چكيده لاتين :
In this paper, a new Continuous Ant Colony Optimization (CACO) algorithm is proposed
for optimal reservoir operation. The paper presents a new method of determining and setting a
complete set ofcontrol parameters for any given problem, saving the user from a tedious trial and
error based approach to determine them. The paper also proposes an elitist strategy for CACO
algorithm where best solution of each iteration is directly copied to the next iteration to improve
per:formance ofthe method. The performance ofthe CACO algorithm is demonstrated against some
benchmark test functions and compared with some other popular heuristic algorithms. The results
indicated good performance of the proposed method for global minimization of continuous test
functions. The method was also used to.find the optimal operation ofthe Dez reservoir in southern
Iran, a problem in the reservoir operation discipline. A normalized squared deviation ofthe releases
from the required demands is considered as the fitness function and the results are presented and
compared with the solution obtained by Non Linear Programming (NLP) and Discrete Ant Colony
Optimization (DACO) models. It is observed that the results obtained from CACO algorithm are
superior to those obtainedfrom NLP and DACO models.
