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...

Multi-objective optimization is widely applied in a number of areas now-a-days. Unfortunately, many combinatorial multi-objective optimization problems are NP-hard. However, it is often unnecessary to have an exact solution. So, heuristic approach can obtain a near-optimal solution in some reasonable time with the smallest possible computational burden. Intelligent Water Drops algorithm (IWD) a...

Mosul's city land covers soil, cultivated land, stony, pastoral water, and ploughed agricultural land. We have classified multispectral images captured by the sensor (TM) carried on Landsat satellite. Integrated approach of intelligent water drops (IWDs) algorithm is used to identify natural terrain. In this research, IWDs been suggested find best results for image classification. The purpose u...

Scientists are beginning to realize more and more that nature is a great source for inspiration in order to develop intelligent systems and algorithms. In the field of Computational Intelligence, especially Evolutionary Computation and Swarm-based systems, the degree of imitation from nature is surprisingly high and we are at the edge of developing and proposing new algorithms and/or systems, w...

We explore numerically the feasibility of using chemical patterning to control the size and polydispersity of micrometer-scale drops. The simulations suggest that it is possible to sort drops by size or wetting properties by using an array of hydrophilic stripes of different widths. We also demonstrate that monodisperse drops can be generated by exploiting the pinning of a drop on a hydrophilic...

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...

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 ...