Load Frequency Control of Multi-interconnected Renewable Energy Plants Using Multi-Verse Optimizer

A reliable approach based on a multi-verse optimization algorithm(MVO)for designing load frequency control incorporated in multi-interconnected power system comprising wind power and photovoltaic(PV)plants is presented in this paper.It has been applied for optimizing the control parameters of the load frequency controller(LFC)of the multi-source power system(MSPS).The MSPS includes thermal,gas,and hydro power plants for energy generation.Moreover,the MSPS is integrated with renewable energy sources(RES).The MVO algorithm is applied to acquire the ideal parameters of the controller for controlling a single area and a multi-area MSPS integrated with RES.HVDC link is utilized in shunt with AC multi-areas interconnection tie line.The proposed scheme has achieved robust performance against the disturbance in loading conditions,variation of system parameters,and size of step load perturbation(SLP).Meanwhile,the simulation outcomes showed a good dynamic performance of the proposed controller.

Intelligent Building Load Scheduling Based on Multi-Objective Multi-Verse Algorithm

In the multi-objective of intelligent building load scheduling, aiming at the problem of how to select Pareto frontier scheme for multi-objective optimization algorithm, the current optimal scheme mechanism combined with multi-objective multi-verse algorithm is used to optimize the intelligent building load scheduling. The update mechanism is changed in updating the position of the universe, and the process of correction coding is omitted in the iterative process of the algorithm, which reduces the com-putational complexity. The feasibility and effectiveness of the proposed method are verified by the optimal scheduling experiments of residential loads.

Balancing Exploration–Exploitation of Multi-verse Optimizer for Parameter Extraction on Photovoltaic Models

Extracting photovoltaic(PV)model parameters based on the measured voltage and current information is crucial in the simulation and management of PV systems.To accurately and reliably extract the unknown parameters of different PV models,this paper proposes an improved multi-verse optimizer that integrates an iterative chaos map and the Nelder–Mead simplex method,INMVO.Quantitative experiments verified that the proposed INMVO fueled by both mechanisms has more affluent populations and a more reasonable balance between exploration and exploitation.Further,to verify the feasibility and competitiveness of the proposal,this paper employed INMVO to extract the unknown parameters on single-diode,double-diode,three-diode,and PV module four well-known PV models,and the high-performance techniques are selected for comparison.In addition,the Wilcoxon signed-rank and Friedman tests were employed to test the experimental results statistically.Various evaluation metrics,such as root means square error,relative error,absolute error,and statistical test,demonstrate that the proposed INMVO works effectively and accurately to extract the unknown parameters on different PV models compared to other techniques.In addition,the capability of INMVO to stably and accurately extract unknown parameters was also verified on three commercial PV modules under different irradiance and temperatures.In conclusion,the proposal in this paper can be implemented as an advanced and reliable tool for extracting the unknown parameters of different PV models.Note that the source code of INMVO is available at https://github.com/woniuzuioupao/INMVO.

基于多版本的协同软件设计系统的研究与设计

针对当前CASE技术面临的图形化系统协同工作支持不够,提出基于多版本的CSCW软件设计,对其中的关键问题进行研究,并对该系统进行设计。

A Two-Tier Fuzzy Meta-Heuristic Hybrid Optimization for Dynamic Android Malware Detection

Application Programming Interface(API)call feature analysis is the prominent method for dynamic android malware detection.Standard benchmark androidmalware API dataset includes featureswith high dimensionality.Not all features of the data are relevant,filtering unwanted features improves efficiency.This paper proposes fuzzy and meta-heuristic optimization hybrid to eliminate insignificant features and improve the performance.In the first phase fuzzy benchmarking is used to select the top best features,and in the second phase meta-heuristic optimization algorithms viz.,Moth Flame Optimization(MFO),Multi-Verse Optimization(MVO)&Whale Optimization(WO)are run with Machine Learning(ML)wrappers to select the best from the rest.Five ML methods viz.,Decision Tree(DT),Random Forest(RF),K-NearestNeighbors(KNN),Naie Bayes(NB)&NearestCentroid(NC)are compared as wrappers.Several experiments are conducted and among them,the best post reduction accuracy of 98.34% is recorded with 95% elimination of features.The proposed novelmethod outperformed among the existing works on the same dataset.

