This paper presents novel krill herd (KH) nature-inspired metaheuristics for solving portfolio optimization task. Krill herd algorithm mimics the herding behavior of krill individuals. The objective function for the krill movement is defined by the minimum distances of each individual krill from food and from higher density of the herd. Constrained portfolio optimization problem extends the cla...

Data clustering is the process of partitioning a set of data objects into meaning clusters or groups. Due to the vast usage of clustering algorithms in many fields, a lot of research is still going on to find the best and efficient clustering algorithm. K-means is simple and easy to implement, but it suffers from initialization of cluster center and hence trapped in local optimum. In this paper...

The hybrid approach is proposed to show that the clusters also show the swarm behavior. Krill herd algorithm is used to show the simulation of the herding behavior of the krill individuals. Density based approach is used for discovering the clusters and to show the region with sufficiently high density into clusters of krill individuals that of the arbitrary shape in environment. The minimum di...

Recently, Gandomi and Alavi proposed a meta-heuristic optimization algorithm, called Krill Herd (KH). This paper introduces the chaos theory into the KH optimization process with the aim of accelerating its global convergence speed. Various chaotic maps are considered in the proposed chaotic KH (CKH) method to adjust the three main movements of the krill in the optimization process. Several tes...

Recently, Gandomi and Alavi proposed a meta-heuristic optimization algorithm, called Krill Herd (KH), for global optimization [Gandomi AH, Alavi AH. Krill Herd: A New Bio-Inspired Optimization Algorithm. Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831–4845, 2012.]. This paper represents an optimization method to global optimization using a novel variant of KH. This me...

In recent years, metaheuristic algorithms have been widely used in solving clustering problems because of their good performance and application effects. krill herd algorithm (KHA) is a new effective algorithm to solve optimization problems based on the imitation of krill individual behavior, and it is proven to perform better than other swarm intelligence algorithms. However, there are some we...

The Krill Herd Algorithm is the latest heuristic technique to be applied in deriving best solution within various optimization tasks. While there has been a few scientific papers written about this algorithm, none of these have described how its numerous basic parameters impact upon the quality of selected solutions. This paper is intended to contribute towards improving the aforementioned situ...

Today, in computer science, a computational challenge exists in finding a globally optimized solution from an enormously large search space. Various meta-heuristic methods can be used for finding the solution in a large search space. These methods can be explained as iterative search processes that efficiently perform the exploration and exploitation in the solution space. In this context, thre...

In video watermarking applications, there is a need to extract the watermark without using the original data because of the huge storage of the cover data. In this paper, we have intended to present an efficient multiple video watermarking using optimized three levels DWT. Basically, the system consists of three modules such as (i) selecting optimal wavelet coefficients using Oppositional Krill...