A survey: Particle Swarm Optimization based algorithms to solve premature convergence problem

Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method based on the social behaviors of birds flocking or fish schooling. Although, PSO is represented in solving many well-known numerical test problems, but it suffers from the premature convergence. A number of basic variations have been developed due to solve the premature convergence problem and improve quality of solution founded by the PSO. This study presents a comprehensive survey of the various PSO-based algorithms. As part of this survey, the authors have included a classification of the approaches and they have identify the main features of each proposal. In the last part of the study, some of the topics within this field that are considered as promising areas of future research are listed.

Balancing exploration and exploitation in particle swarm optimization on search tasking

In this study we present a combinatorial optimization method based on particle swarm optimization and local search algorithm on the multi-robot search system. Under this method, in order to create a balance between exploration and exploitation and guarantee the global convergence, at each iteration step if the distance between target and the robot become less than specific measure then a local search algorithm is performed. The local search encourages the particle to explore the local region beyond to reach the target in lesser search time. Experimental results obtained in a simulated environment show that biological and sociological inspiration could be useful to meet the challenges of robotic applications that can be described as optimization problems.

Precipitation In Small Systems--II. Mean Field Equations More Effective Than Population Balance

Part I (Manjunath et al., 1994, Chem. Engng Sci. 49, 1451-1463) of this paper showed that the random particle numbers and size distributions in precipitation processes in very small drops obtained by stochastic simulation techniques deviate substantially from the predictions of conventional population balance. The foregoing problem is considered in this paper in terms of a mean field approximation obtained by applying a first-order closure to an unclosed set of mean field equations presented in Part I. The mean field approximation consists of two mutually coupled partial differential equations featuring (i) the probability distribution for residual supersaturation and (ii) the mean number density of particles for each size and supersaturation from which all average properties and fluctuations can be calculated. The mean field equations have been solved by finite difference methods for (i) crystallization and (ii) precipitation of a metal hydroxide both occurring in a single drop of specified initial supersaturation. The results for the average number of particles, average residual supersaturation, the average size distribution, and fluctuations about the average values have been compared with those obtained by stochastic simulation techniques and by population balance. This comparison shows that the mean field predictions are substantially superior to those of population balance as judged by the close proximity of results from the former to those from stochastic simulations. The agreement is excellent for broad initial supersaturations at short times but deteriorates progressively at larger times. For steep initial supersaturation distributions, predictions of the mean field theory are not satisfactory thus calling for higher-order approximations. The merit of the mean field approximation over stochastic simulation lies in its potential to reduce expensive computation times involved in simulation. More effective computational techniques could not only enhance this advantage of the mean field approximation but also make it possible to use higher-order approximations eliminating the constraints under which the stochastic dynamics of the process can be predicted accurately.

Mesh-free approximations via the error reproducing kernel method and applications to nonlinear systems developing shocks

Error estimates for the error reproducing kernel method (ERKM) are provided. The ERKM is a mesh-free functional approximation scheme [A. Shaw, D. Roy, A NURBS-based error reproducing kernel method with applications in solid mechanics, Computational Mechanics (2006), to appear (available online)], wherein a targeted function and its derivatives are first approximated via non-uniform rational B-splines (NURBS) basis function. Errors in the NURBS approximation are then reproduced via a family of non-NURBS basis functions, constructed using a polynomial reproduction condition, and added to the NURBS approximation of the function obtained in the first step. In addition to the derivation of error estimates, convergence studies are undertaken for a couple of test boundary value problems with known exact solutions. The ERKM is next applied to a one-dimensional Burgers equation where, time evolution leads to a breakdown of the continuous solution and the appearance of a shock. Many available mesh-free schemes appear to be unable to capture this shock without numerical instability. However, given that any desired order of continuity is achievable through NURBS approximations, the ERKM can even accurately approximate functions with discontinuous derivatives. Moreover, due to the variation diminishing property of NURBS, it has advantages in representing sharp changes in gradients. This paper is focused on demonstrating this ability of ERKM via some numerical examples. Comparisons of some of the results with those via the standard form of the reproducing kernel particle method (RKPM) demonstrate the relative numerical advantages and accuracy of the ERKM.

Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures

We present a new, generic method/model for multi-objective design optimization of laminated composite components using a novel multi-objective optimization algorithm developed on the basis of the Quantum behaved Particle Swarm Optimization (QPSO) paradigm. QPSO is a co-variant of the popular Particle Swarm Optimization (PSO) and has been developed and implemented successfully for the multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are - the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria; Failure Mechanism based Failure criteria, Maximum stress failure criteria and the Tsai-Wu Failure criteria. The optimization method is validated for a number of different loading configurations - uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences as well as fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. Also, the performance of QPSO is compared with the conventional PSO.

Lipid assisted membrane entry in specific phospholipid targeted protein systems

The purpose of this thesis project is to study changes in the physical state of cell membranes during cell entry, including how these changes are connected to the presence of ceramide. The role of enzymatical manipulation of lipids in bacterial internalization is also studied. A novel technique, where a single giant vesicle is chosen under the microscope and an enzyme coupled-particle attached to the micromanipulator pipette towards the vesicle, is used. Thus, the enzymatic reaction on the membrane of the giant vesicle can be followed in real-time. The first aim of this study is to develop a system where the localized sphingomyelinase membrane interaction could be observed on the surface of the giant vesicle and the effects could be monitored with microscopy. Domain formation, which resembles acid sphingomyelinase (ASMase), causes CD95 clustering in the cell membrane due to ceramide production (Grassmé et al., 2001a; Grassmé et al., 2001b) and the formation of small vesicles inside the manipulated giant vesicle is observed. Sphingomyelinase activation has also been found to be an important factor in the bacterial and viral invasion process in nonphagocytic cells (Grassmé et al., 1997; Jan et al., 2000). Accordingly, sphingomyelinase reactions in the cell membrane might also give insight into bacterial or viral cellular entry events. We found sphingomyelinase activity in Chlamydia pneumonia elementarybodies (EBs). Interestingly, the bacterium enters host cells by endocytosis but the internalization mechanism of Chlamydia is unknown. The hypothesis is that sphingomyelin is needed for host cell entry in the infection of C. pneumonia. The second project focuses on this subject. The goal of the third project is to study a role of phosphatidylserine as a target for a membrane binding protein. Phosphatidylserine is chosen because of its importance in fusion processes. This will be another example for the importance of lipids in cell targeting, internalization, and externalization.

A semi-analytical particle filter for identification of nonlinear oscillators

Particle filters find important applications in the problems of state and parameter estimations of dynamical systems of engineering interest. Since a typical filtering algorithm involves Monte Carlo simulations of the process equations, sample variance of the estimator is inversely proportional to the number of particles. The sample variance may be reduced if one uses a Rao-Blackwell marginalization of states and performs analytical computations as much as possible. In this work, we propose a semi-analytical particle filter, requiring no Rao-Blackwell marginalization, for state and parameter estimations of nonlinear dynamical systems with additively Gaussian process/observation noises. Through local linearizations of the nonlinear drift fields in the process/observation equations via explicit Ito-Taylor expansions, the given nonlinear system is transformed into an ensemble of locally linearized systems. Using the most recent observation, conditionally Gaussian posterior density functions of the linearized systems are analytically obtained through the Kalman filter. This information is further exploited within the particle filter algorithm for obtaining samples from the optimal posterior density of the states. The potential of the method in state/parameter estimations is demonstrated through numerical illustrations for a few nonlinear oscillators. The proposed filter is found to yield estimates with reduced sample variance and improved accuracy vis-a-vis results from a form of sequential importance sampling filter.

Nonlinear structural dynamical system identification using adaptive particle filters

The problem of identifying parameters of nonlinear vibrating systems using spatially incomplete, noisy, time-domain measurements is considered. The problem is formulated within the framework of dynamic state estimation formalisms that employ particle filters. The parameters of the system, which are to be identified, are treated as a set of random variables with finite number of discrete states. The study develops a procedure that combines a bank of self-learning particle filters with a global iteration strategy to estimate the probability distribution of the system parameters to be identified. Individual particle filters are based on the sequential importance sampling filter algorithm that is readily available in the existing literature. The paper develops the requisite recursive formulary for evaluating the evolution of weights associated with system parameter states. The correctness of the formulations developed is demonstrated first by applying the proposed procedure to a few linear vibrating systems for which an alternative solution using adaptive Kalman filter method is possible. Subsequently, illustrative examples on three nonlinear vibrating systems, using synthetic vibration data, are presented to reveal the correct functioning of the method. (c) 2007 Elsevier Ltd. All rights reserved.

