Image Edge Detection Using Ant Colony Optimization with Genetic Algorithm

An edge detection technique using genetic algorithm-based optimization

TThis paper presents a hybrid method that combines the genetic algorithm (GA) and the ant colony system algorithm (ACS), namely GACS, to solve the traffic routing problem. In the proposed framework, we use the genetic algorithm to optimize the ACS parameters in order to attain the best trips and travelling time through several novel functions to help ants to update the global and local pheromones. The GACS framework is implemented using the VANETsim package and the real city maps from the open street map project. The experimental results show that our framework achieves a considerably higher performance than A-Star and the classical ACS algorithms in terms of the length of the global best path and the time for trips. Moreover, the GACS framework is also efficient in solving the congestion problem by online monitoring the conditions of traffic light systems.
 KeywordsTraffic routing; Ant colony system; Genetic algorithm; VANET simulator.
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The first ant colony optimization (ACO) called ant system was inspired through studying of the behavior of ants in 1991 by Macro Dorigo and co-workers [1]. An ant colony is highly organized, in which one interacting with others through pheromone in perfect harmony. Optimization problems can be solved through simulating ant’s behaviors. Since the first ant system algorithm was proposed, there is a lot of development in ACO. In ant colony system algorithm, local pheromone is used for ants to search optimum result. However, high magnitude of computing is its deficiency and sometimes it is inefficient. Thomas Stutzle et al. introduced MAX-MIN Ant System (MMAS) [2] in 2000. It is one of the best algorithms of ACO. It limits total pheromone in every trip or sub-union to avoid local convergence. However, the limitation of pheromone slows down convergence rate in MMAS. In optimization algorithm, it is well known that when local optimum solution is searched out or ants arrive at stagnating state, algorithm may be no longer searching the global best optimum value. According to our limited knowledge, only Jun Ouyang et al [3] proposed an improved ant colony system algorithm for multi-colony ant systems. In their algorithms, when ants arrived at local optimum solution, pheromone will be decreased in order to make algorithm escaping from the local optimum solution. When ants arrived at local optimum solution, or at stagnating state, it would not converge at the global best optimum solution. In this paper, a modified algorithm, multi-colony ant system based on a pheromone arithmetic crossover and a repulsive operator, is proposed to avoid such stagnating state. In this algorithm, firstly several colonies of ant system are created, and then they perform iterating and updating their pheromone arrays respectively until one ant colony system reaches its local optimum solution. Every ant colony system owns its pheromone array and parameters and records its local optimum solution. Furthermore, once a ant colony system arrives at its local optimum solution, it updates its local optimum solution and sends this solution to global best-found center. Thirdly, when an old ant colony system is chosen according to elimination rules, it will be destroyed and reinitialized through application of the pheromone arithmetic crossover and the repulsive operator based on several global best-so-far optimum solutions. The whole algorithm implements iterations until global best optimum solution is searched out. The following sections will introduce some concepts and rules of this multi-colony ant system. This paper is organized as follows. Section II briefly explains the basic ACO algorithm and its main variant MMAS we use as a basis for multi-colony ant algorithm. In Section III we

Detecting edges is one of the most significant aspects of computer vision. Typical methods for edge detection like Sobel and Canny are robust and fast, but they are sensitive to noise. Soft computing techniques such as particle swarm optimization (PSO), ant colony optimization (ACO), genetic algorithms (GA) and fuzzy logic system (FLS) have extensive application in edge detection of images because of their adaptive behavior. Edge detection is identifying the discontinuities in intensity of the pixel and grouping the contour of edges. The quality of edges in ACO-based edge detection majorly depends on the choice of constants, pheromone evaporation rate, number of iterations etc. In PSO-based edge detection, the quality of images depends on the values of acceleration coefficients and inertia weight. However, thresholding is major stakeholder in determining the fitness of the chromosomes. The population contains 2-D chromosomes. Fuzzy systems are most suitable for designing edge detection hardware. This paper presents a thorough comparative study of soft-computing-based edge detection techniques and highlights their key features. The factors affecting quality of edges are compared, and the actual outcomes of the approaches are systematically arranged for better understanding.

