This paper provides a novel implementation of the intelligent water drops (IWD) method for resolving data aggregation issues in heterogeneous wireless sensor networks (WSN). When aggregating node is utilized to transmit base station, research attempts show that traffic situations WSN may be modified appropriately by parameter tuning and algorithm modification. IWD used generate an optimum tree ...

In this paper, a hybrid algorithm based on modified intelligent water drops algorithm and learning automata for solving Steiner tree problem is proposed. Since the Steiner tree problem is NP-hard, the aim of this paper is to design an algorithm to construct high quality Steiner trees in a short time which are suitable for real time multicast routing in networks. The global search and fast conve...

In this paper, a modification to the new problem solving algorithm called “Intelligent Water Drops” or simply IWD algorithm has been proposed. This algorithm is based on the dynamic of river systems and the actions that water drops do in the rivers. Based on the observation on the behavior of natural water drops, artificial water drops are developed which possesses some of the remarkable proper...

ABSTARCT This paper presents a new method for optimal multi-objective reconfiguration of distribution system based on the novel Intelligent Water Drops (IWD) algorithm in order to mitigation of losses, improving the voltage profile and equalizing the feeder load balancing. The proposed method is validated using the IEEE33-bus test system and a TaiPower 11.4 KV distribution system as a real dist...

Abstract Travelling salesman problem (TSP) is a well known combinatorial optimization which has drawn colossal attention due to its eclectic range of applications. In this article, we have proposed two modified versions intelligent water drops (IWD) algorithm. The first one the enhanced IWD (e-IWD) algorithm solve single objective TSP. second modification, e-IWD been extended multi-objective IW...

In this paper, the K-means algorithm for data clustering is improved by the swarm-based nature-inspired optimisation algorithm, the intelligent water drops (IWD) algorithm. The K-means algorithm is an iterative algorithm in which the number of clusters is given in advance. Although the K-means is fast to converge, it is sensitive to the initial conditions. As a result, it is often trapped in lo...

In this paper, the Intelligent Water Drops (IWD) algorithm is augmented with a mutation-based local search to find the optimal values of numerical functions. The proposed algorithm called the IWD-CO (IWD for continuous optimization) is tested with six different benchmark functions. The experimental results are satisfactory, which encourage further researches in this regard. © 2011 Published by ...

The intelligent water drop algorithm is a swarm-based metaheuristic algorithm, inspired by the characteristics of water drops in the river and the environmental changes resulting from the action of the flowing river. Since its appearance as an alternative stochastic optimization method, the algorithm has found applications in solving a wide range of combinatorial and functional optimization pro...