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A more efficient pre-initializing strategy of PSO algorithm: Multi-Period Particle Swarm Optimization (MP-PSO) is proposed. The process is divided into two periods: pre-initialization and post-optimization. The former is determined to find a better local solution to initialize the next period instead of standard uniform randomness. In order to explore further, adaptive escaping weight is adopted to...
The matrix completion problem addresses the recovery of a low-rank matrix from a subset of its entries. In this paper, we analyze rank-r matrix completion algorithm based on the rank-r singular value decomposition (SVD). We introduce the doubly-restricted contraction constant (DRCC), a characteristic of a matrix, which predicts the feasibility of matrix recovery from a subset of its entries. We establish...
For a connected network, consensus based algorithms guarantee that local estimates are iteratively shared and refined among neighbors to reach the same weighted average on all nodes. Parameter estimation for linear models are common problems where average consensus are routinely adopted to mimic a centralized approach without the need of any fusion center. Convergence speed, accuracy and the amount...
In this work we discuss the accuracy of some approximations for fractional derivatives. We analyze the cases in which the fractional derivatives are defined in a domain different from the real line and show how the order of accuracy can be affected by the presence of boundaries. We observe the accuracy can be easily lost, in particular near the boundary points, unless some conditions of regularity...
Two computation iterative algorithm are studied to solve the coupled game algebraic Riccati equation (CGARE) associated with the optimal H∞ control problems for a class of Markovian jumping linear systems (MJLSs). The two iterative algorithms are based on the framework of Kleinman iteration algorithm. At first, the direct parallel Kleinman iteration algorithm is proposed and the convergence of the...
By introducing the fractional-order difference into the updating formulas of the velocity and position, fractional-order particle swarm optimization algorithm is proposed. The effects on the convergence rate and accuracy are analyzed, by introducing fractional-orders in the updating formulas for the velocity and position. Moreover, the linear increasing methods to adjust the fractional-orders are...
A new method to achieve most luminous efficacy of LED mixing color is developed in this paper, and then algorithm simulation, samples design and validation of the results have been carried out. Compared with the traditional manual LED proportion calculation method, new LED proportion calculation method is optimized by artificial fish swarm algorithm (AFSA). As for AFSA with slow rate of convergence...
In cooperative localization, distributed algorithm is more attractive than its centralized counterpart due to the low complexity and robustness. However, the position ambiguity of cooperative mobile terminal (MT) is usually neglected by related works, causing the loss of accuracy in distributed algorithms. In this paper, we establish an element-wise-weighted total least-squares (EW-TLS) model for...
We present a novel tool called XGossip for Internet-scale cardinality estimation of XPath queries over distributed XML data. XGossip relies on the principle of gossip, is scalable, decentralized, and can cope with network churn and failures. It employs a novel divide-and-conquer strategy for load balancing and reducing the overall network bandwidth consumption. It has a strong theoretical underpinning...
The automated tracking of social insects, such as ants, can efficiently provide unparalleled amounts of data for the of study complex group behaviors. However, a high level of occlusion along with similarity in appearance and motion can cause the tracking to drift to an incorrect ant. In this paper, we reduce drifting by using occlusion to identify incorrect ants and prevent the tracking from drifting...
The cutting stock problem (CSP) is an important problem in class of combinatorial optimization problems because of its NP-hard nature. Cutting of the required material from available stock with minimum wastage is a challenging process in many manufacturing industries such as rod industry, paper industry, textile industry, wood industry, plastic and leather manufacturing industry etc. The objective...
Virtualization is a double-edged sword in large scale cloud computing environments. On one hand, virtualization provides a logical and unified view of the underlying cloud resources to facilitate more efficient resource utilization and to support multi-tenancy. On the other hand, virtualization introduces additional layers of indirections which make the virtual-to-physical resource mapping relationship...
The improvised Particle Swarm Optimization (PSO) Algorithm offers better search efficiency than conventional PSO algorithm. It provides an efficient technique to obtain the best optimized result in the search space. This algorithm ensures a faster rate of convergence to the desired solution whose precision can be preset by the user. The inertia parameter is varied linearly with iteration number, which...
3-D near field source localization is one of the hot areas of research which has found direct applications in Radar, Sonar and digital communication. In this work, we propose hybrid meta-heuristic based algorithm to estimate jointly and efficiently the range, elevation angle and amplitude of the near field sources impinging on uniform linear array. For this, first the Differential evolution (DE) and...
An Adjustable Frequency Bat Algorithm (AFBA) is proposed to improve solution accuracy for optimization problem in this study. The conception is to employ the adjustable frequency determined by flight direction of bats to adapt the velocity toward the correct direction. The bats emit an ultrasound with various frequencies decided by flight direction to the current best bat. The adjustable frequency...
Recently, various consensus-based protocols have been developed for time synchronization in wireless sensor networks. However, due to the uncertainties lying in both the hardware fabrication and network communication process, it is not clear how most of the protocols will perform in real implementations. In order to reduce such gap, this paper investigates whether and how the typical consensus-based...
Methods for distributed optimization are necessary to solve large-scale problems such as those becoming more common in machine learning. The communication cost associated with transmitting large messages can become a serious performance bottleneck. We propose a consensus-based distributed algorithm to minimize a convex separable objective. Each node holds one component of the objective function, and...
This paper provides a detailed description of a novel multivariant optimization algorithm (MOA) for multi-modal optimization with the main idea to share search information by organizing all search atoms into a special designed structure. Its multiple and variant group property make MOA capable on multi-modal optimization problems. The capability of the MOA method in locating and maintaining multi...
Elastic Net Regularizers have shown much promise in designing sparse classifiers for linear classification. In this work, we propose an alternating optimization approach to solve the dual problems of elastic net regularized linear classification Support Vector Machines (SVMs) and logistic regression (LR). One of the sub-problems turns out to be a simple projection. The other sub-problem can be solved...
Basis pursuit denoising (BPDN) is an optimization method used in cutting edge computer vision and compressive sensing research. Although hosting a BPDN solver on an embedded platform is desirable because analysis can be performed in real-time, existing solvers are generally unsuitable for embedded implementation due to either poor run-time performance or high memory usage. To address the aforementioned...
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