First observation of the decay Bs --> Ds K and measurement of the ratio of branching fractions Br(Bs --> DsK)/Br(Bs --> Ds pi)

A combined mass and particle identification fit is used to make the first observation of the decay Bs --> Ds K and measure the branching fraction of Bs --> Ds K relative to Bs --> Ds pi. This analysis uses 1.2 fb^-1 integrated luminosity of pbar-p collisions at sqrt(s) = 1.96 TeV collected with the CDF II detector at the Fermilab Tevatron collider. We observe a Bs --> Ds K signal with a statistical significance of 8.1 sigma and measure Br(Bs --> Ds K)/Br(Bs --> Ds pi) = 0.097 +- 0.018(stat) +- 0.009(sys).

First measurement of the ratio of branching fractions B(Λb0→Λc+μ-ν̅ μ)/B(Λb0→Λc+π-)

This article presents the first measurement of the ratio of branching fractions B(Λb0→Λc+μ-ν̅ μ)/B(Λb0→Λc+π-). Measurements in two control samples using the same technique B(B̅ 0→D+μ-ν̅ μ)/B(B̅ 0→D+π-) and B(B̅ 0→D*(2010)+μ-ν̅ μ)/B(B̅ 0→D*(2010)+π-) are also reported. The analysis uses data from an integrated luminosity of approximately 172  pb-1 of pp̅ collisions at √s=1.96  TeV, collected with the CDF II detector at the Fermilab Tevatron. The relative branching fractions are measured to be B(Λb0→Λc+μ-ν̅ μ)/B(Λb0→Λc+π-)=16.6±3.0(stat)±1.0(syst)+2.6/-3.4(PDG)±0.3(EBR), B(B̅ 0→D+μ-ν̅ μ)/B(B̅ 0→D+π-)= 9.9±1.0(stat)±0.6(syst)±0.4(PDG)±0.5(EBR), and B(B̅ 0→D*(2010)+μ-ν̅ μ)/B(B̅ 0→D*(2010)+π-)=16.5±2.3(stat)± 0.6(syst)±0.5(PDG)±0.8(EBR). The uncertainties are from statistics (stat), internal systematics (syst), world averages of measurements published by the Particle Data Group or subsidiary measurements in this analysis (PDG), and unmeasured branching fractions estimated from theory (EBR), respectively. This article also presents measurements of the branching fractions of four new Λb0 semileptonic decays: Λb0→Λc(2595)+μ-ν̅ μ, Λb0→Λc(2625)+μ-ν̅ μ, Λb0→Σc(2455)0π+μ-ν̅ μ, and Λb0→Σc(2455)++π-μ-ν̅ μ, relative to the branching fraction of the Λb0→Λc+μ-ν̅ μ decay. Finally, the transverse-momentum distribution of Λb0 baryons produced in pp̅ collisions is measured and found to be significantly different from that of B̅ 0 mesons, which results in a modification in the production cross-section ratio σΛb0/σB̅ 0 with respect to the CDF I measurement.

First Observation of B̅ s0→Ds±K∓ and Measurement of the Ratio of Branching Fractions B(B̅ s0→Ds±K∓)/B(B̅ s0→Ds+π-)

A combined mass and particle identification fit is used to make the first observation of the decay B̅ s0→Ds±K∓ and measure the branching fraction of B̅ s0→Ds±K∓ relative to B̅ s0→Ds+π-. This analysis uses 1.2  fb-1 integrated luminosity of pp̅ collisions at √s=1.96  TeV collected with the CDF II detector at the Fermilab Tevatron collider. We observe a B̅ s0→Ds±K∓ signal with a statistical significance of 8.1σ and measure B(B̅ s0→Ds±K∓)/B(B̅ s0→Ds+π-)=0.097±0.018(stat)±0.009(syst).

First Observation of B̅ s0→Ds±K∓ and Measurement of the Ratio of Branching Fractions B(B̅ s0→Ds±K∓)/B(B̅ s0→Ds+π-)

A combined mass and particle identification fit is used to make the first observation of the decay Bs --> Ds K and measure the branching fraction of Bs --> Ds K relative to Bs --> Ds pi. This analysis uses 1.2 fb^-1 integrated luminosity of pbar-p collisions at sqrt(s) = 1.96 TeV collected with the CDF II detector at the Fermilab Tevatron collider. We observe a Bs --> Ds K signal with a statistical significance of 8.1 sigma and measure Br(Bs --> Ds K)/Br(Bs --> Ds pi) = 0.097 +- 0.018(stat) +- 0.009(sys).

