Ant-based Algorithm Research Articles

An Efficient Bio-inspired Routing Scheme for Tactical Ad hoc Networks

Ad hoc networks are temporary networks, created mainly for applications that are infrastructure-less. Such networks and network nodes demand special characteristics like mobile nodes having dynamic topology, wireless medium, heterogeneous deployment environment, and reactive or proactive routing depending on the nature of the network which includes network parameters such as node placement, mobility model, number of participants in the network, patterns of mobility, etc. Due to these characteristics and the mobility of network nodes, the process of routing is quite challenging in the ad hoc environment, especially when the node mobility is high. Bio-inspired routing can be an effective solution to meet all the design requirements and deal with the issues of tactical ad hoc networks. Different types of nature-inspired routing mechanisms are possible to use for tactical networks. This paper proposes the design of a novel Ant Colony Optimization-based routing strategy for ad hoc networks. Ant-based algorithms are dynamic and have adaptive behavior. Hence, they are competent for routing in ad hoc networks. Our proposed routing scheme is evaluated based on the network’s performance by varying different parameters. The performance of our proposed ACO-based routing approach is also compared to some existing ad hoc routing mechanisms. Different metrics in different deployment scenarios that can affect the efficiency of our proposed protocol are taken into consideration to evaluate the performance.

A study of ant-based pheromone spaces for generation constructive hyper-heuristics

Research into the applicability of ant-based optimisation techniques for hyper-heuristics is largely limited. This paper expands upon the existing body of research by presenting a novel ant-based generation constructive hyper-heuristic and then investigates how different pheromone maps affect its performance. Previous work has focused on applying ant-based optimisation techniques that work in the solution space directly to the heuristic space and we hypothesise that this may be problematic for the hyper-heuristic’s efficacy. The focus of this analysis is primarily on how the pheromone map, 2D and 3D, of ant-based methods, can be used for this hyper-heuristic task. 2D pheromone maps are the predominant pheromone map type used by ant-based algorithms. Thus the comparison here is between the existing 2D pheromone map and the newly introduced 3D pheromone map. The analysis consists of multiple experiments with algorithms in the TSP and 1DBPP domain which are assessed in terms of optimality and generality. The results of the experiment demonstrate key differences in performance between the two different pheromone spaces. The 3D pheromone map showed better generality and optimality in the 1DBPP domain whereas the 2D pheromone map showed better generality and only marginally better optimality for the TSP domain. The analysis indicated that the different pheromone maps work most optimally for different types of optimisation problems. The hybrid method showed some improvements in generality but showed little improvements in optimality overall.

Hybridizing Ant Colony Optimization Algorithm for Optimizing Edge-Detector Techniques

Ant colony optimization is a swarm intelligent algorithm that mimics the ant behaviors to optimize solutions for hard optimization problems. Over years Ant-based algorithms have been used in solving different problems including: Traveling Salesman Problem (TSP), Wireless Sensors Network (WSN), Benchmark Problem, and it has been used in various image processing applications. In the image processing fields various techniques have been used to detect edges in a digital image such as Canny and Sobel edge detectors. This Study, proposed a hybridized Ant Colony Optimization algorithm for optimizing the edge detector quality. The proposed method initializes its attribute matrix and the information at each pixel routed by ants on the input image. Experimental results show the results of the proposed algorithm and compare the results with the original built-in MATLAB edge detection method called Canny and the results of basic Aco edge detector. All three algorithms tested in different images and the MSE and PNSR are calculated before and after applying Gaussian noise. Based on the Experimental results obtained by the three used methods (Canny Edge Detector, Ant Colony Optimization, and Hybrid Aco-Canny), the proposed Hybrid ACO-CANNY methods was the best method for detecting edges.

