This research presents new algorithms for sub transmission simultaneous substation and network expansion planning.  Given an existing system model, the projected load growth in a target year and various system expansion options, the algorithms find the optimal mix of system expansion options to minimize the cost function subject to various system constraints and single contingencies on lines and transformers.The system expansion options considered include building new sub transmission lines/substations, the capacity to be upgraded and the service area of HV/MV substations. In this research, Genetic Algorithm (GA) with new coding, Ant Colony algorithm (AC) and hybrid Ant Colony and Genetic Algorithm (AC&GA) methods are employed. The optimization results are compared with successive elimination method to demonstrate the performance improvement.

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.

Financial distress prediction of companies is one of the important issues that can contribute to the success and survival of companies; because providing warning and timely signals can make companies aware of financial distress and bankruptcy and, therefore, by a correct management, they can prevent waste of resources and the damage caused by bankruptcy.Ant Colony Algorithm (ACA) is an intelligent method that was recently used to solve problems including classifications and predictions which had desired results. This study aims to investigate the financial distress prediction of companies using ant colony algorithm. The statistical population includes companies listed in Tehran Stock Exchange and the sample consists of 174 healthy and distressed companies. Predictor variables were selected from previous studies according to the ratios that were proposed as key variables in prediction model.The results of the study indicate that the ACA approach in predicting financial distress of companies had significantly better performance than multiple discriminant analysis (MDA).

In this paper, a new algorithm for solving the single loop routing problem is presented. The purpose of the single loop routing problem(SLRP) Is to find the shortest loop for an automated guided vehicle covering at least one edge of each department of a block layout. First it shown that this problem can be represented as a graph model Then a meta-heuristic algorithm based on and colony system is developed for ALRP by using the properties of the graph model. Computational results show the efficiency of the proposed algorithm in comparison with other techniques for solving SLRP.

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.