Ant Colony Technique Research Articles

Ant with Artificial Bee Colony Techniques in Vehicular Ad-hoc Networks

A VANET faces many problems due to dynamic changing of networks with certain requirements such as low delay, high (PDR) packet delivery ratio, low routing overhead and throughput. However, numerous routing protocols have been suggested to meet the demands of Quality of Service (QoS), but none of them can consistently maintain the highest level of QoS simultaneously. The proposed method Ant with Artificial Bee Colony Techniques provides better performance when compared to the existing techniques. This work is compared with latest developed Techniques in VANET to find the best path and different performance metrics are used to check the performance. This work premeditated the comparative analysis of Quality of services made by the performance of latest emerging techniques in VANET and will provide the best solution for the recognition problem in finding the best path based on the evaluation of performance of Quality of Service. Simulation results imply the benefits of the proposed Ant with Artificial Bee Colony Techniques (AABC) produces better result when compare to the other conventional method and Ant Colony Techniques(ACT)in terms of high packet delivery ratio, less end-to-end delay and less energy consumption level. The performance is evaluated by using Ns2 simulator and results shows that the AABC successfully achieve the optimal routes.

To Use an Ant Colony Technique to Solve a Crispy Type Bi- and Tri-Objective Transportation Problem

Transportation problem (TP) aims to reduce the entire transportation cost of moving resources from various supply hubs to various demand hubs. However, in real-life situations, all organizations want to achieve numerous objectives while making transportation of goods. The degree of deterioration may vary depending on the mode, route, and time of transport. In some cases, the multiobjective could be used to reduce the use of a scarce resource, such as energy. As a result, the proposed approach was discovered to be an algorithm that improves bi- and triobjective TP techniques. This is an innovative way for solving the new bi- and triobjective transportation algorithm using a modified ant colony optimization (ACO) algorithm. According to the literature, various strategies have been developed in the past to tackle the multiobjective transportation problem (MOTP). The MOTP is solved using goal programming, fuzzy programming, interactive solution algorithms, and other techniques. These strategies are occasionally good or bad in achieving better results in a reasonable amount of time. The heuristic technique used in this work is the improved ACO algorithm, which is based on the ant colony algorithm and has been found to provide solutions with a reasonable degree of satisfaction for two and three objective TPs. When the findings are compared, the solution achieved using the proposed method has delivered the best performance and provides a case study to show the new strategy.

Intelligent energy-efficient scheduling with ant colony techniques for heterogeneous edge computing

Energy efficiency is a significant issue in heterogeneous edge computing systems for a large number of latency-sensitive applications. This article presents an efficient technique to minimize energy overhead of time-constrained applications modeled by DAGs in heterogeneous edge computing. The technique is divided into three stages. First, we design a new method to compute task priority and propose the ant-colony based energy-aware scheduling algorithm to get a preliminary scheduling result. Second, taking the slack time between tasks and their deadlines into consideration, we propose the downward proportionally reclaiming slack algorithm to further cut down energy overhead by the DVFS technique. Third, taking the slack time between tasks into consideration, we propose the upward and downward proportionally reclaiming slack algorithm to cut down energy overhead by the DVFS technique again. Simulated results indicate that the presented technique is highly efficient in reducing energy overhead compared with state-of-the-art techniques using benchmarks of distinct characteristics.

RETRACTED ARTICLE: Multi constraints applied energy efficient routing technique based on ant colony optimization used for disaster resilient location detection in mobile ad-hoc network

A mobile ad-hoc network (MANET) is a group of advanced mobile devices which are capable of self-organization. Due to the diverse nature of mobile devices and wireless connectivity, MANET faces several issues like topology management, energy management due to battery power limits, data communication issues etc. The utilization rate of battery powered energy and QoS properties are significant in MANET. In order to address these issues, we propose a new ant colony inspired technique for energy efficient routing in MANET. The proposed technique is a multi-objective constraints applied energy efficient routing technique based on ant colony optimization in mobile adhoc networks (MCER-ACO). The proposed MCER-ACO technique selects the next hop node centered on the constraints, residual energy of mobile node, no of packets in path and dynamic movement of topology. By applying ant colony technique on objectives and constraints, probability of choosing next hop node as forwarding node is determined. From the performance evaluation carried out in this research work, it is proved that the proposed MCER-ACO approach is providing optimal energy efficient routing while comparing with few other existing methods.