An ant approach to define product families and remanufacturing cells

Cellular manufacturing has the potential to reduce the complexity of a manufacturing system, decoupling a complex factory in mini-factories or cells. This concept has an immense potential to be implemented in remanufacturing, which is a promising circular economy strategy which allows to recover the value added to used products. A remanufacturing system is complex, and this creates a barrier to its implementation. This study introduces an ant-based algorithm which addresses the formation of product/part families and remanufacturing cells. A novel approach used in this study mimics ant behaviour by using an artificial pheromone that reinforces similarities between machines and parts, allowing the definition of cells and families with superior performance over Genetic Algorithms (GA) and Bacterial Foraging Optimization Algorithm (BFOA) in the solution of benchmark problems.

Opposition-Inspired synergy in sub-colonies of ants: The case of Focused Ant Solver

Recently, Opposition-Inspired Learning strategies were proposed to improve the search process of ant-based algorithms to solve combinatorial problems. In this paper, we propose a collaborative framework of these strategies called Multiple Opposite Synergic Strategy for Ants (MOSSA). Because of each strategy has a different goal, we expect that the ants algorithm will benefit from their collaboration. The algorithm strongly uses the pheromone matrix for accomplishing stigmergy. To evaluate our framework, we use a recently proposed algorithm to solve Constraint Satisfaction Problems named Focused Ant Solver. Results and statistical analysis show that using MOSSA, Focused Ant Solver is able to solve more problems from the transition phase.

Enhancing Ant-Based Algorithms for Medical Image Edge Detection by Admissible Perturbations of Demicontractive Mappings

The aim of this paper is to show analytically and empirically how ant-based algorithms for medical image edge detection can be enhanced by using an admissible perturbation of demicontractive operators. We thus complement the results reported in a recent paper by the second author and her collaborators, where they used admissible perturbations of demicontractive mappings as test functions. To illustrate this fact, we first consider some typical properties of demicontractive mappings and of their admissible perturbations and then present some appropriate numerical tests to illustrate the improvement brought by the admissible perturbations of demicontractive mappings when they are taken as test functions in ant-based algorithms for medical image edge detection. The edge detection process reported in our study considers both symmetric (Head CT and Brain CT) and asymmetric (Hand X-ray) medical images. The performance of the algorithm was tested visually with various images and empirically with evaluation of parameters.

Solving Rwa Problem in All-optical Networks Based on Optimized Ant Colony Algorithm

The information bandwidth growth can be achieved using optical networking technology. Optical fiber has been used as a physical means through many network technologies. Wavelength Division Multiplexing (WDM) has emerged as a promising technology to conquer the use of large-bandwidth in optical networks. In WDM network, the essential problem is to serve the load traffic represented by the requests that arrive at the network and need to be served. For serve the request, the network will determine a path paired with a specific wavelength used to send the request's data from source to the destination node. This problem is called the routing and wavelength assignment (RWA). In this work, Multi-Node Optimized Ant Colony Algorithm (M-NOACA) proposed and implemented to solve the RWA problem in all-optical networks by simulating traffic with the RWA algorithms. A performance measurements applied to study the optimization problem of the RWA, and apply a comparison with the classic algorithms. Determining the best collection of parameters for an ant-based algorithm to obtain the best performance remains an open issue, in this work, a study is done to introduce a new contribution to obtain the optimal setting of Ant Colony parameters used to solve the RWA problem.

Admissible Perturbation of Demicontractive Operators within Ant Algorithms for Medical Images Edge Detection

Nowadays, demicontractive operators in terms of admissible perturbation are used to solve difficult tasks. The current research uses several demicontractive operators in order to enhance the quality of the edge detection results when using ant-based algorithms. Two new operators are introduced, χ -operator and K H -operator, the latter one is a Krasnoselskij admissible perturbation of a demicontractive operator. In order to test the efficiency of the new operators, a comparison is made with a trigonometric operator. Ant Colony Optimization (ACO) is the solver chosen for the images edge detection problem. Demicontractive operators in terms of admissible perturbation are used during the construction phase of the matrix of ants artificial pheromone, namely the edge information of an image. The conclusions of statistical analysis on the results shows a positive influence of proposed operators for image edge detection of medical images.

