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A novel severity performance index for optimal allocation and sizing of photovoltaic distributed generations

ENERGY REPORTS 2020年 6卷 2180-2190页

作者： Farh, Hassan M. H. Al-Shaalan, Abdullah M. Eltamaly, Ali M. Al-Shamma'a, Abdullrahman A. King Saud Univ Coll Engn Elect Engn Dept Riyadh 11421 Saudi Arabia King Saud Univ Sustainable Energy Technol Ctr Riyadh 11421 Saudi Arabia Mansoura Univ Elect Engn Dept Mansoura 35516 Egypt Taiz Univ Coll Engn Dept Mechatron Engn Taizi 6803 Yemen

Optimal allocation and sizing of the distributed generations (DGs) attract great attention of many researchers all over the world. In this study, the problems associated to optimal DGs allocation and sizing are divided into two sub-problems. One of them deals with the DGs allocation at the critical buses and the other aims to select the optimal size of these DGs. A novel severity performance index (SPI) is introduced to evaluate both the power system reliability and quality. The crow search (CS) is integrated with PSO not only to collect the merits of both CS and PSO but also to solve the optimal power flow problem including multi-DGs allocated at the critical buses for the IEEE 30-bus test system. Detailed comparisons of four distinct DGs allocation cases are presented, discussed, and analyzed. The simulation findings proved that the DGs allocated based on the multi-objectives (SPI, total fuel costs;FC;and power losses;PL) have a superior performance compared to other cases in terms of voltage profile, branches loading, power losses, and total costs. Therefore, the multi DGs is preferred to be allocated based on these multi-objectives (SPI, FC, and PL) in order to attain additional techno-economic benefits to the power system. (C) 2020 The Author(s). Published by Elsevier Ltd.

关键词： Crow search integrated with PSO Distributed generations Severity performance index Allocation and sizing multi-objective function

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Elephant herding with whale optimization enabled ORB features and CNN for Iris recognition

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multiMEDIA TOOLS AND APPLICATIONS 2022年第4期81卷 5761-5794页

作者： Babu, Gorla Khayum, Pinjari Abdul JNTU Elect & Commun Engn Anantapur Andhra Pradesh India G Pullareddy Engn Coll Elect & Commun Engn Kurnool India

Iris biometrics is one of the frequently used biometrics for security purposes, and it provides the appropriate tools for identifying humans. Deep learners when used along with iris recognition can improve accuracy, automatic learning, and generalization ability. Despite the advantages of deep learners, some problems like time complexity and computational efforts exist in deep learners. In order to solve these obstacles, this paper intends to develop the intelligent iris recognition model based on the concepts merged with multi-objective feature selection and deep learning. The main phases of the proposed model involve (a) Pre-processing, (b) Iris Segmentation, (c) optimal ORB point selection and feature vector generation, and (d) optimal recognition. Here, the pre-processing of the image is performed by filtering and contrast enhancement techniques. Further, the iris segmentation is done by few approaches such as reflection moving, iris localization, and Hough-transform-based segmentation. Once the segmentation of the iris is finished, feature extraction is carried out by optimized Oriented FAST and rotated BRIEF (ORB) features concerning on multi-objective function. Finally, the optimized Convolutional Neural Network (CNN) is used for iris recognition. Here, the hybrid algorithm with the integration of two meta-heuristic algorithms like Elephant Herding Optimization (EHO), and Whale Optimization Algorithm (WOA) called Elephant Herding with Whale Optimization Algorithm (EH-WOA) is utilized for performing the optimized ORB feature selection and optimized CNN. The experiments are conducted on a benchmark datasets like IIT Delhi (IITD) Iris database, and MMU iris dataset to analyze the performance of the proposed model over the different network structures for accurate iris recognition. Through the experimental analysis, the proposed optimized CNN + EH-WOA had better values in terms of accuracy, which is 0.98% higher than SVM, 0.92% higher than KNN, 0.88% higher NN, a

关键词： Iris recognition Iris segmentation ORB point selection Convolutional neural network Elephant herding with whale optimization algorithm multi-objective function

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Robust design of roll-formed slide rail using response surface method

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2010年第12期24卷 2545-2553页

作者： Oh, Minjin Lee, Moon Kyu Kim, Naksoo Sogang Univ Dept Mech Engn Seoul 121742 South Korea

Roll-formed slide rail used as a linear guide in the smooth movement of drawers and electric home appliances requires geometric accuracy because of a high slenderness ratio and repetitive usage. The slide rail members are generally manufactured by the roll forming process. The members need to be improved through optimization of the roll forming process instead of the designer's experience. The aim of this study is to determine the optimal roll forming parameters by using robust optimization technique which simultaneously satisfies three criteria such as the shape difference factor, bowing factor and modified inverse safety factor. In analyzing the roll forming process of a slide rail, the pass in which the largest deformation occurred is designated as the target pass. The positions and the curvature of rolls are set as the design variables in the target pass. The cost function, which is comprised of the shape difference factor, the bowing factor, and the modified inverse safety factor, is obtained using design-of-experiments of the response surface method. The cost function is minimized by using robust optimization techniques and showed the improved the straightness and the durability value. Using robust design methodology, it is able to be constructed a multi-objective function, and optimized three criteria, simultaneously.

