Optimization of controllers in the thermal system using initial pheromone distribution in Ant Colony Optimization

Ant Colony Optimization (ACO) , an intelligent swarm algorithm, proves effective in various fields. However, the choice of the first route and the initial distribution of pheromone are among the toughest yet most crucial factors in determining the performance of process optimization. According to the materials we referred to, almost all the existing methods of ACO set the same constant in all routes as the initial pheromone. However, in that case, the searching process might be misleading, or stick into local optimal values. In this article, a new method is proposed to optimize the parameter searching process in thermal objects particularly, implementing initial pheromone distribution according to a set of formulas concluded from many observances and practical tests. We used MATLAB as the program design platform. The experiment showed that this method is satisfactory. Moreover, it can be applied in other intelligent algorithms such as Genetic Algorithm, which is also in demand of setting initial parameters and range of values.