An improved ant-based algorithm based on heaps merging and fuzzy c-means for clustering cancer gene expression data

The microarray technology enables the analysis of the gene expression data and the understanding of the important biological processes in an efficient way. We have developed an efficient clustering scheme for microarray gene expression data based on correlation-based feature selection, ant-based clustering, fuzzy c-means algorithm and a novel heaps merging heuristic. The algorithm utilizes the feature selection algorithm to overcome the high-dimensionality problem encountered in bioinformatics domain. Based on extensive empirical analysis on microarray data, clustering quality of the ant-based clustering algorithm is enhanced with the use of fuzzy c-means algorithm and heaps merging heuristic. The performance of the proposed clustering scheme is compared with k-means, PAM algorithm, CLARA, self-organizing map, hierarchical clustering, divisive analysis clustering, self-organizing tree algorithm, hybrid hierarchical clustering, consensus clustering, AntClass algorithm and fuzzy c-means clustering algorithms. The experimental results indicate that the proposed clustering scheme yields better performance in clustering cancer gene expression data.

Quota travelling salesman problem with passengers, incomplete ride and collection time optimization by ant-based algorithms

The Quota Travelling Salesman Problem with Passengers, Incomplete Ride, and Collection Time is a new version of the Quota Travelling Salesman Problem. In this problem, the salesman uses a flexible ridesharing system to minimize travel costs while visiting some vertices to satisfy a pre-established quota. We consider operational constraints regarding vehicle capacity, travel time, passenger limitations, and penalties for rides that do not meet passenger requirements. We present a mathematical formulation and heuristics based on Ant Colony Optimization.

Traffic-Aware VANET Routing for City Environments—A Protocol Based on Ant Colony Optimization

This paper presents a traffic-aware position-based routing protocol for vehicular ad hoc networks (VANETs) suitable for city environments. The protocol is an enhanced version of the geographical source routing (GSR) protocol. The proposed protocol, named efficient GSR, uses an ant-based algorithm to find a route that has optimum network connectivity. It is assumed that every vehicle has a digital map of the streets comprised of junctions and street segments. Using information included in small control packets called ants, the vehicles calculate a weight for every street segment proportional to the network connectivity of that segment. Ant packets are launched by the vehicles in junction areas. In order to find the optimal route between a source and a destination, the source vehicle determines the path on a street map with the minimum total weight for the complete route. The correct functionality of the proposed protocol has been verified, and its performance has been evaluated in a simulation environment. The simulation results show that the packet delivery ratio is improved by more than 10% for speeds up to 70 km/h compared with the VANET routing protocol based on ant colony optimization (VACO) that also uses an ant-based algorithm. In addition, the routing control overhead and end-to-end delay are also reduced.

A novel dynamic assignment rule for the distributed job shop scheduling problem using a hybrid ant-based algorithm

Distributed scheduling problems are among the most investigated research topics in the fields of Operational Research, and represents one of the greatest challenges faced by industrialists and researchers today. The Distributed Job shop Scheduling Problem (DJSP) deals with the assignment of jobs to factories and with determining the sequence of operations on each machine in distributed manufacturing environments. The objective is to minimize the global makespan over all the factories. Since the problem is NP-hard to solve, one option to cope with this intractability is to use an approximation algorithm that guarantees near-optimal solutions quickly. Ant based algorithm has proved to be very effective and efficient in numerous scheduling problems, such as permutation flow shop scheduling, flexible job shop scheduling problems and network scheduling, etc. This paper proposes a hybrid ant colony algorithm combined with local search to solve the Distributed Job shop Scheduling Problem. A novel dynamic assignment rule of jobs to factories is also proposed. Furthermore, the Taguchi method for robust design is adopted for finding the optimum combination of parameters of the ant-based algorithm. To validate the performance of the proposed algorithm, intensive experiments are carried out on 480 large instances derived from well-known classical job-shop scheduling benchmarks. Also, we show that our algorithm can process up to 10 factories. The results prove the efficiency of the proposed algorithm in comparison with others.

