Genetic Algorithm-based Robot Path Planning

Nowadays, building an intelligent robot that able to move by itself from one location to another without collides with other obstacles is of interest in many applications. In the real world, condition of an environment is always unpredictable and changes with the existence of dynamic obstacles. This paper tends to propose an algorithm for robot path planning in a dynamic environment using Genetic algorithm (GA) technique. The proposed algorithm is able to find an optimum path for a robot and avoid any static and dynamic obstacles. The variation of the proposed algorithm is shown with the implementation of the algorithm in 4-way movement robot and 8-way movement robot. The simulation results show significant performance of the algorithm when compared with real optimum path

Genetic algorithm based DSP multiprocessor scheduling

This paper presents recent work on the application of genetic algorithms to the NP-complete problem of multiprocessor scheduling for audio DSP algorithms. The genetic algorithm is used to schedule algorithms written in the form of data flow graphs onto specified multiprocessor arrays. A unique chromosome representation technique is described and a number of application-specific genetic operators are introduced. Comparisons of the performance of the genetic algorithm technique with heuristic scheduling techniques show that the choice of the most suitable technique varies with the structure and complexity of the scheduling problem. Finally, techniques for combining heuristic and genetic algorithm scheduling techniques are discusse

SACOC: A spectral-based ACO clustering algorithm

The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, where ACO-based techniques have showed a great potential. At the same time, new clustering techniques that seek the continuity of data, specially focused on spectral-based approaches in opposition to classical centroid-based approaches, have attracted an increasing research interest–an area still under study by ACO clustering techniques. This work presents a hybrid spectral-based ACO clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach combines ACOC with the spectral Laplacian to generate a new search space for the algorithm in order to obtain more promising solutions. The new algorithm, called SACOC, has been compared against well-known algorithms (K-means and Spectral Clustering) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository

A GPU-based hyperbolic SVD algorithm

A one-sided Jacobi hyperbolic singular value decomposition (HSVD) algorithm, using a massively parallel graphics processing unit (GPU), is developed. The algorithm also serves as the final stage of solving a symmetric indefinite eigenvalue problem. Numerical testing demonstrates the gains in speed and accuracy over sequential and MPI-parallelized variants of similar Jacobi-type HSVD algorithms. Finally, possibilities of hybrid CPU--GPU parallelism are discussed.Comment: Accepted for publication in BIT Numerical Mathematic

Window-based Streaming Graph Partitioning Algorithm

In the recent years, the scale of graph datasets has increased to such a degree that a single machine is not capable of efficiently processing large graphs. Thereby, efficient graph partitioning is necessary for those large graph applications. Traditional graph partitioning generally loads the whole graph data into the memory before performing partitioning; this is not only a time consuming task but it also creates memory bottlenecks. These issues of memory limitation and enormous time complexity can be resolved using stream-based graph partitioning. A streaming graph partitioning algorithm reads vertices once and assigns that vertex to a partition accordingly. This is also called an one-pass algorithm. This paper proposes an efficient window-based streaming graph partitioning algorithm called WStream. The WStream algorithm is an edge-cut partitioning algorithm, which distributes a vertex among the partitions. Our results suggest that the WStream algorithm is able to partition large graph data efficiently while keeping the load balanced across different partitions, and communication to a minimum. Evaluation results with real workloads also prove the effectiveness of our proposed algorithm, and it achieves a significant reduction in load imbalance and edge-cut with different ranges of dataset

Enhanced Pilot-Based Spectrum Sensing Algorithm

In this paper, we develop an enhanced pilot-based spectrum sensing algorithm for cognitive radio. Unlike conventional pilot-based detectors which merely detect the presence of pilot signals, the proposed detector also utilizes the presence of the signal that carries the actual information. We analytically compare the performance of the proposed detector with the conventional one, and we show that the detection performance is significantly improved.Comment: 4 pages, 2 figures; published in Proc. IEEE Biennial Symps. on Commun. (QBSC'14), June 201

Level-Based Analysis of the Population-Based Incremental Learning Algorithm

The Population-Based Incremental Learning (PBIL) algorithm uses a convex combination of the current model and the empirical model to construct the next model, which is then sampled to generate offspring. The Univariate Marginal Distribution Algorithm (UMDA) is a special case of the PBIL, where the current model is ignored. Dang and Lehre (GECCO 2015) showed that UMDA can optimise LeadingOnes efficiently. The question still remained open if the PBIL performs equally well. Here, by applying the level-based theorem in addition to Dvoretzky--Kiefer--Wolfowitz inequality, we show that the PBIL optimises function LeadingOnes in expected time $\mathcal{O}(n\lambda \log \lambda + n^2)$ for a population size $\lambda = \Omega(\log n)$, which matches the bound of the UMDA. Finally, we show that the result carries over to BinVal, giving the fist runtime result for the PBIL on the BinVal problem.Comment: To appea