Multi-verse Optimizer with Rosenbrock and Diffusion Mechanisms for Multilevel Threshold Image Segmentation from COVID-19 Chest X-Ray Images

Coronavirus Disease 2019(COVID-19)is the most severe epidemic that is prevalent all over the world.How quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the epidemic.Moreover,it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray images.As we all know,image segmentation is a critical stage in image processing and analysis.To achieve better image segmentation results,this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named RDMVO.Then utilizes RDMVO to calculate the maximum Kapur’s entropy for multilevel threshold image segmentation.This image segmentation scheme is called RDMVO-MIS.We ran two sets of experiments to test the performance of RDMVO and RDMVO-MIS.First,RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark functions.Second,the image segmentation experiment was carried out using RDMVO-MIS,and some meta-heuristic algorithms were selected as comparisons.The test image dataset includes Berkeley images and COVID-19 Chest X-ray images.The experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.

Multi-verse optimizer based parameters decision with considering tool life in dry hobbing process

Dry hobbing has received extensive attention for its environmentally friendly processing pattern.Due to the absence of lubricants,hobbing process is highly dependent on process parameters combination since using unreasonable parameters tends to affect the machining performance.Besides,the consideration of tool life is frequently ignored in gear hobbing.Thus,to settle the above issues,a multiobjective parameters decision approach considering tool life is developed.Firstly,detailed quantitative analysis between process parameters and hobbing performance,i.e.,machining time,production cost and tool life is introduced.Secondly,a multi-objective parameters decision-making model is constructed in search for optimum cutting parameters(cutting velocity v,axial feed rate f_(a))and hob parameters(hob diameter d_(0),threads z_(0)).Thirdly,a novel algorithm named multi-objective multi-verse optimizer(MOMVO)is utilized to solve the presented model.A case study is exhibited to show the feasibility and reliability of the proposed approach.The results reveal that(i)a balance can be achieved among machining time,production cost and tool life via appropriate process parameters determination;(ii)optimizing cutting parameters and hob parameters simultaneously contributes to optimal objectives;(iii)considering tool life provides usage precautions support and process parameters guidance for practical machining.

Efficient query processing framework for big data warehouse： an almost join-free approach

The rapidly increasing scale of data warehouses is challenging today＇s data analytical technologies. A con- ventional data analytical platform processes data warehouse queries using a star schema -- it normalizes the data into a fact table and a number of dimension tables, and during query processing it selectively joins the tables according to users＇ demands. This model is space economical. However, it faces two problems when applied to big data. First, join is an expensive operation, which prohibits a parallel database or a MapReduce-based system from achieving efficiency and scalability simultaneously. Second, join operations have to be executed repeatedly, while numerous join results can actually be reused by different queries. In this paper, we propose a new query processing frame- work for data warehouses. It pushes the join operations par- tially to the pre-processing phase and partially to the post- processing phase, so that data warehouse queries can be transformed into massive parallelized filter-aggregation oper- ations on the fact table. In contrast to the conventional query processing models, our approach is efficient, scalable and sta- ble despite of the large number of tables involved in the join. It is especially suitable for a large-scale parallel data ware- house. Our empirical evaluation on Hadoop shows that our framework exhibits linear scalability and outperforms some existing approaches by an order of magnitude.

Multi-Clock Snapshot Isolation Concurrency Control for NVM Database

Multi-Clock Snapshot Isolation(MCSI)is a concurrency control mechanism that implements snapshot isolation on a single-layer Non-Volatile Memory(NVM)database.It stores a single copy of data by using multi-version storage to ensure durability and runtime access.With multi-clock transaction timestamp assignment,MCSI can efficiently generate snapshots with vector clocks and use per-thread transaction status arrays to identify uncommitted versions in NVM.For evaluation,we compared MCSI with the PostgreSQL-style concurrency control used in the single-layer NVM database N2DB.The maximum transaction throughput of MCSI is 101%–195%higher than that of N2DB for the YCSB workloads,and 25%–49%higher for the TPC-C workloads.Moreover,the transaction latency of MCSI remains relatively stable as the thread count increases.With 18 worker threads,the average transaction latency of MCSI is 65%–84%lower than that of N2DB for the YCSB workloads and 16%–43%lower for the TPC-C workloads.