Path planning optimization for mechatronic systems with the use of genetic algorithm and ant colony

Path planning optimization of mechatronic systems is a very important field of research that has been growing rapidly in recent years. Coordinate measuring machines (CMM), robotic arms, CNC machines, are often using a big amount of points to control the path planning. Within these efforts, some encouraging results are presented in this work on the optimization of path planning. By integrating ant colony techniques into genetic algorithm, path optimization can be reached up to 50% instead of the simple genetic algorithm. At the same time, the calculation of the optimal solution that makes this technique even more efficient is accelerated. From measurements made in a simulation model, reductions in overall trajectories were recorded up to 40% in cases where the number of points exceeded 500 while the optimal path planning time was reduced by up to 20%. In real-system implementation these values are slightly reduced due to the real-time execution of the movements. This integration makes the proposed technique particularly attractive in cases where are path planning between a large number of points and the calculation time is required to be small.

Ant Colony Technique for Optimizing the Order of Cascaded SVM Classifier for Sunflower Seed Classification

In Seed Testing Laboratory (STL), test for other distinguishable variety (ODV) is carried out for the seed sample received from the seed producer after harvesting. This is done to obtain foundation/certified tag from the seed certification department before marketing the product. This is currently done manually by observing the morphological characteristics of the seeds through the naked eye. This is a time-consuming process for the STL and there is a chance for human error in identifying ODV. Hence, there is the need for the machine vision technique to automate the process of identifying ODV in the seed testing laboratory. Various techniques for varietal identification of sunflower seeds are explored in this paper. Experiments are performed on a dataset that contains ten varieties of sunflower seeds and the success rate achieved using various techniques is reported in this paper. In cascaded support vector machine (SVM), the order in which the classifier blocks are arranged plays an important role in improving the classification rate. The main contribution of this paper lies in the manipulation of ant colony optimization technique for obtaining the order of cascaded SVM by maximizing the total probability of correct decision. When the SVM is cascaded in optimum order, classification rate has been increased from $88.32\%$ (obtained using the actual order) to $98.82\%$ for kernel linear discriminant analysis based boundary descriptors. The closed form expression for computing the total probability of correct detection of the constructed cascaded SVM classifier is also reported in this paper.

An Innovative Approach for Molecular Simulation of Nanostructured Adsorption Isotherms via the Ant Colony Method

The original grand canonical Monte Carlo (GCMC) molecular simulation method heavily relies on pure random steps for its various phases of displacement, removal and insertion. A new algorithm is presented in this Article which employs the global optimization ant colony technique in a state-of-the-art algorithm to predict the adsorption isotherm of any adsorbate inside nanostructured adsorbents. Several experimentally measured isotherms are used from the literature to successfully validate the proposed method. A detailed flowchart is presented to describe the entire algorithm. The Akaike information criterion is used as a part of the proposed method to determine the optimum number of required iterations. It was clearly demonstrated that our in-house ant colony molecular simulation (ACMS) method outperforms the conventional GCMC method for all case studies. The simulation results also indicate that the newly proposed algorithm performs several orders of magnitude faster than GCMC with the same or better accu...

An element search ant colony technique for solving virtual machine placement problem

The data centres in the cloud environment play a key role in providing infrastructure for ubiquitous computing, pervasive computing, mobile computing etc. This computing technique tries to utilize the available resources in order to provide services. Hence maintaining the resource utilization without wastage of power consumption has become a challenging task for the researchers. In this paper we propose the direct guidance ant colony system for effective mapping of virtual machines to the physical machine with maximal resource utilization and minimal power consumption. The proposed algorithm has been compared with the existing ant colony approach which is involved in solving virtual machine placement problem and thus the proposed algorithm proves to provide better result than the existing technique.

OPTIMIZATION AND IMPLEMENTATION OF WIRELESS SENSOR NETWORK PERFORMANCEUSING ANT BEE COLONY

Routing in wireless protocols is a tedious task. A wireless node is never stationary. It is moving from one location to other which makes traditional routing algorithms like OSPF, RIP inefficient in wireless routing. Aim is to produce a routing algorithm which works efficiently with constantly changing locations of the mobile node by implementing traditional biological background based ant bee colony technique. Traditional biological background based ant bee colony is used. A efficient ant bee colony based routing algorithm for Mobile Ad hoc Networks is obtained. Shortcomings in implementation of above algorithm are also discussed.

