Elitist Continuous Ant Colony Optimization Algorithm: Application to Reservoir Operation Problems

In this paper, a new continuous ant colony optimization (caco) algorithm is proposedfor optimal reservoir operation. the paper presents a new method of determining and setting acomplete set ofcontrol parameters for any given problem, saving the user from a tedious trial anderror based approach to determine them. the paper also proposes an elitist strategy for cacoalgorithm where best solution of each iteration is directly copied to the next iteration to improveper:formance ofthe method. the performance ofthe caco algorithm is demonstrated against somebenchmark test functions and compared with some other popular heuristic algorithms. the resultsindicated good performance of the proposed method for global minimization of continuous testfunctions. the method was also used to.find the optimal operation ofthe dez reservoir in southerniran, a problem in the reservoir operation discipline. a normalized squared deviation ofthe releasesfrom the required demands is considered as the fitness function and the results are presented andcompared with the solution obtained by non linear programming (nlp) and discrete ant colonyoptimization (daco) models. it is observed that the results obtained from caco algorithm aresuperior to those obtainedfrom nlp and daco models.