The edges of an image define the image boundary. When the image is noisy, it does not become easy to identify the edges. Therefore, a method requests to be developed that can identify edges clearly in a noisy image. Many methods have been proposed earlier using filters, transforms and wavelets with Ant Colony Optimization (ACO) that detect edges. We here used ACO for edge detection of noisy images with Gaussian noise and salt and pepper noise. As the image edge frequencies are close to the noise frequency band, the edge detection using the conventional edge detection methods is challenging. The movement of Ants depends on local discrepancy of image’s intensity value. The simulation results compared with existing conventional methods and are provided to support the superior performance of ACO Algorithm in noisy images edge detection. Canny, Sobel and Prewitt operator have thick, non continuous edges and with less clear image content. But the applied method gives thin and clear edges.

In this paper, an improved Ant Colony Optimization (ACO) Algorithm is proposed for reservoir operation. Through a collection of cooperative agents called Ants, the near-optimum solution to the reservoir operation can be effectively achieved. To apply the proposed ACO Algorithm, the problem is approached by considering a finite horizon with a time series of inflow, classifying the reservoir volume to several intervals and deciding for release sat each period, with respect to a predefined optimality criterion. Pheromone promotion, explorer Ants and a local search are included in the standard ACO Algorithm for a single reservoir, deterministic, finite-horizon problem and applied to the Dez reservoir in Iran. The results demonstrate that the proposed ACO Algorithm provides improved estimates of the optimal releases of the Dez reservoir, as compared to traditional state-of-the-art Genetic Algorithms. It is Anticipated that further tuning of the Algorithmic parameters will further improve the computational efficiency and robustness of the proposed method.

Intruders often combine exploits against multiple vulnerabilities in order to break into the system. Each attack scenario is a sequence of exploits launched by an intruder that leads to an undesirable state such as access to a database, service disruption, etc. The collection of possible attack scenarios in a computer network can be represented by a directed graph, called network attack graph (NAG). The aim of minimization analysis of network attack graphs is to find a minimum critical set of exploits that completely disconnect the initial nodes and the goal nodes of the graph. In this paper, we present an Ant Colony Optimization Algorithm, called AntNAG, for minimization analysis of large-scale network attack graphs. Each Ant constructs a critical set of exploits. A local search heuristic has been used to improve the overall performance of the Algorithm. The aim is to find a minimum critical set of exploits that must be prevented to guarAntee no attack scenario is possible. We compare the performance of the AntNAG with a greedy Algorithm for minimization analysis of several large-scale network attack graphs. The results of the experiments show that the AntNAG can be successfully used for minimization analysis of large-scale network attack graphs.

Searching and optimizing by using collective intelligence are known as highly efficient methods that can be used to solve complex engineering problems. Ant Colony Optimization Algorithm (ACO) is based on collective intelligence inspired by Ants' behavior in finding the best path in search of food. In this paper, the ACO Algorithm is used for image edge detection. A fuzzy-based system is proposed to increase the dynamics and speed of the proposed method. This system controls the amount of pheromone and distance. Thus, instead of considering constAnt values for the parameters of the Algorithm, variable values are used to make the search space more accurate and reasonable. The fuzzy Ant Colony Optimization Algorithm is applied on several images to illustrate the performance of the proposed Algorithm. The obtained results show better quality in extracting edge pixels by the proposed method compared to several image edge detection methods. The improvement of the proposed method is shown quAntitatively by the investigation of the time and entropy of conventional methods and previous works. Also, the robustness of the proposed method is demonstrated against additive noise.

In this paper, a new Continuous Ant Colony Optimization (CACO) Algorithm is proposed for optimal reservoir operation. The paper presents a new method of determining and setting a complete set of control parameters for any given problem, saving the user from a tedious trial and error based approach to determine them. The paper also proposes an elitist strategy for CACO Algorithm where best solution of each iteration is directly copied to the next iteration to improve performance of the method. The performance of the CACO Algorithm is demonstrated against some benchmark test functions and compared with some other popular heuristic Algorithms. The results indicated good performance of the proposed method for global minimization of continuous test functions. The method was also used to find the optimal operation of the Dez reservoir in southern Iran, a problem in the reservoir operation discipline. A normalized squared deviation of the releases from the required demands is considered as the fitness function and the results are presented and compared with the solution obtained by Non Linear Programming (NLP) and Discrete Ant Colony Optimization (DACO) models. It is observed that the results obtained from CACO Algorithm are superior to those obtained from NLP and DACO models.