Minimizing the of shell and tube heat exchangers cost using evolutionary algorithms

Shell and Tube Heat exchangers (STHEs) are the most common type of heat exchangers. They are applied in numerous industrial installations. The minimization of the costs of these heat exchangers is a key goal for both designers and users. The design of this type of heat exchanger involves complex processes, including selection of the geometrical and operating parameters. The traditional design approach of Shell and Tube Heat Exchangers consists of the evaluation of a number of different exchanger geometries to identify those satisfying a requirement of heat and a set of geometric and operational constraints. However, this approach is slow, inaccurate and does not guarantee an optimal solution. In this paper, different evolutionary algorithms to optimize STHEs economically are analyzed and applied, including the particle swarm optimization, genetic algorithms and mixed ant colony optimization, this last one developed by the authors. As objective function the minimization of total annual cost was considered. For optimization were considered as independent design variables the inside diameter of the shell, the outer pipe diameter and spacing between the baffles. They were also considered for optimizing two patterns or arrangements of tubes, in a triangle and a square. An example of three case studies of optimization of heat exchanger is presented. The results of the optimization using the mixed ant colony technique are compared with those obtained using particle swarm and genetic algorithms.

Ant Colony Optimization: A Review and Comparison

Many optmization algorithms are developed over period of time, among these most famous and widely used is Ant Colony systems (ACA). Ant Colony Systems (ACS) are the collection of different ant colony optimization algorithms. Different algorithms are used for solve the Travelling salesmen Problem (TCP) but ant colony algorithm is more preferred to solve the travelling salesmen problem. In ant colony best solution is found with the help of cooperating agents called ants. Ants cooperate with each other and communicate using an indirect way with the help of pheromone which is deposited on the edges on the path. To achieve the finest solution, different ant colony techniques are compared. In this study we compared different Ant Colony Systems (ACS) algorithms through their implementation on classical problem of Travelling salesmen Problem (TCP).

Optimisation of process plans in reconfigurable manufacturing systems using ant colony technique

A reconfigurable manufacturing systems (RMS) is designed around part family products because of its customised flexibility. System scalability and machine adaptability to new products makes RMS preferable in comparison to other manufacturing systems. The modification of RMS system can be done both physically by changing layout, machines and material handling devices and logically by changing route, schedule, planning, etc. This paper aims at finding optimal process plans of a single product flow-line RMS using ant colony optimisation approach consist of two phases. In the first phase, priority-based encoding technique is applied to find feasible operation clusters and the second phase uses ant colony technique for minimising the total cost of the RMS. A case study illustrates the proposed approach by developing a tool box in MATLAB software.

An ant colony optimization technique for solving min–max Multi-Depot Vehicle Routing Problem

The Multi-Depot Vehicle Routing Problem (MDVRP) involves minimizing the total distance traveled by vehicles originating from multiple depots so that the vehicles together visit the specified customer locations (or cities) exactly once. This problem belongs to a class of Nondeterministic Polynomial Hard (NP Hard) problems and has been used in literature as a benchmark for development of optimization schemes. This article deals with a variant of MDVRP, called min–max MDVRP, where the objective is to minimize the tour-length of the vehicle traveling the longest distance in MDVRP. Markedly different from the traditional MDVRP, min–max MDVRP is of specific significance for time-critical applications such as emergency response, where one wants to minimize the time taken to attend any customer. This article presents an extension of an existing ant-colony technique for solving the Single Depot Vehicle Routing Problem (SDVRP) to solve the multiple depots and min–max variants of the problem. First, the article presents the algorithm that solves the min–max version of SDVRP. Then, the article extends the algorithm for min–max MDVRP using an equitable region partitioning approach aimed at assigning customer locations to depots so that MDVRP is reduced to multiple SDVRPs. The proposed method has been implemented in MATLAB for obtaining the solution for the min–max MDVRP with any number of vehicles and customer locations. A comparative study is carried out to evaluate the proposed algorithm's performance with respect to a currently available Linear Programming (LP) based algorithm in literature in terms of the optimality of solution. Based on simulation studies and statistical evaluations, it has been demonstrated that the ant colony optimization technique proposed in this article leads to more optimal results as compared to the existing LP based method.

On the Suitability of Using ANT Colony Optimization for Routing Multimedia Content over Wireless Sensor Networks

This paper studies the suitability of using a meta-heuristic ant colony technique in routing multimedia content over wireless sensor networks. The presented technique is both energy and QoS-aware. Ant colony algorithm is used to find the optimal routing path. Optimality is in the sense of minimizing energy consumption and increasing link quality and reliability. The proposed approach results in minimizing energy consumption and prolonging the lifetime of the network. Moreover, the optimal path has a high link quality and reliability which enhances video frame quality and ensures high probability of successful delivery of video frames. The importance given to energy consumption, link quality, and link reliability metrics can be varied depending on the multimedia application requirements.

