This paper presents a new nature inspired Intelligent Water Drops (IWD) based algorithm for finding peaks in continuous multimodal optimization problems. Initially various conceptual similarities were identified between IWD algorithm and Genetic Algorithm(GA). Simultaneously applying IWD-Continuous Optimization(IWD-CO) algorithm and GA on a function in finding the global optima and found IWDCO ...

PURPOSE OF THE STUDY Family caregivers experience high levels of stress that place them at risk for poor health outcomes. We explore whether an intervention which lowers caregivers' daily exposure to stressors, adult day services (ADS), leads to improved regulation of the stress hormone, cortisol, which has implications for health and well-being. DESIGN AND METHODS Participants (N = 158) were...

Purpose – The purpose of this paper is to test the capability of a new population-based optimization algorithm for solving an NP-hard problem, called “Multiple Knapsack Problem”, or MKP. Design/methodology/approach – Here, the intelligent water drops (IWD) algorithm, which is a population-based optimization algorithm, is modified to include a suitable local heuristic for the MKP. Then, the prop...

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...

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 (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we investigate the effectiveness of the selection method in the solution construction phase of the IWD algorithm. Instead of the fitness proportionate selection method in the original IWD algorithm, two ranking-based sel...

This paper implements Intelligent Water Drop (IWD) technique for removal of speckle noise from the ultrasound images. Noise is a factor that degrades the quality of images, so we need to use an efficient technique to remove the noise from the images. IWD algorithm removes the noise and also retains the useful information of the medical image. It improves the quality of an image. In this work, r...

A comparison between two technologies „Swarm Intelligence‟ and „Intelligent Water drops‟ inorder to overcome the disadvantages of various technologies is an integral concern of this paper . It is nature inspired. IWD algorithm is used to calculate the solutions of the n-queen puzzle with a simple local heuristic. Water of the ocean river easily finds best way from the number of various ways ava...

Automatic test data generation is required to generate test cases dynamically for a specific software program. Manual generation of test data is too tedious and a time consuming task. This paper proposes a technique using Intelligent Water Drop (IWD) for automatic generation of test data. Correctly generated test data helps in reducing the effort while testing the software. This paper discusses...

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...