Edge detection is identifying the discontinuities in the intensity of the pixel and grouping the contour of edges. Edge detection in digital images is a significant phenomenon in computer vision. In the initial stages of development of edge detection techniques, typical methods like Sobel Canny came into existence. These methods are fast but sensitive towards noise. Soft computing techniques like ant colony optimization (ACO), Genetic Algorithms (GA), Particle Swarm optimization (PSO) and Fuzzy Logic System (FLS) have extensive application in image edge detection due to their adaptiveness. In this paper, prominent ACO based techniques for edge detection are discussed and compared. The quality of edges identified by ACO based edge detection technique depends on the choice of constants, pheromone evaporation rate, number of iterations etc.

Ant colony system which is classified as a meta-heuristic algorithm is considered as one of the best optimization algorithm for solving different type of NP-Hard problem including the travelling salesman problem. A heuristic function in the Ant colony system uses pheromone and distance values to produce heuristic values in solving the travelling salesman problem. However, the heuristic values are not updated in the entire process to reflect the knowledge discovered by ants while moving from city to city. This paper presents the work on enhancing the heuristic function in ant colony system in order to reflect the new information discovered by the ants. Experimental results showed that enhanced algorithm provides better results than classical ant colony system in term of best, average and standard of the best tour length. Keywords : Ant Colony Optimization, Ant Colony System, Heuristic Function, Traveling Salesman Problem 1. Introduction Biological ants have the ability to discover the shortest route from the nest to the source of food [1]. Although they do not have an advanced vision system [2], they have the ability to communicate with the environment. Ants use a chemical substance called a “pheromone” to communicate with the environment and between each other [3]. Pheromone substance has an evaporation property which is a powerful mechanism to update the route information. While an ant moves looking for food, it deposits a pheromone along the path. The following ant will, more likely, select the route with richer pheromones. This mechanism will make the ant choose the shortest path. In 1992, Marco Dorigo proposed the first Ant Colony Optimization (ACO) algorithm to search for an optimal solution in graphs to solve optimization problems such as the travelling salesman problem, job scheduling and network routing [1]. The variants of ACO are: (i) Ant System (AS) [4] [5] [6]. (ii) The first improvement on the ant system, called the Elitist strategy for Ant System (EAS) [7]. The improvement was done by providing strong additional reinforcement to the arcs belonging to the best tour found since the start of the algorithm. (iii) Rank-Based Ant System (AS

The paper based on the analysis of ant colony algorithm and any improved algorithm, We change the initial position of the ant placed from randomly into placed near the edge points and define bounded domain node gradient values as the heuristic information,and also adaptive computing method is used to reach the final threshold.Meanwhile histogram equalization method for image preprocessing is introduced.Simulation experiment results show that this algorithm can better continuously detected edges and test results are efficient and accurate. This algorithm provides a theoretical basis for the more effective detection in infrared image edge character. Introduction Ant Colony Optimization (ACO) is a kind of distributed intelligence simulation algorithm, the basic idea is to imitate ants which rely on pheromones to communicate and show the social behavior[1].That is to use biological information as the basis of ants to select subsequent behavior and through the cooperation and interaction between ants to complete global optimization search process. Nezamabadi pour is the first successful person who realized image edge detection based on ant colony algorithm in 2005[2].Then many scholars put forward many improved algorithm, such as the improvement based on stimulating factor, combine with other swarm intelligence algorithm or merge with classic image edge detection technology.Literature [3] proposed the use of gradient image gray value change as the heuristic information of edge detection and join the movement factor change quantity, and change quantity inspire ant colony move to the edge .Literature [4] proposed to integrate the traditional Canny edge detection operator with ant colony algorithm, different pixels proportion and image angular points which Canny operator get as a priori knowledge of edge points,through the calculation of ant colony algorithm to realize image edge extraction.Literature [5] put forward the idea of using genetic algorithm to improve the ant colony algorithm, and variation factors which can be adjusted with algorithms process were introduced in the algorithm , and so on. In this paper, the histogram equalization method is applied to preprocess images to enhance the contrast of images, then a kind of improved ant colony algorithm is proposed according to the characteristics of infrared image and applied it to the infrared image edge detection. ACO algorithm and its improvements Improved ACO (Improve Ant Colony Optimization, IACO) Initialize improvements: ant’s initial position is amended and place it from random into near edge, the specific approach is to take the image gray gradient threshold to optimize this process. Inspired pheromone improvements: Neighborhood node gradient values will be defined as heuristic information, then we can get the calculating formula of heuristic information as