关键词： multi-objective function Robust optimization Roll forming Table of orthogonal arrays

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ABSO: an energy-efficient multi-objective VM consolidation using adaptive beetle swarm optimization on cloud environment

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第3期14卷 2185-2197页

作者： Hariharan, B. Siva, R. Kaliraj, S. Prakash, P. N. Senthil SRM Inst Sci & Technol Dept Sch Comp Chennai Tamil Nadu India SRM Inst Sci & Technol Dept Software Engn Chennai Tamil Nadu India RMK Coll Engn & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India

Cloud computing is a powerful way to provide a suitable platform for data centers and to store data. Along with the so many benefits, there are still some management issues that need to be investigated. Although cloud computing seems to be a very attractive implementation it is facing incredible energy consumption and costs concerns. To avoid energy consumption, a VM consolidation and migration approach is introduced. The main objective of VM consolidation is to perform more jobs while consuming less amount of power. To achieve this, in this paper multi-objective energy-efficient VM consolidation using adaptive beetle swarm optimization (ABSO) algorithm is proposed. The proposed ABSO is a hybridization of particle swarm optimization (PSO) and Beetle swarm optimization (BSO).The proposed method presented with efficient solution representation, derivation of efficient fitness function (or multi-objective function) along with PSO and BSO operator. The effectiveness of the approach is analyzed based on the different evaluation measures and effectiveness is compared with different methods. From the results, our proposed approach consumes only 8.234 J energy for scheduling 100 tasks which are 10.616 J for BSO-based VM consolidation, 11.754 J for PSO-based VM consolidation, and 13.545 J for GA-based VM consolidation.

关键词： VM migration Consolidation Load balancer multi-objective function Particle swarm optimization Beetle swarm optimization

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Energy management strategy based on energy storage equalization technology and transferable load

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2018年第9期28卷 1-1页

作者： Fang, Lei Niu, Yugang Zu, Qiwu Wang, Siming East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai Peoples R China

The adoption of the transferable load and energy storage system (ESS) makes the microgrid more suitable for the development of modern electricity. By means of energy storage equalization technology and transferable load, this paper proposes a day-head optimal scheduling method for a grid-connected microgrid. In the proposed strategy, by solving the multi-objective function involving in the cost of electricity market demand, operating of BESS, and the residents' satisfaction, then the optimal control of transferable load and the charging/discharging power are achieved according to the strategy of ESS equalization and transferable load. Besides, the stability of charging/discharging power and the consistency of the battery units are analyzed. Finally, simulation results are provided by using the improved PSO to show that this proposed method can attain much stability and efficiency.

关键词： battery balance energy storage system micro-grid multi-objective function transferable load

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multi-objective non-intrusive load disaggregation based on appliances characteristics in smart homes

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ENERGY REPORTS 2021年 7卷 4445-4459页

作者： Fan, Wen Liu, Qing Ahmadpour, Ali Farkoush, Saeed Gholami North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Baoding 071003 Peoples R China North China Elect Power Univ Dept Elect Engn Baoding 071003 Peoples R China Univ Mohaghegh Ardabili Dept Elect Engn Ardebil Iran Yeungnam Univ Dept Elect Engn Yeungnam South Korea

The Load Disaggregation (LD) is an optimizing problem. The actual operation states of the appliances would not serve as an optimal solution for a single-objective function, due to various noises as well as frequency interferences from the adjacent systems. In this paper, the LD model with a multi-objective function combines the appliance features at both macro and micro levels. This model contributes a good representation of the appliances from several viewpoints. Recognizing numerous appliances is carried out through five objective functions using apparent, active, and reactive powers, currents, and harmonics as the loads characteristic. The suggested problem is solved utilizing Moth-Flame Optimization (MFO) algorithm with several objectives for LD. Besides, it prevents tuning the weighted parameters and does not ignore the conflict among the objectives. In addition, the Factorial Hidden Markov Model (FHMM) is used to define the allowable modes of the appliances for the next second. It could be resorted to an objective-rank project to cope with the restraint on the number of appliances functioning concurrently. The efficiency of the suggested method for LD is shown by experimental outcomes and is compared with other methods. The results of various combinations of appliances features are evaluated by various evaluation metrics. It is presented that in more features, the results are more accurate. The results show that the accuracy of the proposed method is at least 20 % more than others. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Load disaggregation Factorial hidden Markov model Moth-flame optimization algorithm multi-objective function Smart home appliance Non-intrusive load monitoring

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A multi-objective APPROACH FOR DETERMINING THE NUMBER OF BLADES ON A NACA MARINE PROPELLER

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BRODOGRADNJA 2016年第2期67卷 15-32页

