16252151
A Topology Optimization for Truss Based on Improved Group Search Optimizer
In this paper, a novel optimization algorithm, named group search optimizer (GSO) with two improvements, is used to truss structure topology optimization. And the improved methods are using harmony memory and adhering to the boundary which can improve the algorithm obviously. Then a simple and effective topology method, discretization of topology variables is incorporated with improved GSO to make sure that the topology optimization works well. In the end of the paper, two topology optimal models, a planar truss and a spatial truss, are used to test this topology methods based on the improved GSO. The results show that the method with improved GSO is feasible and robust for truss topology optimization.
9882029
The Group Search Optimizer and its Application on Truss Structure Design
This paper introduces a novel optimization algorithm, group search optimization (GSO) algorithm and its implementation method is presented in detail. The GSO is used to investigate the planar and space truss structures with continuous variables and is tested by two truss optimization problems. The optimization results are compared with that of the particle swarm optimization (PSO) algorithm, the particle swarm optimization with passive congregation (PSOPC) and the heuristic particle swarm optimizer (HPSO) algorithm. Results from the two tested cases illustrate the ability of the GSO algorithm to find the optimal results, which are better than that of the PSO and PSOPC, while are at the same level of that of HPSO optimization method. Meanwhile, the results also show that the GSO algorithm maintains a preferable convergence accuracy among these four algorithms.
14281509
A Quick Group Search Optimizer with Passive Congregation and its Convergence Analysis
This paper presents a quick group search optimizer with passive congregation (QGSOPC) for optimum design of pin connected structure. The algorithm is based on the quick group search optimizer (QGSO). The QGSOPC algorithm is verified and compared with the HPSO, GSO and QGSO algorithm used for the designs of two planar and spatial truss structures. The results show that the QGSOPC algorithm not only has preferable convergence rate and accuracy, but also it has the best stability of convergence rate and accuracy. It is desired for QGSOPC to be used more effectively for practical structural optimal design problems.
18928608
An Improved Particle Swarm Optimizer for Truss Structure Optimization
This paper presents an improved particle swarm optimizer (IPSO) for solving truss structure optimization problems. The algorithm is based on the particle swarm optimizer with passive congregation (PSOPC) and a harmony search (HS) scheme. It handles the problem-specified constraints using a 'fly-back mechanism' method and the variables' constraints using the harmony search scheme. The IPSO is tested on a planar truss structure optimization problem and is compared with the PSO and the PSOPC algorithm respectively. The result shows that the IPSO method presented in this paper is able to accelerate the convergence rate effectively and has the fastest convergence rate among these three other algorithms
35354920
Color text image binarization based on binary texture analysis
In this paper, a novel binarization algorithm for color text images is presented. This algorithm effectively integrates color clustering and binary texture analysis, and is capable of handling situations with complex backgrounds. In this algorithm, dimensionality reduction and graph theoretical clustering are first employed. As the result, binary images related to clusters can be obtained. Binary texture analysis is then performed on each candidate binary image. Two kinds of effective texture features, run-length histogram and spatial-size distribution related respectively, are extracted and explored. Cooperating with an LDA classifier, the optimal candidate of the best binarization effect is obtained. Experiments with images collected from Internet has been carried out and compared with existing techniques, both show the effectiveness of the algorithm.
5390173
A Self-supervised Learning Framework for Classifying Microarray Gene Expression Data
It is important to develop computational methods that can effectively resolve two intrinsic problems in microarray data: high dimensionality and small sample size. In this paper, we propose a self-supervised learning framework for classifying microarray gene expression data using Kernel Discriminant-EM (KDEM) algorithm. This framework applies self-supervised learning techniques in an optimal nonlinear discriminating subspace. It efficiently utilizes a large set of unlabeled data to compensate for the insufficiency of a small set of labeled data and it extends linear algorithm in DEM to kernel algorithm to handle nonlinearly separable data in a lower dimensional space. Extensive experiments on the Plasmodium falciparum expression profiles show the promising performance of the approach.
17180210
An efficient distributed algorithm for constructing delay- and degree-bounded application-level multicast tree
In this paper we investigate the problem of finding a delay- and degree-bounded maximum sum of nodes application level multicast tree. We then proved the problem is NP-hard, and its relationship with the well-studied degree-bounded minimum maximum-delay multicast tree problem. We propose a distributed algorithm - OverStream, which adopts PPAF heuristic for father node to choose optimal child nodes based on multiple network parameters. Simulation results show that our algorithm is better than OMNI, SpreadIt, HMTP, and Zigzag in general.
15290782
The Performance Analysis of an Improved Robust Beamforming Algorithm
The conventional minimum variance beamformer (MVB) is known to have better resolution and much better interference rejection capability when the array steering vector is accurately known. However, the major problem of the MVB is that it lacks robustness against the unknown array steering vector errors. Therefore, diagonal loading and its extended versions have been a popular approach to improve the robustness of MVB. In this paper, we provide an improved robust MVB (RMVB) algorithm, which has been derived by enforcing a spherical uncertainty set constraint on the array steering vector. The new algorithm has reasonable computation complexity and more numerical stability. Simulation results show that the new algorithm is effective in the presence of array steering vector errors.
13252008
Anode-ALM: An Aggregation-Based Application Level Multicast for Media Streaming
Building application level multicast (ALM) for media streaming, we had better keep the invariability of source to-end delay (S2EDelay) of any node on the tree, so as to avoid delay jitter problem. In this paper, we proposed an aggregation-based ALM architecture, which aggregates the unstable nodes within a local area into a relative stable aggregation node Anode, and then builds tree by Anodes. Anode shields node’s dynamicity inside and behaves as a stable one outside. To build the architecture, we first proposed an Anode structure by n-dimension anti-cube, and proposed a distributed algorithm to bind the logic view and physical view of an Anode dynamically by the differentiation and assimilation operations, which maintain the integrality of the logic view while the physical view changes dynamically. Experiment results indicate that our algorithm can keep theS2EDelay invariability of the built ALM tree effectively.