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Reservoir Operation by Ant Colony Optimization Algorithms

Mohammad Reza Jalali (mrjalali***at***iust.ac.ir)
Abbas Afshar (a_afshar***at***iust.ac.ir)
M. A. Marino (mamarino***at***ucdavis.edu)

Abstract: 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.

Keywords: Ant colony; Optimization; Reservoir operation

Category 1: Applications -- Science and Engineering (Civil and Environmental Engineering )

Category 2: Combinatorial Optimization (Meta Heuristics )

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

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Entry Submitted: 07/29/2003
Entry Accepted: 07/29/2003
Entry Last Modified: 02/24/2006

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