作者： Nouri, Nowrouz Mohammad Mohammadi, Saber Iran Univ Sci & Technol Sch Mech Engn Tehran Iran

This paper numerically discusses how the performance of a NACA marine propeller is affected by the number of blades, which is one of the most crucial geometrical parameters determining the performance of a propeller. Results are presented in terms of the hydrodynamic and structural parameters. The results show that changing the number of blades changes the hydrodynamic efficiency, torque, thrust, cavitation behaviour and structural stiffness of the propeller nonlinearly. Furthermore, it is shown that the propellers structural lifetime is shortened by increasing the number of blades. Hence, the propeller's number of blades is a multi-objective function and will be discussed in this research. The applied tool which is used to study the hydrodynamic performance of the propellers is a RANS-based CFD one and the FEM is considered to study the structural behaviour of the propellers.

关键词： marine propeller number of blades CFD FEM multi-objective function

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Two-stage layout-size optimization method for prow stiffeners

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INTERNATIONAL JOURNAL OF NAVAL ARCHITECTURE AND OCEAN ENGINEERING 2019年第1期11卷 44-51页

作者： Liu, Zhijun Cho, Shingo Takezawa, Akihiro Zhang, Xiaopeng Kitamura, Mitsuru Hiroshima Univ Grad Sch Engn Dept Transportat & Environm Syst Hiroshima Japan Dalian Univ Technol Dept Engn Mech Dalian Peoples R China

Designing sophisticate ship structures that satisfy several design criteria simultaneously with minimum weight and cost is an important engineering issue. For a ship structure composed of a shell and stiffeners, this issue is more serious because their mutual effect has to be addressed. In this study, a two-stage optimization method is proposed for the conceptual design of stiffeners in a ship's prow. In the first stage, a topology optimization method is used to determine a potential stiffener distribution based on the optimal results, whereupon stiffeners are constructed according to stiffener generative theory and the material distribution. In the second stage, size optimization is conducted to optimize the plate and stiffener sections simultaneously based on a parametric model. A final analysis model of the ship-prow structure is presented to assess the validity of this method. The analysis results show that the two-stage optimization method is effective for stiffener conceptual design, which provides a reference for designing actual stiffeners for ship hulls. (C) 2018 Society of Naval Architects of Korea. Production and hosting by Elsevier B.V.

关键词： Prow stiffener Two-stage optimization Manufacturing constraints multi-objective function Compromise programming

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A finite-volume based topology optimization procedure for an aero-thermal system with a simplified sensitivity analysis method

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2020年 163卷 120524-120524页

作者： Lee, Jun Seok Ha, Man Yeong Min, June Kee Pusan Natl Univ Sch Mech Engn 2Busandaehak Ro 63 Beon Gil Busan 46241 South Korea

A finite-volume-based topology optimization procedure for application to problems in aero-thermal systems has been studied. A simplified sensitivity analysis procedure was proposed and examined by neglecting the exact adjoint approach. Two types of conventional interpolation schemes for inverse porosity with a penalized polynomial form and its gradient with respect to the design variables were examined. The four-terminal problem was used to determine the best method for the optimization. The objective function of minimum external force to the flow was considered for simplicity. The finally chosen method showed good agreement with the analytic results and previous numerical results. With this method, for examined cases in this paper, it was possible to obtain the optimal shapes without solving the corresponding adjoint equations in the iterations. The present method, however, showed a slow convergence compared with the adjoint method, especially for the external flow problem. Various representative problems, such as the external and internal flows, laminar and turbulent flows, and the multi-objective function problem regarding the heat transfer as well as minimum flow loss were considered. For the turbulent flow, the wall-distance in the fluid region corresponding to the continuously varying solid region was calculated using the direct geometric method, instead of solving the Eikonal equation, due to the numerical stability. The Spalart-Allmaras one-equation turbulence model was adopted for the flow field. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Topology optimization Sensitivity analysis Computational Fluid Dynamics Finite-Volume method multi-objective function

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Aspect-based sentiment analysis using deep networks and stochastic optimization

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NEURAL COMPUTING & APPLICATIONS 2020年第8期32卷 3221-3235页

作者： Kumar, Ravindra Pannu, Husanbir Singh Malhi, Avleen Kaur Thapar Univ Comp Sci & Engn Patiala 147004 Punjab India

Sentiment analysis, also known as opinion mining, is a computational study of unstructured textual information which is used to analyze a persons attitude from a piece of text. This paper proposes an efficient method for sentiment analysis by effectively combining three procedures: (a) creating the ontologies for extraction of semantic features (b) Word2vec for conversion of processed corpus (c) convolutional neural network (CNN) for opinion mining. For CNN parameter tuning, a multi-objective function is solved for nondominant Pareto front optimal values using particle swarm optimization. Experiments show that the proposed technique outperforms other state-of-the-art techniques while yielding 88.52%, 94.30%, 85.63% and 86.03% in accuracy, precision, recall and F-measure, respectively.

关键词： Sentiment analysis Convolutional neural networks PSO multi-objective function

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