A comparison of three population-based optimization techniques for the design of composite sandwich materials

Three population-based optimization techniques — particle swarm optimization, ant colony optimization, and simulated annealing — are applied to the multi-objective design of sandwich materials. The formulation of the algorithms is described, along with metrics for quantifying their performance. For the case study considered of a sandwich beam in bending, it is shown that the ant colony technique out-performs the other two algorithms with respect to identifying optimal geometries, materials, and lay-ups for a stiff sandwich in which low mass and low cost are the design objectives. The particle swarm and simulated annealing algorithms were found to have particular difficulties in handling the optimization of multi-ply oriented fiber-reinforced polymer facing laminates.

Editorial

This year sees an important landmark reached for Strain and the British Society for Strain Measurement, namely 40 years of continuous support for industrial and academic members. The importance of the interchange of research and development information has never been of greater importance, with the rapidly advancing technologies threatening to leave the uninformed behind. The society encourages cooperation at all levels and between widely different branches of engineering. Strain now has an editorial board with members from 12 countries, showing the wide appeal and influence of the journal. Much has been written recently about the developments of the publication of Strain. We now have a very high-quality journal that is read worldwide. This issue of Strain contains two full papers, two technical notes and a tutorial. Two of the papers deal with sensors in a generic fashion. The first by Sodano et al., suggests a novel means of energy harvesting using the piezoelectric effect. At present, the idea of using wireless technology in conjunction with sensors to eliminate problems brought about by ‘wiring’ is gaining much attention. The advantages of such devices are most greatly exploited in a structural health monitoring or machine condition monitoring context. Applications such as embedded sensors in composite structures, inaccessible locations such as in large civil and offshore structures and in particular large rotating machinery such as wind turbines come to mind as suitable candidates for wireless sensors. The wireless sensors can be remotely interrogated with minimal effect on the structure. The only difficulty is powering the sensor. Batteries are bulky, have a finite lifespan and also require wiring; therefore it is desirable to define a means of ‘self-powering’. The paper defines a means of obtaining power by the conversion of mechanical energy from the motion of the structure to electrical energy to power the sensor. A model of the energy that can be produced was developed in the paper and validated using a non-homogeneous cantilever beam. The experimental study showed that the power-harvesting exercise produced damping in the beam where the energy was stored rather than dissipated into the structure. The second paper by Overton and Worden deals with sensor placement to optimise fault detection in a structure. The paper uses an ant colony metaphor to determine optimum sensor placement for the location and classification of impacts on a composite panel using a neural network. The ant colony metaphor is based on the idea of ants foraging for food using the shortest path by tracking a pheromone trail deposited by other ants. The composite panel used in the work simulated an aircraft skin and was instrumented with 17 piezoceramic strain sensors, which were used initially to train the neural network. The ant colony technique was used to optimise the positioning of three sensors and then six sensors. In both cases, the ant colony approach outperformed a genetic algorithm. The work in this paper will be useful in reducing the amount of computational effort used in structural health monitoring, as optimisation reduces the number of sensors necessary for accurate fault location and hence reduces costs associated with online monitoring. The technical note by Guntaka et al. deals with a new recording medium in the full-field analysis area of holographic interferometry. The new material is self-developing and has the potential to facilitate real-time holographic inspections. The technique was validated on a thick PVC pipe subjected to internal pressure. The technical note by Noroozi et al. deals with the use of neural networks in conjunction with photoelastic data. FEA is used to train the neural network and then the technique is applied in an inverse fashion to derive the relative retardation obtained from photoelastic data and hence the value of the applied load. This work provides an important first step in combining FEA and photoelasticity to produce a hybrid technique for stress analysis. The tutorial by Truman will be of value to all stress analysts and deals with the application of tensor elasticity to isotropic bodies. Two well-known problems in plane elasticity are cited as examples and the reader is shown how the solutions for these problems are derived. The tutorial follows on from the first in the series presented in the November 2003 issue of Strain. To conclude, the journal Strain and the Society have offered much information and support through the hard work of many dedicated members and thanks goes to all that have contributed so much over the last 40 years. Some have been active throughout virtually all of that time, which is a tremendous show of dedication and enthusiasm for their chosen field.