues in the lower land of Nepal, and is specifically focused on Laukahi feeder, a radial distribution system with an approximate length of 65 km and distributed at 11KV system voltage. Currently, the end-users feeding through this feeder are getting extremely poor voltage along with frequent interruptions in the power supply. In this study, a forward/ backward sweep algorithm is used to analyze the load flow of the distribution system, whereas ant colony optimization (ACO) technique is used to identify the best location for the Distributed Generator (DG) penetrations. After completion of this study, it is found that, the branch loss of the feeder can be reduced up to 87.22%, and voltage profile can be improved from 0.828 pu to 0.982 pu by integrating some form of DGs.

Adaptive Management of Multimodal Biometrics—A Deep Learning and Metaheuristic Approach

This paper introduces the framework for adaptive rank-level biometric fusion: a new approach towards personal authentication. In this work, a novel attempt has been made to identify the optimal design parameters and framework of a multibiometric system, where the chosen biometric traits are subjected to rank-level fusion. Optimal fusion parameters depend upon the security level demanded by a particular biometric application. The proposed framework makes use of a metaheuristic approach towards adaptive fusion in the pursuit of achieving optimal fusion results at varying levels of security. Rank-level fusion rules have been employed to provide optimum performance by making use of Ant Colony Optimization technique. The novelty of the reported work also lies in the fact that the proposed design engages three biometric traits simultaneously for the first time in the domain of adaptive fusion, so as to test the efficacy of the system in selecting the optimal set of biometric traits from a given set. Literature reveals the unique biometric characteristics of the fingernail plate, which have been exploited in this work for the rigorous experimentation conducted. Index, middle and ring fingernail plates have been taken into consideration, and deep learning feature-sets of the three nail plates have been extracted using three customized pre-trained models, AlexNet, ResNet-18 and DenseNet-201. The adaptive multimodal performance of the three nail plates has also been checked using the already existing methods of adaptive fusion designed for addressing fusion at the score-level and decision-level. Exhaustive experiments have been conducted on the MATLAB R2019a platform using the Deep Learning Toolbox. When the cost of false acceptance is 1.9, experimental results obtained from the proposed framework give values of the average of the minimum weighted error rate as low as 0.0115, 0.0097 and 0.0101 for the AlexNet, ResNet-18 and DenseNet-201 based experiments respectively. Results demonstrate that the proposed system is capable of computing the optimal parameters for rank-level fusion for varying security levels, thus contributing towards optimal performance accuracy.

Adapting ant colony optimization of compliant structures using elements with linearly varying cross-sections

This work addresses the design of miniature compliant displacement amplifiers. The optimum design of the compliant mechanism is generated through topology optimization of two-node frame elements with linearly varying cross sections using the Ant Colony Optimization technique. First, stiffness matrices that account for the change in the cross-section dimensions are formulated. Then, each element is assigned 5 independent ants that represent its design variables defined as the width and thickness of each of the two peripheral cross-sections in addition to the material density. Three case studies with customized cost functions are furnished; the first maximizes the amplification ratio, the second maximizes the output displacement, while the third maximizes both amplification ratio and output displacement simultaneously. The resulting micro-compliant amplifiers are more compact in volume and surpass their constant cross-section counterparts in terms of amplification ratio and output displacement while keeping relatively low internal stresses. The performances of all optimized topologies are verified through ANSYS.

A hybrid multi-faceted task scheduling algorithm for cloud computing environment

Cloud computing has now become the most effective platform for providing elastic and on-demand provisioning of high performance heterogeneous and homogeneous computing services basis on pay-per-use in the field high performance computing world. Task scheduling in cloud computing devotes researchers' attention to provide the optimal solution to this NP-Complete problem. An optimized task scheduling algorithm optimizes the cloud system's performance and generates the maximum profit for the cloud service provider. To overcome this issue in cloud computing, Authors developed a hybrid multi-faceted task scheduling algorithm in this research work. The proposed algorithm exploited the features of standard particle swarm optimization (PSO) and Ant Colony Optimization (ACO) technique. The PSO technique provides the best global optimal solution, whereas ACO offers the best local solution. To validate the results of the developed algorithm, performed a comparison of the makespan, cost, and resource utilization rate parameters against the well-known exiting four algorithms for the computer-generated tasks set in the cloud environment through a simulation experiment. The comparison results showed that the proposed algorithm reduces the makespan time and computation cost as well as increases resource utilization rate.