Purpose: the distribution routing plans of multi-depots vehicle scheduling problem will increase exponentially along with the adding of customers. So, it becomes an important studying trend to solve the vehicle scheduling problem with heuristic algorithm. On the basis of building the model of multi-depots vehicle scheduling problem, in order to improve the efficiency of the multiple depots vehicle routing , the paper puts forward a fusion algorithm on multiple depots vehicle routing based on the ant colony algorithm with genetic algorithm. Design/methodology/approach: to achieve this objective, the genetic algorithm optimizes the parameters of the ant colony algorithm. The fusion algorithm on multiple depots vehicle based on the ant colony algorithm with genetic algorithm is proposed. Findings: simulation experiment indicates that the result of the fusion algorithm is more excellent than the other algorithm, and the improved algorithm has better convergence effective and global ability. Research limitations/implications: in this research, there are some assumption that might affect the accuracy of the model such as the pheromone volatile factor, heuristic factor in each period, and the selected multiple depots. These assumptions can be relaxed in future work. Originality/value: In this research, a new method for the multiple depots vehicle routing is proposed. The fusion algorithm eliminate the influence of the selected parameter by optimizing the heuristic factor, evaporation factor, initial pheromone distribute, and have the strong global searching ability. The Ant Colony algorithm imports cross operator and mutation operator for operating the first best solution and the second best solution in every iteration, and reserves the best solution. The cross and mutation operator extend the solution space and improve the convergence effective and the global ability. This research shows that considering both the ant colony and genetic algorithm together can improve the efficiency multiple depots vehicle routing.

Present research is focused on content-based satellite image retrieval. Boundaries of satellite images have been analyzed using edge detection and compression technique. Edge detection and compression in ant colony optimization and image processing are the subject of current research. The suggested study is centered on the integration of ACO with an edge detection technique and compression. Detection of edges, optimization of ant colonies, and image processing are all topics that have been studied in the past. The difficulties that have been addressed in earlier research are included in the problem statement. According to ant colony optimization requirements, edge detection is necessary when it comes to image processing. Edge detection is also used for image processing, and this is done using the edge detection approach. Edge detection pictures have been studied for contrast, correlation, and entropy as well as for energy, variance, deviation, smoothness, and skewness throughout the feature extraction process.KeywordsImage processingFeature extractionCompressionEdge detectionACO

The ant colony optimization algorithm (ACO) was initially developed to be a metaheuristic for combinatorial optimization problem. In scores of experiments, it is confirmed that the parameter settings in ACO have direct effects on the performance of the algorithm. However, few studies have specially reported the parameter control for ACO. The aim of this paper was to put forward some strategies to adaptively adjust the parameter in ACO and further provide a deeper understanding of ACO parameter control, including static and dynamic parameters. We choose well-known ant system (AS) and ant colony system (ACS) to be controlled by our proposed strategies. The parameters in AS and ACS include β, pheromone evaporation rate (?), exploration probability factor (q0) and number of ants (m). We have proposed three adaptive parameter control strategies (SI, SII and SIII) based on fuzzy logic control which adjusts ?, q0 and m, respectively. The feature selection problem is considered for evaluating the parameter control strategies. In addition, because AS and ACS are not intrinsically fit for feature selection problem, we have modified the AS and ACS, which are named as fuzzy adaptive ant system (FAAS) and fuzzy adaptive ant colony system (FAACS), to make them more suitable for feature selection problem. Because only one parameter is allowed to be dynamically adjusted in FAAS or FAACS, the remaining parameters should be statically specified. Thus, we have developed parametric guidelines for proper combination of static parameter settings. The performance of FAAS and FAACS is compared with that of the AS-based, ACS-based, particle swarm optimization-based and genetic algorithm-based methods on a comprehensive set of 10 benchmark data sets, which are taken from UCI machine learning and StatLog databases. The numerical results and statistical analysis show that the proposed algorithms outperform significantly than other methods in terms of prediction accuracy with smaller subset of features.