Direct detection of atmospheric particle formation using the Neutral cluster and Air Ion Spectrometer

Aerosol particles play an important role in the Earth s atmosphere and in the climate system: they scatter and absorb solar radiation, facilitate chemical processes, and serve as seeds for cloud formation. Secondary new particle formation (NPF) is a globally important source of these particles. Currently, the mechanisms of particle formation and the vapors participating in this process are, however, not truly understood. In order to fully explain atmospheric NPF and subsequent growth, we need to measure directly the very initial steps of the formation processes. This thesis investigates the possibility to study atmospheric particle formation using a recently developed Neutral cluster and Air Ion Spectrometer (NAIS). First, the NAIS was calibrated and intercompared, and found to be in good agreement with the reference instruments both in the laboratory and in the field. It was concluded that NAIS can be reliably used to measure small atmospheric ions and particles directly at the sizes where NPF begins. Second, several NAIS systems were deployed simultaneously at 12 European measurement sites to quantify the spatial and temporal distribution of particle formation events. The sites represented a variety of geographical and atmospheric conditions. The NPF events were detected using NAIS systems at all of the sites during the year-long measurement period. Various particle formation characteristics, such as formation and growth rates, were used as indicators of the relevant processes and participating compounds in the initial formation. In a case of parallel ion and neutral cluster measurements, we also estimated the relative contribution of ion-induced and neutral nucleation to the total particle formation. At most sites, the particle growth rate increased with the increasing particle size indicating that different condensing vapors are participating in the growth of different-sized particles. The results suggest that, in addition to sulfuric acid, organic vapors contribute to the initial steps of NPF and to the subsequent growth, not just later steps of the particle growth. As a significant new result, we found out that the total particle formation rate varied much more between the different sites than the formation rate of charged particles. The results infer that the ion-induced nucleation has a minor contribution to particle formation in the boundary layer in most of the environments. These results give tools to better quantify the aerosol source provided by secondary NPF in various environments. The particle formation characteristics determined in this thesis can be used in global models to assess NPF s climatic effects.

Investigations on the melting and bending modulus of polymer grafted bilayers using dissipative particle dynamics

Understanding the influence of polymer grafted bilayers on the physicomechanical properties of lipid membranes is important while developing liposomal based drug delivery systems. The melting characteristics and bending moduli of polymer grafted bilayers are investigated using dissipative particle dynamics simulations as a function of the amount of grafted polymer and lipid tail length. Simulations are carried out using a modified Andersen barostat, whereby the membrane is maintained in a tensionless state. For lipids made up of four to six tail beads, the transition from the low temperature L-beta phase to the L-alpha phase is lowered only above a grafting fraction of G(f)=0.12 for polymers made up of 20 beads. Below G(f)=0.12 small changes are observed only for the HT4 bilayer. The bending modulus of the bilayers is obtained as a function of G(f) from a Fourier analysis of the height fluctuations. Using the theory developed by Marsh Biochim. Biophys. Acta 1615, 33 (2003)] for polymer grafted membranes, the contributions to the bending modulus due to changes arising from the grafted polymer and bilayer thinning are partitioned. The contributions to the changes in kappa from bilayer thinning were found to lie within 11% for the lipids with four to six tail beads, increasing to 15% for the lipids containing nine tail beads. The changes in the area stretch modulus were also assessed and were found to have a small influence on the overall contribution from membrane thinning. The increase in the area per head group of the lipids was found to be consistent with the scalings predicted by self-consistent mean field results. (C) 2010 American Institute of Physics.

New results on systems with second-class constraints

We show how, for large classes of systems with purely second-class constraints, further information can be obtained about the constraint algebra. In particular, a subset consisting of half the full set of constraints is shown to have vanishing mutual brackets. Some other constraint brackets are also shown to be zero. The class of systems for which our results hold includes examples from non-relativistic particle mechanics as well as relativistic field theory. The results are derived at the classical level for Poisson brackets, but in the absence of commutator anomalies the same results will hold for the commutators of the constraint operators in the corresponding quantised theories.