A comparative study of adaptive mutation operators for metaheuristics

Genetic algorithms (GAs) are a class of stochastic optimization methods inspired by the principles of natural evolution. Adaptation of strategy parameters and genetic operators has become an important and promising research area in GAs. Many researchers are applying adaptive techniques to guide the search of GAs toward optimum solutions. Mutation is a key component of GAs. It is a variation operator to create diversity for GAs. This paper investigates several adaptive mutation operators, including population level adaptive mutation operators and gene level adaptive mutation operators, for GAs and compares their performance based on a set of uni-modal and multi-modal benchmark problems. The experimental results show that the gene level adaptive mutation operators are usually more efficient than the population level adaptive mutation operators for GAs

An adaptive mutation operator for particle swarm optimization

Copyright @ 2008 MICParticle swarm optimization (PSO) is an effcient tool for optimization and search problems. However, it is easy to betrapped into local optima due to its in-formation sharing mechanism. Many research works have shown that mutation operators can help PSO prevent prema- ture convergence. In this paper, several mutation operators that are based on the global best particle are investigated and compared for PSO. An adaptive mutation operator is designed. Experimental results show that these mutation operators can greatly enhance the performance of PSO. The adaptive mutation operator shows great advantages over non-adaptive mutation operators on a set of benchmark test problems.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1

Strong lensing interferometry for compact binaries

We propose a possibility to improve the current precision measurements on compact binaries. When the orbital axis is almost perpendicular to our line of sight, a pulsar behind its companion can form two strong-lensing images. These images cannot be resolved, but we can use multi-wavelength interferometry to accurately determine the passage through superior conjunction. This method does not depend strongly on the stability of the pulse profile, and applies equally well to both slow and fast pulsars. We discuss the possible improvement this can bring to the bound on stochastic gravitational wave background and to determine black hole spin. We also discuss the possibility of discovering a suitable binary system by the Square Kilometer Array that our method can apply to.Comment: 5 pages, 5 figure

Q-operator and T-Q relation from the fusion hierarchy

We propose that the Baxter $Q$-operator for the spin-1/2 XXZ quantum spin chain is given by the $j\to \infty$ limit of the transfer matrix with spin-$j$ (i.e., $(2j+1)$-dimensional) auxiliary space. Applying this observation to the open chain with general (nondiagonal) integrable boundary terms, we obtain from the fusion hierarchy the $T$-$Q$ relation for {\it generic} values (i.e. not roots of unity) of the bulk anisotropy parameter. We use this relation to determine the Bethe Ansatz solution of the eigenvalues of the fundamental transfer matrix. This approach is complementary to the one used recently to solve the same model for the roots of unity case.Comment: Latex file, 12 pages; V2, misprints corrected and references adde

Analyzing big time series data in solar engineering using features and PCA

In solar engineering, we encounter big time series data such as the satellite-derived irradiance data and string-level measurements from a utility-scale photovoltaic (PV) system. While storing and hosting big data are certainly possible using today’s data storage technology, it is challenging to effectively and efficiently visualize and analyze the data. We consider a data analytics algorithm to mitigate some of these challenges in this work. The algorithm computes a set of generic and/or application-specific features to characterize the time series, and subsequently uses principal component analysis to project these features onto a two-dimensional space. As each time series can be represented by features, it can be treated as a single data point in the feature space, allowing many operations to become more amenable. Three applications are discussed within the overall framework, namely (1) the PV system type identification, (2) monitoring network design, and (3) anomalous string detection. The proposed framework can be easily translated to many other solar engineer applications

Probing the low-x structure of nuclear matter with diffractive hadron production in pA collisions

We argue that hadron production in coherent diffraction of proton on a heavy nucleus provides a very sensitive probe of the low-x QCD dynamics. This process probes the BFKL dynamics in proton and the non-linear gluon evolution in nucleus. We calculate the diffractive hadron production cross sections in the RHIC and LHC kinematic regions. To study the nuclear effects we introduce the diffractive nuclear modification factor. We show that unlike the nuclear modification factor for inclusive hadron production that has a very interesting dynamics at RHIC but is expected to be almost completely saturated at the LHC, the nuclear modification factor for diffractive production exhibits a non-trivial behavior both at RHIC and LHC.Comment: 18 pages, 7 figure