Edge detection plays a prominent role in the medical field, computer vision and image processing. Particularly in the medical field, it serves as a essential technique to identify disruptions, irregularities, edges, and other significant features. Ant colony optimization is nature inspired metaheuristic algorithm that is popularly applied to optimization problems. This article proposes a method for edge detection for medical images using a metaheuristic ant colony system algorithm. A new heuristic information function based on statistical range is proposed. Working systematically and behaving cooperatively, the artificial ant’s colony builds a binary image, which eventually results in edge detection. The proposed approach takes advantage of improving solutions using the revive operator. Experimental results exhibit the better performance of the proposed approach compare with existing method in terms of output image and effectiveness.

The ant colony algorithm is a new kind of simulated evolutionary algorithm for random searching, which converges to the optimal path by the accumulation of pheromone and updating. But it lacks the global searching ability, and easily falls into local minimum and appears the phenomenon of premature stagnation. In this paper, the improved method, which applies a stronger global searching ability of genetic algorithm and introduces the improved crossover and mutation operation, is designed to enhance the global searching ability of ant colony algorithm. The improved algorithm is applied to a two-dimensional function optimization simulation calculation. The results show that the improved ant colony algorithm is better than the original in accuracy and time efficiency

Focusing on the problem that the ant colony algorithm gets into stagnation easily and cannot fully search in solution space, a text clustering approach based on the fusion of the ant colony and genetic algorithms is proposed. The four parameters that influence the performance of the ant colony algorithm are encoded as chromosomes, thereby the fitness function, selection, crossover and mutation operator are designed to find the combination of optimal parameters through a number of iteration, and then it is applied to text clustering. The simulation results show that compared with the classical k-means clustering and the basic ant colony clustering algorithm, the proposed algorithm has better performance and the value of F-Measure is enhanced by 5.69%, 48.60% and 69.60%, respectively, in 3 test datasets. Therefore, it is more suitable for processing a larger dataset.

In this paper, the genetic algorithm is added to each iteration of the ant colony system, taking the and advantage of fast convergence of the genetic algorithm to speed up the convergence rate of the ant colony system. While the mutation mechanism of genetic algorithm helps ant colony system improve its ability to jump out of the local optimization. Thus, a hybrid algorithm with the advantages of genetic algorithm and ant colony algorithm is formed. The application of this method in welding robot path planning is realized by modeling, we compare the optimal solution and convergence speed of genetic ant colony hybrid algorithm with genetic algorithm in path planning, and the comparison results show that the hybrid algorithm is superior to genetic algorithm. Simulation results show that this hybrid algorithm solves the contradiction between the convergence speed and the optimization degree of welding robot path planning, and has good application in the welding robot path planning.

Edge detection refers to the process of extracting edge information of an image. It is considered as a basic step used in the majority of image processing applications. The aim of this study was to modify local updates of pheromones. Therefore, the convergence of the Ant Colony Optimization (ACO) algorithm applied to image edge detection could be accelerated effectively. Such the algorithm is a metaheuristic method applying the ants as agents with their pheromone updates for an effective and efficient solution of search processes. Five ACO algorithms for edge detection, i.e., ACO, modified ACO, ACO with the Sobel operator, ACO with the Prewitt operator, and ACO with the Isotropic operator were in comparison. Nearly optimal solutions of several image datasets were discovered through examination of the number of ants and iterations. Additionally, calculation results of each image dataset and algorithm were compared. The evidence shows that solutions produced by all algorithms are equally good. For an image dataset with more ants, however, it is found that the modified ACO algorithm has the best solution in terms of time convergence. The study contribution is further next to adding the concept of improving edge detection in the image with the ant colony optimization algorithm. The implementation of the study carried out is to modify local updates which are functionally used for improving the edge detection dealt with by ants taking part in ACO.