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.

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.

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.