A direct search approach to determine optimal reservoir operating is proposed with ant colony optimization for continuous domains (ACOR). The model is applied to a system of single reservoir to determine the optimum releases during 42 years of monthly steps. A disadvantage of ant colony based methods and the ACOR in particular, refers to great amount of computer run time consumption. In this st...

In this paper, we solve some benchmarks of Set Covering Problem and Equality Constrained Set Covering or Set Partitioning Problem. The resolution techniques used to solve them were Ant Colony Optimization algorithms and Hybridizations of Ant Colony Optimization with Constraint Programming techniques based on Arc Consistency. The concept of Arc Consistency plays an essential role in constraint s...

For the shortcoming that the PI controller parameters can’t been dynamic tuning in constant voltage control system of doubly-fed wind turbines, a PI controller parameters dynamic tuning strategy based on the ant colony optimization (ACO) algorithm is presented. This strategy makes the two parameters in PI controller as the ant of the ant colony, the controlled absolute error integral function t...

Swarm intelligence is a relatively novel approach to problem solving that takes inspiration from the social behaviors of insects and of other animals. In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful one is the ant colony optimization. Ant colony optimization (ACO) algorithm, a novel population-based and meta-heuristic app...

Ant Colony Optimization (ACO) is a promising new approach to combinatorial optimization. Here ACO is applied to the traveling salesman problem (TSP). Using a genetic algorithm (GA) to nd the best set of parameters, we demonstrate the good performance of ACO in nding good solutions

Population-based method in which artificial ants iteratively construct candidate solutions. Solution construction is probabilistically biased by pheromone trail information, heuristic information and partial candidate solution of each ant (memory). Pheromone trails are modified during the search process to reflect collective experience.

Ant Colony Optimization (ACO) is a meta-heuristic iterative algorithm used to solve different combinatorial optimization problems. In this method, a number of artificial ants build solutions for an optimization problem and exchange information on their quality through a communication scheme that is similar to the one adopted by real ants. In this paper, Ant Colony Optimization is used to solve ...