An energy-efficient cluster-based routing protocol using unequal clustering and improved ACO techniques for WSNs

Routing and hotspot are considered among the key issues in Wireless Sensor Networks (WSNs). Several routing protocols have been proposed; yet they suffer from the lack of fault tolerance, the uneven load balancing and the local optimal solution issues. To fix these issues, we propose in this paper an algorithm named Energy-efficient Cluster-based Routing Protocol using Unequal Clustering and improved Ant Colony Optimization (ACO) techniques (ECRP-UCA). ECRP-UCA divides the network into unequal clusters based on residual energy, distance to the sink, number of neighbor nodes, and a new parameter named number of backward relay nodes in previous round to properly balance the load among Cluster Heads (CHs). We propose also a batch-based clustering method that allows the network to function several rounds without requiring control overhead for its configuration. Additionally, we devise an improved ACO based routing technique for efficient and reliable inter-cluster routing from CHs to the sink. In this algorithm, the heuristic function is formulated considering the energy of next hop sensor node, distance from the current sensor node to the next sensor node and the latter to the destination, and the new parameter Number of Preferred Probable Relay Nodes (NPPRN). By using NPPRN, the ants have the probability to follow other paths, which improves the algorithm’s ability to obtain the optimal global solution and protect the algorithm from converging quickly into the optimal local solution. Moreover, the ant searching direction is improved. The proposed routing protocol is intensively experimented and compared with recent and relevant existing protocols. The simulation results show that the proposed ECRP-UCA outperforms these protocols in terms of various interesting metrics.

Application of Ant Colony Optimization Technique in Economic Load Dispatch of IEEE-26 Bus System with Valve Point Loading

Application of Ant Colony Optimization Technique in Economic Load Dispatch of IEEE-26 Bus System with Valve Point Loading

Enhanced Bee Colony Approach for reducing the energy consumption during VM migration in cloud computing environment

To achieve virtualization in a cloud environment, resource utilization and energy need to be handled carefully. For this one should have to manage the workload, by distributing the load equally among the node. So that, the resources should be distributed equally among the cloud user and access data anytime from anywhere with minimum energy. In this paper, an enhanced Artificial Bee Colony (E-ABC) approach is presented to minimize overall energy consumption with minimum number of migrations. E-ABC approach migrates the VM from the overloaded host to underloaded hosts and hence save energy. The enhancement of the proposed work is exhibited by showing comparison with the Enhanced Cuckoo Search (E-CS) approach and Ant Colony Optimization technique using MATLAB simulator. Enhancement in the reduction of energy consumption of about 15.45 %, and 17.03 % is observed against E-CS, and existing work.

Optimization of Piezoelectric Sensor-Actuator for Plate Vibration Control Using Evolutionary Computation: Modeling, Simulation and Experimentation

The development of lightweight, stronger, and more flexible structures has received the utmost interest from many researchers. For this reason, piezoelectric materials, with their inherent electromechanical coupling, have been widely incorporated in the development of such structures to attenuate their vibrations. However, one of the main challenges is to find the optimal control and sensor-actuator placement. This paper presents an active vibration control for flexible structures, whereby a simply supported plate is taken as the benchmark model. A feedback controller with a collocated sensor-actuator configuration is used. Both disturbance and control signal acting on the plate is created by using piezoelectric (PZT) patches. The analytical model is derived based on the Euler-Bernoulli model. The Optimal location of the collocated sensor-actuator, as well as PID controller gains, are determined using Ant Colony Optimization (ACO) technique, then compared with the Genetic Algorithm (GA) and enumerative method (EM). Optimization in this paper is based on minimizing frequency average energy. The optimal performance value of piezoelectric patch sensor-actuator position and PID controller gains are verified experimentally. It was found that PID controller gains and collocated sensor-actuator location optimizations using ACO, GA and enumerative methods give similar results, which implies the effectiveness of ACO as an optimization technique. More than 20 % of attenuation achieved using the available hardware setup.

