14-09-2017, 02:52 AM
14-09-2017, 11:38 AM
A natural river often finds good roads between lots of roads possible in its ways from source to destination. These optimal or near optimal trajectories are obtained by the actions and reactions that take place between the drops of water and the drops of water with the beds of the rivers. The Intelligent Water Droplet Algorithm (IWD) is a new swarm-based optimization algorithm inspired by the observation of natural water droplets that flow in rivers. In this article, the IWD algorithm is tested to find solutions of the n-queen puzzle with a simple local heuristic. The vendor's problem (TSP) is also solved with a modified IWD algorithm. In addition, the IWD algorithm is tested with some more multi-pack problems (MKP) in which almost optimal or optimal solutions are obtained.
The optimal functioning of reservoirs is a difficult problem in water resources systems. In this article, the Intelligent Water-drop (IWD) algorithm is applied in a reservoir operation problem. IWD is an algorithm based on population and is initially proposed to solve combinatorial problems. The algorithm imitates the dynamics of the river system and the behavior of water droplets in rivers. To do so, data from the Dez depot located in southwestern Iran have been used to examine the performance of the model. In addition, due to the similarities between IWD and Ant Colony Optimization (ACO) algorithms, the results are compared with those of the ACO algorithm. The comparison of the results shows that while the IWD algorithm finds relatively better solutions, it is able to overcome the deficiencies of computational time consumption inherited in the ACO methods. This is very important in large models with too many decision variables where the runtime becomes a limiting factor for optimization model applications.