QoS multicast tree construction in IP/DWDM optical internet by bio-inspired algorithms

Copyright @ Elsevier Ltd. All rights reserved.In this paper, two bio-inspired Quality of Service (QoS) multicast algorithms are proposed in IP over dense wavelength division multiplexing (DWDM) optical Internet. Given a QoS multicast request and the delay interval required by the application, both algorithms are able to find a flexible QoS-based cost suboptimal routing tree. They first construct the multicast trees based on ant colony optimization and artificial immune algorithm, respectively. Then a dedicated wavelength assignment algorithm is proposed to assign wavelengths to the trees aiming to minimize the delay of the wavelength conversion. In both algorithms, multicast routing and wavelength assignment are integrated into a single process. Therefore, they can find the multicast trees on which the least wavelength conversion delay is achieved. Load balance is also considered in both algorithms. Simulation results show that these two bio-inspired algorithms can construct high performance QoS routing trees for multicast applications in IP/DWDM optical Internet.This work was supported in part ny the Program for New Century Excellent Talents in University, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1, the National Natural Science Foundation of China under Grant no. 60673159 and 70671020, the National High-Tech Reasearch and Development Plan of China under Grant no. 2007AA041201, and the Specialized Research Fund for the Doctoral Program of Higher Education under Grant no. 20070145017

Development of Hybrid PS-FW GMPPT Algorithm for improving PV System Performance Under Partial Shading Conditions

A global maximum power point tracking (MPPT) algorithm hybrid based on Particle Swarm Fireworks (PS-FW) algorithm is proposed which is formed with Particle Swarm Optimization and Fireworks Algorithm. The algorithm tracks the global maximum power point (MPP) when conventional MPPT methods fail due to occurrence of partial shading conditions. With the applied strategies and operators, PS-FW algorithm obtains superior performances verified under simulation and experimental setup with multiple cases of shading patterns

A Survey on Particle Swarm Optimization for Association Rule Mining

Association rule mining (ARM) is one of the core techniques of data mining to discover potentially valuable association relationships from mixed datasets. In the current research, various heuristic algorithms have been introduced into ARM to address the high computation time of traditional ARM. Although a more detailed review of the heuristic algorithms based on ARM is available, this paper differs from the existing reviews in that we expected it to provide a more comprehensive and multi-faceted survey of emerging research, which could provide a reference for researchers in the field to help them understand the state-of-the-art PSO-based ARM algorithms. In this paper, we review the existing research results. Heuristic algorithms for ARM were divided into three main groups, including biologically inspired, physically inspired, and other algorithms. Additionally, different types of ARM and their evaluation metrics are described in this paper, and the current status of the improvement in PSO algorithms is discussed in stages, including swarm initialization, algorithm parameter optimization, optimal particle update, and velocity and position updates. Furthermore, we discuss the applications of PSO-based ARM algorithms and propose further research directions by exploring the existing problems.publishedVersio

The coloniality of metadata : a critical data analysis of the Archive Of Early American Images at the John Carter Brown Library

How do contemporary metadata practices replicate the coloniality of power embedded in European colonial documents describing the Spanish Americas? This report draws from critical theory and postcolonial thought within the field of Latin American studies to explore the extent to which standardized description and categorization practices can perpetuate a Eurocentric colonial gaze on the Spanish Americas. In order to ground this theoretical engagement within the fields of information science and critical data studies, a dataset is compiled and computationally analyzed, which contains metadata records corresponding to images derived from books, manuscripts, and broadsides printed between 1492 and 1825 about the Spanish Americas found in the Archive of Early American Images at the John Carter Brown Library. This report then applies unsupervised machine learning techniques and counts word frequencies to identify broad metadata trends across the collection. Ultimately, this report combines methods from critical data studies and Latin American cultural studies to understand how controlled vocabularies and descriptive practices can perpetuate colonial structures of power.Latin American StudiesInformatio

An effective feature selection using improved marine predators algorithm for Alzheimer’s disease classification

Alzheimer’s disease (AD) is an irremediable neurodegenerative illness developed by the fast deterioration of brain cells. AD is mostly common in elder people and it extremely disturbs the physical and mental health of patients, therefore early detection is essential to prevent AD development. However, the precise detection of AD and mild cognitive impairment (MCI) is difficult during classification. In this paper, the Residual network i.e., ResNet-18 is used for extracting the features, and the proposed improved marine predators algorithm (IMPA) is developed for choosing the optimum features to perform an effective classification of AD. The multi-verse optimizer (MVO) used in the IMPA helps to balance exploration and exploitation, which leads to the selection of optimal relevant features. Further, the classification of AD is accomplished using the multiclass support vector machine (MSVM). Open access series of imaging studies-1 (OASIS-1) and Alzheimer disease neuroimaging initiative (ADNI) datasets are used to evaluate the IMPA-MSVM method. The performance of the IMPA-MSVM method is analyzed using accuracy, sensitivity, specificity, positive predictive value (PPV) and matthews correlation coefficient (MCC). The existing methods such as the deep learning-based segmenting method using SegNet (DLSS), mish activation function (MAF) with spatial transformer network (STN) and BrainNet2D are used to evaluate the IMPA-MSVM method. The accuracy of IMPA-MSVM for the ADNI dataset is 98.43% which is more when compared to the DLSS and MAF-STN

A review of recent advances in metaheuristic maximum power point tracking algorithms for solar photovoltaic systems under the partial-shading conditions

Several maximum power point (MPP) tracking algorithms for solar power or photovoltaic (PV) systems concerning partial-shading conditions have been studied and reviewed using conventional or advanced methods. The standard MPPT algorithms for partial-shading conditions are: (i) conventional; (ii) mathematics-based; (iii) artificial intelligence; (iv) metaheuristic. The main problems of the conventional methods are poor power harvesting and low efficiency due to many local maximum appearances and difficulty in determining the global maximum tracking. This paper presents MPPT algorithms for partial-shading conditions, mainly metaheuristics algorithms. Firstly, the four classification algorithms will be reviewed. Secondly, an in-depth review of the metaheuristic algorithms is presented. Remarkably, 40 metaheuristic algorithms are classified into four classes for a more detailed discussion; physics-based, biology-based, sociology-based, and human behavior-based are presented and evaluated comprehensively. Furthermore, the performance comparison of the 40 metaheuristic algorithms in terms of complexity level, converter type, sensor requirement, steady-state oscillation, tracking capability, cost, and grid connection are synthesized. Generally, readers can choose the most appropriate algorithms according to application necessities and system conditions. This study can be considered a valuable reference for in-depth works on current related issues