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 nearoptimum 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.


Submitted to Iranian Journal of Science and Technology (IJST), October 2003