325,708 research outputs found
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
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
Convergence of the Lasserre Hierarchy of SDP Relaxations for Convex Polynomial Programs without Compactness
The Lasserre hierarchy of semidefinite programming (SDP) relaxations is an
effective scheme for finding computationally feasible SDP approximations of
polynomial optimization over compact semi-algebraic sets. In this paper, we
show that, for convex polynomial optimization, the Lasserre hierarchy with a
slightly extended quadratic module always converges asymptotically even in the
face of non-compact semi-algebraic feasible sets. We do this by exploiting a
coercivity property of convex polynomials that are bounded below. We further
establish that the positive definiteness of the Hessian of the associated
Lagrangian at a saddle-point (rather than the objective function at each
minimizer) guarantees finite convergence of the hierarchy. We obtain finite
convergence by first establishing a new sum-of-squares polynomial
representation of convex polynomials over convex semi-algebraic sets under a
saddle-point condition. We finally prove that the existence of a saddle-point
of the Lagrangian for a convex polynomial program is also necessary for the
hierarchy to have finite convergence.Comment: 17 page
QED Penguin Contributions To Isospin Splittings of Heavy-Light Quark Systems
Recent experiments show that the isospin-violating mass splitting of the B
mesons is very small, but the best fits with a QCD sum rule analysis give a
splitting of at least 1.0 MeV. The isospin-violating mass splittings of the
charmed mesons, on the other hand, are in agreement with experiment. In this
letter we show that the inclusion of 2 kind QED penguin diagrams can
account for this discrepancy within the errors in the QCD sum rule method.Comment: 9 pages, latex, 2 figure
Macroscopic Black Holes, Microscopic Black Holes and Noncommutative Membrane
We study the stretched membrane of a black hole as consisting of a perfect
fluid. We find that the pressure of this fluid is negative and the specific
heat is negative too. A surprising result is that if we are to assume the fluid
be composed of some quanta, then the dispersion relation of the fundamental
quantum is , with at the scale of the Planck mass. There are two
possible interpretation of this dispersion relation, one is the noncommutative
spacetime on the stretched membrane, another is that the fundamental quantum is
microscopic black holes.Comment: 10 pages, harvmac; v2: refs. adde
Recommended from our members
Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme
The controlled outflows from a reservoir or dam are highly dependent on the decisions made by the reservoir operators, instead of a natural hydrological process. Difference exists between the natural upstream inflows to reservoirs and the controlled outflows from reservoirs that supply the downstream users. With the decision maker's awareness of changing climate, reservoir management requires adaptable means to incorporate more information into decision making, such as water delivery requirement, environmental constraints, dry/wet conditions, etc. In this paper, a robust reservoir outflow simulation model is presented, which incorporates one of the well-developed data-mining models (Classification and Regression Tree) to predict the complicated human-controlled reservoir outflows and extract the reservoir operation patterns. A shuffled cross-validation approach is further implemented to improve CART's predictive performance. An application study of nine major reservoirs in California is carried out. Results produced by the enhanced CART, original CART, and random forest are compared with observation. The statistical measurements show that the enhanced CART and random forest overperform the CART control run in general, and the enhanced CART algorithm gives a better predictive performance over random forest in simulating the peak flows. The results also show that the proposed model is able to consistently and reasonably predict the expert release decisions. Experiments indicate that the release operation in the Oroville Lake is significantly dominated by SWP allocation amount and reservoirs with low elevation are more sensitive to inflow amount than others
- …