A self-learning particle swarm optimizer for global optimization problems

Copyright @ 2011 IEEE. All Rights Reserved. This article was made available through the Brunel Open Access Publishing Fund.Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.This work was supported by the Engineering and Physical Sciences Research Council of U.K. under Grants EP/E060722/1 and EP/E060722/2

Production and rescattering of strange baryons at SPS energies in a transport model with hadron potentials

A mean-field potential version of the Ultra-relativistic Quantum Molecular Dynamics (UrQMD) model is used to investigate the production of strange baryons, especially the $\Lambda$s and $\overline{\Lambda}$s, from heavy ion collisions at SPS energies. It is found that, with the consideration of both formed and pre-formed hadron potentials in UrQMD, the transverse mass and longitudinal rapidity distributions of experimental data of both $\Lambda$s and $\overline{\Lambda}$s can be quantitatively explained fairly well. Our investigation also shows that both the production mechanism and the rescattering process of hadrons play important roles in the final yield of strange baryons.Comment: 15 pages, 7 figure

X-Ray Spectral Variability in Cygnus X-1

Spectral variability in different energy bands of X-rays from Cyg X-1 in different states is studied with RXTE observations and time domain approaches. In the hard tail of energy spectrum above $\sim 10$ keV, average peak aligned shots are softer than the average steady emission and the hardness ratio decreases when the flux increases during a shot for all states. In regard to a soft band lower $\sim 10$ keV, the hardness in the soft state varies in an opposite way: it peaks when the flux of the shot peaks. For the hard and transition states, the hardness ratio in respect to a soft band during a shot is in general lower than that of the steady component and a sharp rise is observed at about the shot peak. For the soft state, the correlation coefficient between the intensity and hardness ratio in the hard tail is negative and decreases monotonically as the timescale increases from 0.01 s to 50 s, which is opposite to that in regard to a soft band. For the hard and transition states, the correlation coefficients are in general negative and have a trend of decrease with increasing timescale.Comment: 14 pages, 3 figures, accepted by Ap

Investigation to define the propagation characteristics of a finite amplitude acoustic pressure wave, phase 1 final report, 29 jun. 1964 - 29 jul. 1965

The contribution of high entropy production regions to the generation and propagation characteristics of a finite amplitude pressure is considered. Preliminary analysis indicates that, for nozzles where pressure rations are above critical, the predominant contribution may come from the shock layer formation in the exhaust region. Temperature effects indicate high dependence of the forcing function upon the initial temperature of the media

Least-Squares Approximation by Elements from Matrix Orbits Achieved by Gradient Flows on Compact Lie Groups

Let $S(A)$ denote the orbit of a complex or real matrix $A$ under a certain equivalence relation such as unitary similarity, unitary equivalence, unitary congruences etc. Efficient gradient-flow algorithms are constructed to determine the best approximation of a given matrix $A_0$ by the sum of matrices in $S(A_1), ..., S(A_N)$ in the sense of finding the Euclidean least-squares distance $$\min \{\|X_1+ ... + X_N - A_0\|: X_j \in S(A_j), j = 1, >..., N\}.$$ Connections of the results to different pure and applied areas are discussed

Learning algorithms for multi-class pattern classification and problems associated with on-line handwritten character recognition

On-line handwritten alphanumeric character recognition system and learning algorithm for multiclass pattern classificatio

Novel quantum phases of dipolar Bose gases in optical lattices

We investigate the quantum phases of polarized dipolar Bosons loaded into a two-dimensional square and three-dimensional cubic optical lattices. We show that the long-range and anisotropic nature of the dipole-dipole interaction induces a rich variety of quantum phases, including the supersolid and striped supersolid phases in 2D lattices, and the layered supersolid phase in 3D lattices.Comment: 4 pages, 4 figure