Force-imitated particle swarm optimization using the near-neighbor effect for locating multiple optima

Copyright @ Elsevier Inc. All rights reserved.Multimodal optimization problems pose a great challenge of locating multiple optima simultaneously in the search space to the particle swarm optimization (PSO) community. In this paper, the motion principle of particles in PSO is extended by using the near-neighbor effect in mechanical theory, which is a universal phenomenon in nature and society. In the proposed near-neighbor effect based force-imitated PSO (NN-FPSO) algorithm, each particle explores the promising regions where it resides under the composite forces produced by the “near-neighbor attractor” and “near-neighbor repeller”, which are selected from the set of memorized personal best positions and the current swarm based on the principles of “superior-and-nearer” and “inferior-and-nearer”, respectively. These two forces pull and push a particle to search for the nearby optimum. Hence, particles can simultaneously locate multiple optima quickly and precisely. Experiments are carried out to investigate the performance of NN-FPSO in comparison with a number of state-of-the-art PSO algorithms for locating multiple optima over a series of multimodal benchmark test functions. The experimental results indicate that the proposed NN-FPSO algorithm can efficiently locate multiple optima in multimodal fitness landscapes.This work was supported in part by the Key Program of National Natural Science Foundation (NNSF) of China under Grant 70931001, Grant 70771021, and Grant 70721001, the National Natural Science Foundation (NNSF) of China for Youth under Grant 61004121, Grant 70771021, the Science Fund for Creative Research Group of NNSF of China under Grant 60821063, the PhD Programs Foundation of Ministry of Education of China under Grant 200801450008, and in part by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1 and Grant EP/E060722/2

Goldstone mode singularities in O(n) models

Monte Carlo (MC) analysis of the Goldstone mode singularities for the transverse and the longitudinal correlation functions, behaving as G_{\perp}(k) \simeq ak^{-\lambda_{\perp}} and G_{\parallel}(k) \simeq bk^{-\lambda_{\parallel}} in the ordered phase at k -> 0, is performed in the three-dimensional O(n) models with n=2, 4, 10. Our aim is to test some challenging theoretical predictions, according to which the exponents \lambda_{\perp} and \lambda_{\parallel} are non-trivial (3/2<\lambda_{\perp}<2 and 0<\lambda_{\parallel}<1 in three dimensions) and the ratio bM^2/a^2 (where M is a spontaneous magnetization) is universal. The trivial standard-theoretical values are \lambda_{\perp}=2 and \lambda_{\parallel}=1. Our earlier MC analysis gives \lambda_{\perp}=1.955 \pm 0.020 and \lambda_{\parallel} about 0.9 for the O(4) model. A recent MC estimation of \lambda_{\parallel}, assuming corrections to scaling of the standard theory, yields \lambda_{\parallel} = 0.69 \pm 0.10 for the O(2) model. Currently, we have performed a similar MC estimation for the O(10) model, yielding \lambda_{\perp} = 1.9723(90). We have observed that the plot of the effective transverse exponent for the O(4) model is systematically shifted down with respect to the same plot for the O(10) model by \Delta \lambda_{\perp} = 0.0121(52). It is consistent with the idea that 2-\lambda_{\perp} decreases for large $n$ and tends to zero at n -> \infty. We have also verified and confirmed the expected universality of bM^2/a^2 for the O(4) model, where simulations at two different temperatures (couplings) have been performed.Comment: 8 pages, 5 figure

Design and Analysis of Honeycomb Structures with Advanced Cell Walls

Honeycomb structures are widely used in engineering applications. This work consists of three parts, in which three modified honeycombs are designed and analyzed. The objectives are to obtain honeycomb structures with improved specific stiffness and specific buckling resistance while considering the manufacturing feasibility. The objective of the first part is to develop analytical models for general case honeycombs with non-linear cell walls. Using spline curve functions, the model can describe a wide range of 2-D periodic structures with nonlinear cell walls. The derived analytical model is verified by comparing model predictions with other existing models, finite element analysis (FEA) and experimental results. Parametric studies are conducted by analytical calculation and finite element modeling to investigate the influences of the spline waviness on the homogenized properties. It is found that, comparing to straight cell walls, spline cell walls have increased out-of-plane buckling resistance per unit weight, and the extent of such improvement depends on the distribution of the spline’s curvature. The second part of this research proposes a honeycomb with laminated composite cell walls, which offer a wide selection of constituent materials and improved specific stiffness. Analytical homogenization is established and verified by FEA comparing the mechanical responses of a full-detailed honeycomb and a solid cuboid assigned with the calculated homogenization properties. The results show that the analytical model is accurate at a small computational cost. Parametric studies reveal nonlinear relationships between the ply thickness and the effective properties, based on which suggestions are made for property optimizations. The third part studies honeycomb structures with perforated cell walls. The homogenized properties of this new honeycomb are analytically modeled and investigated by finite element modeling. It is found that comparing to conventional honeycombs, honeycombs with perforated cell walls demonstrate enhanced in-plane stiffness, out-of-plane bending rigidity, out-of-plane compressive buckling stress, approximately the same out-of-plane shear buckling strength, and reduced out-of-plane stiffness. For the future design, empirical formulas, based on finite element results and expressed as functions of the perforation size, are derived for the mechanical properties and verified by mechanical tests conducted on a series of 3D printed perforated honeycomb specimens

Convolutional Networks for Object Category and 3D Pose Estimation from 2D Images

Current CNN-based algorithms for recovering the 3D pose of an object in an image assume knowledge about both the object category and its 2D localization in the image. In this paper, we relax one of these constraints and propose to solve the task of joint object category and 3D pose estimation from an image assuming known 2D localization. We design a new architecture for this task composed of a feature network that is shared between subtasks, an object categorization network built on top of the feature network, and a collection of category dependent pose regression networks. We also introduce suitable loss functions and a training method for the new architecture. Experiments on the challenging PASCAL3D+ dataset show state-of-the-art performance in the joint categorization and pose estimation task. Moreover, our performance on the joint task is comparable to the performance of state-of-the-art methods on the simpler 3D pose estimation with known object category task