In this paper a novel technique for Neighbor embedding single image super resolution (SR) is proposed. Given a low-resolution image, its high-resolution image is reconstructed from a set of training images, which are composed of one or more low-resolution and corresponding highresolution image pairs. In this paper we propose a new approach to a single image super-resolution through neighbor emb...

In this chapter, the authors study a new variant of Particle Swarm Optimization (PSO) to efficiently execute business processes. The main challenge of this application for the PSO is that the function evaluations typically take a high computation time. They propose the Gap Search (GS) method in combination with the PSO to perform a better exploration in the search space and study its influence ...

A heuristic particle swarm optimization (PSO) based algorithm is presented in this work and the novel hybrid approach is applied to linear array synthesis considering complex weights and directive element patterns so as to analyze its usefulness and limitations. Basically, classical PSO schemes are modified by introducing a tournament selection strategy and the downhill simplex local search met...

Heuristic optimization provides a robust and efficient approach for solving complex real-world problems. The focus of this research is on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO), for the global optimization of multimodal functions. Denoted as GA-PSO, this hybrid technique incorporates concepts from GA and PSO...

This paper presents a method for data model partitioning of power distribution network. Modern Distribution Management Systems which utilize multiprocessor systems for efficient processing of large data model are considered. The data model partitioning is carried out for parallelization of analytical power calculations. The proposed algorithms (Particle Swarm Optimization (PSO) and distributed ...

Particle Swarm Optimization (PSO) is a popular algorithm used extensively in continuous optimization. One of its well-known drawbacks is its propensity for premature convergence. Many techniques have been proposed for alleviating this problem. One of the popular and promising approaches is low-level hybridization (LLH) of PSO with Genetic Algorithm (GA). Nevertheless, the LLH implementation is ...

In this paper a novel technique for Neighbor embedding single image super resolution (SR) is proposed. Given a low-resolution image, its highresolution image is reconstructed from a set of training images, which are composed of one or more lowresolution and corresponding high-resolution image pairs. In this paper we propose a new approach to a single image super-resolution through neighbor embe...

Grid computing which is based on the high performance computing environment, basically used for solving complex computational demands. In the grid computing environment, scheduling of tasks is a big challenge. The task scheduling problem can be defined as a problem of assigning the number of resources to tasks where number of resources is less than the number of available tasks. Particle swarm ...

clustering is one of the known techniques in the field of data mining where data with similar properties is within the set of categories. k-means algorithm is one the simplest clustering algorithms which have disadvantages sensitive to initial values of the clusters and converging to the local optimum. in recent years, several algorithms are provided based on evolutionary algorithms for cluster...

The traveling salesman problem (TSP) is the problem of finding the shortest tour through all the nodes that a salesman has to visit. The TSP is probably the most famous and extensively studied problem in the field of combinatorial optimization. Because this problem is an NP-hard problem, practical large-scale instances cannot be solved by exact algorithms within acceptable computational times. ...