Reconfiguration is one of the main operations in electric distribution systems for minimizing transmission losses, load balancing, increasing the reliability of distribution system, etc. These objectives would be considered individually or simultaneously. There are different methods for system reconfiguration such as to close one of the normally open switches and then open one of normally closed switches, close all normally closed switches and open some of them properly, dividing system into several subsystems and reconfiguring them separately. Some new methods based on artificial intelligency such as genetic algorithm, fuzzy logic, neural networks, simulated annealing and ant colony algorithm are also reported in reconfiguration of distribution systems. In this report, a new method based on ant colony algorithm using genetic operators is presented and its performance is investigated for reconfiguration of distribution systems. The methods, based on ant colony algorithm, were iired by the observation of real ant colonies. Ants have social insects, and one of the important and interesting behaviors of ant colonies is their foraging behavior, and, in particular, ability of finding shorter paths between food sources and their nest naturally. While moving from food source to the nest, ants remain on the ground a substance called pheromone that can be used by other ants to lead them to shorter paths between food source and nest. Using foraging behavior of real ants, we can use artificial ants for searching optimum solution in optimization problems. This idea is used in this thesis for reconfiguration of distribution systems in order to minimize loss and improve load balancing. In the proposed method, by some modifications we may remove some of the weaknesses of ltr"