An Ant-Based Algorithm to Solve Distributed Constraint Optimization Problems

As an important population-based algorithm, ant colony optimization (ACO) has been successfully applied into various combinatorial optimization problems. However, much existing work in ACO focuses on solving centralized problems. In this paper, we present a novel algorithm that takes the power of ants to solve Distributed Constraint Optimization Problems (DCOPs), called ACO_DCOP. In ACO_DCOP, a new mechanism that captures local benefits is proposed to compute heuristic factors and a new method that considers the cost structure of DCOPs is proposed to compute pheromone deltas appropriately. Moreover, pipelining technique is introduced to make full use of the computational capacity and improve the efficiency. In our theoretical analysis, we prove that ACO_DCOP is an anytime algorithm. Our empirical evaluation indicates that ACO_DCOP is able to find solutions of equal or significantly higher quality than state-of-the-art DCOP algorithms.

Different hydraulic analysis conditions for sewer network design optimisation problem using three different evolutionary algorithms

In this paper, the efficiency of considering the constant and varying Manning coefficient for a hydraulic analysis model on the optimal solution of sewer network design optimisation problem is studied. To solve sewer network design optimisation problem, here, different formulations are proposed using genetic algorithm, discreet and continues ant colony optimisation algorithms. In all proposed formulations, the nodal cover depths of the sewer network are taken as decision variables of the problem. Furthermore, for both ant-based algorithms two different formulations are proposed using unconstrained and constrained versions of these algorithms. The constrained versions of these algorithms are proposed here for the explicit satisfaction of the minimum pipe slope constraint leading to smaller search space. Two benchmark test examples are solved here using proposed formulations and the results are presented and compared with other available results. Comparison of the results shows the superiority of considering varying Manning coefficient condition for hydraulic analysis model. Furthermore, the results show the superiority of continues ant colony optimisation algorithm and especially the constrained version of it to optimally solve the sewer network design optimisation problem.

Different hydraulic analysis conditions for sewer network design optimisation problem using three different evolutionary algorithms

In this paper, the efficiency of considering the constant and varying Manning coefficient for a hydraulic analysis model on the optimal solution of sewer network design optimisation problem is studied. To solve sewer network design optimisation problem, here, different formulations are proposed using genetic algorithm, discreet and continues ant colony optimisation algorithms. In all proposed formulations, the nodal cover depths of the sewer network are taken as decision variables of the problem. Furthermore, for both ant-based algorithms two different formulations are proposed using unconstrained and constrained versions of these algorithms. The constrained versions of these algorithms are proposed here for the explicit satisfaction of the minimum pipe slope constraint leading to smaller search space. Two benchmark test examples are solved here using proposed formulations and the results are presented and compared with other available results. Comparison of the results shows the superiority of considering varying Manning coefficient condition for hydraulic analysis model. Furthermore, the results show the superiority of continues ant colony optimisation algorithm and especially the constrained version of it to optimally solve the sewer network design optimisation problem.

A distributed ant-based algorithm for routing and wavelength assignment in an optical burst switching flexible spectrum network with transmission impairments

This work describes a distributed algorithm inspired by ant colony optimisation for a solution to the problem of dynamic routing and wavelength assignment with wavelength continuity constraint in optical burst switched networks. The evaluation was conducted in a wavelength division multiplexed network environment with limited number of wavelength channels and in a flexible spectrum network environment undergoing transmission impairments. The simulations in the flexible spectrum network environment aim to replicate the effects of both linear and nonlinear physical layer impairments. Under these effects, an optical burst control packet could be lost during traversal of the network with no measure in place to notify the network of the loss or to free up optical resource reservations. The optical burst switching acknowledgement protocol has been modified in this work to account for a burst control packet failure by implementing a traversal acknowledgement to cater for its loss. The performance of the distributed ant-based algorithm has been extensively evaluated on several network topologies and compared with that obtained by shortest path routing and ant colony routing and wavelength assignment. The results show that the distributed ant-based algorithm significantly improves the burst transmission success probability in the wavelength division multiplexed network environment and provides a good solution in the flexible spectrum network environment undergoing transmission impairments.