A Novel Routing Protocol for Realistic Traffic Network Scenarios in VANET

The vehicular ad hoc network (VANET) has traditional routing protocols that evolved from mobile ad hoc networks (MANET). The standard routing protocols of VANET are geocast, topology, broadcast, geographic, and cluster‐based routing protocols. They have their limitations and are not suitable for all types of VANET traffic scenarios. Hence, metaheuristics algorithms like evolutionary, trajectory, nature‐inspired, and ancient‐inspired algorithms can be integrated with standard routing algorithms of VANET to achieve optimized routing performance results in desired VANET traffic scenarios. This paper proposes integrating genetic algorithm (GA) in ant colony optimization (ACO) technique (GAACO) for an optimized routing algorithm in three different realistic VANET network traffic scenarios. The paper compares the traditional VANET routing algorithm along with the metaheuristics approaches and also discusses the VANET simulation scenario for experimental purposes. The implementation of the proposed approach is tested on the open‐source network and traffic simulation tools to verify the results. The three different traffic scenarios were deployed on Simulation of Urban Mobility (SUMO) and tested using NS3.2. After comparing them, the results were satisfactory and it is found that the GAACO algorithm has performed better in all three different traffic scenarios. The realistic traffic network scenarios are taken from Dehradun City with four performance metric parameters including the average throughput, packet delivery ratio, end‐to‐end delay, and packet loss in a network. The experimental results conclude that the proposed GAACO algorithm outperforms particle swarm intelligence (PSO), ACO, and Ad‐hoc on Demand Distance Vector Routing (AODV) routing protocols with an average significant value of 1.55%, 1.45%, and 1.23% in three different VANET network scenarios.

Controller Parameters Optimization for Multi-Terminal DC Power System Using Ant Colony Optimization

Voltage source converter based multi – terminal DC grids (VSC – MTDC) are widely used for integration of renewable resources. Control of these grids requires vector control method, which includes inner and outer current controllers, tuning of these controllers is very important. Conventionally used tuning methods consider approximated linear model to tune the proportional – integral (PI) parameters of voltage source converter (VSC) which fails to produce optimum disturbance rejection results. In this research an Ant Colony Optimization (ACO) technique is used to get the optimum parameters of inner and outer power control layers for multi – terminal DC system. ACO is applied simultaneously on inner and outer control layers and results are compared with classical tuning method and well established meta - herustic technique, Particle Swarm Optimization (PSO) using MATLAB software. Dynamic models of VSC – MTDC are developed in PSCAD/EMTDC to exalt the qualities of the presented tuning method. In this paper, a four terminal VSC – MTDC has been used under different sequence of events of disturbances e.g. load change on AC side, active power reference change for inverter and rectifier mode and disconnection of one terminal to evaluate the robustness of ACO algorithm with respect to classical method. The proposed tuning method gives superior results under different disturbance compared to conventional method.

Electronically Tunable ACO Based Fuzzy FOPID Controller for Effective Speed Control of Electric Vehicle

The phenomenal growth of the Electric Vehicle (EV) technology demands efficient and intelligent control strategies for the propulsion system. In this work, a novel fuzzy fractional order PID (FOPID) controller using Ant Colony Optimization (ACO) algorithm has been proposed to control EV speed effectively. The controller parameters and the fuzzy logic controller's membership functions are tuned and updated in real-time using the multi-objective ACO technique. The proposed controller's speed tracking performance is verified using the new European driving cycle (NEDC) test in the MATLAB-Simulink platform. The proposed controller outperforms the ACO-based fuzzy integer-order PID (IOPID), FOPID, and traditional IOPID controllers. The sensitivity analysis confirms the robustness of the proposed controller for varying parameters of the EV model. The stabilization of EV speed in the presence of external disturbance is also confirmed. In the proposed work, an attempt is made to analyze the system's stability using Matignon's theorem, considering the linearized EV model. The proposed controller gives optimum speed tracking performance compared to the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) based fuzzy FOPID controllers. Additionally, the optimized fuzzy FOPID controller is realized using a second-generation current conveyor with extra inputs (EX-CCII) and fractional-order capacitors with electronic tunability. The controller circuit's performance evaluation is carried out in the Cadence Analog Design Environment using GPDK 180 nm CMOS process.

An Improved Collaborative Pruning Using Ant Colony Optimization and Pessimistic Technique of C5.0 Decision Tree Algorithm

An Improved Collaborative Pruning Using Ant Colony Optimization and Pessimistic Technique of C5.0 Decision Tree Algorithm

Priority Based Train Scheduling Method Using ACO in Indian Railway Perspective

The aim of this paper is to solve a train scheduling problem (TSP) with different train priorities and travelling at different speeds in the Indian railways (IR) perspective. Initially, a mathematical model for a TSP is developed and then the Ant Colony Optimization (ACO) technique is applied to resolve the problem. A real-world train timetable problem is presented to make a comparison between the manual solutions method used by the Indian Railway (IR) and the solution obtained from ACO and CPLEX solver to check for quality and accuracy in terms of total travel time. The result shows that there is a 6.827% improvement in travel time using ACO over manual solution. A collision free train schedule is determined based on the obtained train sequence.