Nowadays, with the advancement of database information technology, databases has led to large-scale distributed databases. According to this study, database management systems are improved and optimized so that they provide responses to customer questions with lower cost. Query processing in database management systems is one of the importAnt topics that grabs attentions. Until now, many techniques have been implemented for query processing in database system. The purpose of these methods is to optimize query processing in the database. The main topics that is interested in query processing in the database makes run-time adjustments of processing or summarizing topics by using the new approaches. The aim of this research is to optimize processing in the database by using adaptive methods. Ant Colony Algorithm (ACO) is used for solving Optimization problems. ACO relies on the created pheromone to select the optimal solution. In this article, in order to make adaptive hybrid query processing. The proposed Algorithm is fundamentally divided into three parts: separator, replacement policy, and query similarity detector. In order to improve the Optimization and frequent adaption and correct selection in queries, the Ant Colony Algorithm has been applied in this research. In this Algorithm, based on Versatility (adaptability) scheduling, Queries sent to the database have been attempted be collected. The simulation results of this method demonstrate that reduce spending time in the database. According to the proposed Algorithm, one of the advAntages of this method is to identify frequent queries in high traffic times and minimize the time and the execution time. This Optimization method reduces the system load during high traffic load times for adaptive query Processing and generally reduces the execution runtime and aiming to minimize cost. The rate of reduction of query cost in the database with this method is 2. 7%. Due to the versatility of high-cost queries, this improvement is manifested in high traffic times. In the future Studies, by adapting new system development methods, distributed databases can be optimized

Through a collection of cooperative agents called Ants, the near optimal solution to the multi-reservoir operation problem may be effectively achieved employing Ant Colony Optimization Algorithms (ACOAs). The problem is approached by considering a finite operating horizon, classifying the possible releases from the reservoir(s) intopre-determined intervals, and projecting the problem on a graph. By defining an optimality criterion, the combination of desirable releases from the reservoirs or operating policy is determined. To minimize the possibility of premature convergence to a local optimum, a combination of Pheromone Re-Initiation (PRI) and Partial Path Replacement (PPR) mechanisms are presented and their effects have been tested in a benchmark, nonlinear, and multimodal mathematical function. The finalized model is then applied to develop an optimum operatingpolicy for a single reservoir and a benchmark four-reservoir operation problem. Integration of these mechanisms improves the final result, as well as initial and final rate of convergence. In the benchmark Ackley function minimization problem, after 410 iterations, PRI mechanism improved the final solution by 97 percent and the combination of PRI and PPR mechanisms reduced final result to global optimum. As expected in the single-reservoir problem, with a continuous search space, a nonlinear programming (NLP) approach performed better than ACOAs employing a discretized search space on the decision variable (reservoir release). As the complexity of the system increases, the definition of an appropriate heuristic function becomes more and more difficult; this may provide wrong initial sight or vision to the Ants. By assigning a minimum weight to the exploitation term in a transition rule, the best result is obtained. In a benchmark 4-reservoirproblem, a very low standard deviation is achieved for 10 different runs and it is considered as an indication of low diversity of the results. In 2 out of 10 runs, the global optimal solution is obtained, where in the other 8 runs; results are as close as 99.8 percent of the global solution. Results and execution time compare well with those of well developed genetic Algorithms (GAs).

Construction planners often face the challenge of optimum resource utilization to compromise between different and usually conflicting aspects of projects. Time, cost and quality of project delivery are among the crucial aspects of each project. Emergence of new contracts that place an increasing pressure on maximizing the quality of projects while minimizing its time and cost, requires the development of models considering quality in addition to time and cost which has modeled extensively. In this paper, a new metaheuristic multi-Colony Ant Algorithm is developed for the Optimization of three objectives time-costquality as a trade-off problem. An example is analyzed to illustrate the capabilities of the present method in generating optimal/near optimal solutions. The model is also applied to two objective time-cost trade-off problem, and the results are compared to those of the existing approaches.