Ant-based job shop scheduling with genetic algorithm for makespan minimisation on identical machines

The ant-based optimisation techniques have been used in various problems and could be used in job shop scheduling. The application of ant-based method in job shop scheduling is to find out the sequence of jobs with low time complexity. A new ant-based job shop scheduling algorithm with genetic algorithm is proposed using support, which generates a scheduling of jobs with low waited time for the processes while scheduling the jobs. The support in the new genetic algorithm is inversely proportional to the time complexity. The genetic algorithm with support function is proposed to find out the path in which all the processes have to service with resources. The proposed algorithm produces optimal scheduling in which all processes have to wait minimum time. The problem is to reduce the waiting time of all the processes.

A Green Ant-Based method for Path Planning of Unmanned Ground Vehicles

Planning of optimal/shortest path is required for proper operation of unmanned ground vehicles (UGVs). Although most of the existing approaches provide proper path planning strategy, they cannot guarantee reduction of consumed energy by UGVs, which is provided via onboard battery with constraint power. Hence, in this paper, a new ant-based path planning approach that considers UGV energy consumption in its planning strategy is proposed. This method is called Green Ant (G-Ant) and integrates an ant-based algorithm with a power/energy consumption prediction model to reach its main goal, which is providing a collision-free shortest path with low power consumption. G-Ant is evaluated and validated via simulation tools. Its performance is compared with ant colony optimization, genetic algorithm, and particle swarm optimization approaches. Various scenarios were simulated to evaluate G-Ant performance in terms of UGV travel time, travel length, computational time by taking into account different numbers of iterations, different numbers of obstacle, and different population sizes. The obtained results show that the G-Ant outperforms the existing methods in terms of travel length and number of iteration.

Arc Based Ant Colony Optimization Algorithm for optimal design of gravitational sewer networks

Abstract In this paper, constrained and unconstrained versions of a new formulation of Ant Colony Optimization Algorithm (ACOA) named Arc Based Ant Colony Optimization Algorithm (ABACOA) are augmented with the Tree Growing Algorithm (TGA) and used for the optimal layout and pipe size design of gravitational sewer networks. The main advantages offered by the proposed ABACOA formulation are proper definition of heuristic information, a useful component of the ant-based algorithms, and proper trade-off between the two conflicting search attributes of exploration and exploitation. In both the formulations, the TGA is used to incrementally construct feasible tree-like layouts out of the base layout. In the first formulation, unconstrained version of ABACOA is used to determine the nodal cover depths of sewer pipes while in the second formulation, a constrained version of ABACOA is used to determine the nodal cover depths of sewer pipes which satisfy the pipe slopes constraint. Three different methods of cut determination are also proposed to complete the construction of a tree-like network containing all base layout pipes, here. The proposed formulations are used to solve three test examples of different scales and the results are presented and compared with other available results in the literature. Comparison of the results shows that best results are obtained using the third cutting method in both the formulations. In addition, the results indicate the ability of the proposed methods and in particular the constrained version of ABACOA equipped with TGA to solve sewer networks design optimization problem. To be specific, the constrained version of ABACOA has been able to produce results 0.1%, 1% and 2.1% cheaper than those obtained by the unconstrained version of ABACOA for the first, second and the third test examples, respectively.

An Energy Efficient Routing Scheme by using GPS information for Wireless Sensor Networks

A wireless sensor network (WSN) (or IoT network) is a collection of distributed nodes. These nodes gather data from various sensors and relay that information to a central point through a wireless network. Therefore, the data can be aggregated and have something useful done with it. These types of networks deal primarily with the transmission of small amounts of data that needs to be sent very efficiently. In this paper, we propose an energy-efficient routing algorithm based on the global positioning system (GPS) information. Proposed scheme track the destination's location based on the beacon messages of the main route nodes. Through the experiments, proposed scheme shows improvements in the data packet delivery ratio and reduces the amount of routing control message overhead compared with existing routing protocols such as energy-efficient ant-based routing algorithm (EEABR) and ladder diffusion.