Reservoir Operation by Ant Colony Optimization Algorithms

In this paper, ant colony optimization (ACO) algorithms are 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 ACO algorithms, 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 releases at each period with respect to a predefined optimality criterion. Three alternative formulations of ACO algorithms for reservoir operation are presented using a single reservoir, deterministic, finite-horizon problem and applied to the Dez reservoir in Iran. It is concluded that the ant colony system global-best algorithm provides better and comparable results with known global optimum results. Application of the model to a two-reservoir problem reveals its potential for being extended to multi-reservoir problems. As any direct search method, the model is quite sensitive to setup parameters, hence fine tuning of the parameters is recommended.