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International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS)

作者： Houcine, Marouani Amin, Sallem Mondher, Chaoui Nouri, Masmoudi Univ Sfax LETI Sfax Tunisia

ISBN: (纸本)9781538678503

Particle Swarm Optimization (PSO) and Differential Evolution (DE) are two dominant metaheuristic optimization techniques with different special features. These specialties are combined together in an improved particle swarm optimization (DPSO) algorithm is estimated. The proposed idea consists of incorporating the concept of the DE algorithm when updating velocity equation of the PSO algorithm in sort to guarantee the global optimal. Also, the objective is to bias the solution, the difference between the selected randomly and the target particle, with an adjust parameter. The improved algorithm is simulated on several test functions and compared between three references metaheuristics and one variant most used in the literature. Then, DPSO has been used to optimize the mutual inductance of an implantable biomedical problem.

关键词： Improved DPSO Particle Swarm Optimization Differential Evolution backtracking search algorithm Gravitational search algorithm Mutual inductance Archimedes coil Solenoid coil

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Solution of Combined Economic Emission Load Dispatch Problem using Chaotic BSA-PSO hybrid algorithm

Solution of Combined Economic Emission Load Dispatch Problem...

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Sustainable Technology and Engineering Applications (NCETSTEA), 2020 National Conference on Emerging Trends on

作者： Koustav Dasgupta Pratosh Kumar Jha Provas Kumar Roy Electrical Engineering Department Techno India University Salt Lake Kolkata India Electrical Engineering Department Kalyani Government Engineering College Kalyani India

ISBN: (数字)9781728143620

ISBN: (纸本)9781728143637

In recent years, generation cost and fuel emission have been essential to minimize to sustain on the planet. Fossil fuel has been enormously utilized for power generation for a long time. It is required to manage customer demand and protect our environment by properly utilizing fossil fuel. Here, the combined economic emission load dispatch (CEELD) problem has been taken for getting a solution to the crisis of conventional energy resources. Here, price penalty factor has been used to formulate a single objective function of CEELD problem and this objective function has been taken to obtain optimal generation cost and emission by chaotic sequence-based hybridized backtracking search algorithm and particle swarm optimization(C-BSA-PSO). Performance and convertibility of the proposed algorithm have been shown by comparing the results with different results of PSO and BSA.

关键词： combined economic emission load dispatch chaotic sequence particle swarm optimization backtracking search algorithm Particle swarm optimization searchING algorithm Fossil Fuels load dispatching Objective function emission Solution Emissions Solutions Conventional energy resources algorithms backtracking search parametric subharmonic oscillator Power production

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Comparative study between different optimisation techniques for finding precise switching angle for SHE-PWM of three-phase seven-level cascaded H-bridge inverter

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IET POWER ELECTRONICS 2018年第3期11卷 600-609页

作者： Kundu, Sourabh Burman, Arka Deb Giri, Santu K. Mukherjee, Sarbani Banerjee, Subrata Natl Inst Technol NIT Durgapur Dept Elect Engn Durgapur 713209 India CSIR Cent Mech Engn Res Inst SET Grp Durgapur 713209 India

Selective harmonic elimination pulse-width modulation (SHE-PWM) works at low-frequency switching, which reduces switching losses, device stress, and increases energy conversion efficiency. So, it can be an effective control strategy for multilevel inverter working on medium-voltage, high-power industrial energy conversion application. It provides desired output voltage by retaining the requested fundamental component as well as eliminating some low-order harmonics. The application of SHE-PWM in industries is having an influence on precise solvability of complex and non-linear equations. This study presents two recently reported optimisation techniques, namely backtracking search algorithm and differential search algorithm (DSA) for obtaining a more accurate solution of the harmonics elimination problem. The superiority of the proposed optimisation algorithms over the well known ancient algorithm such as genetic algorithm, BEE algorithm and particle swarm optimisation have been established by a comparative study with respect to the possibility of attaining global minima, the rank of convergence rate, and inverter performance analysis. Simulation and experimental results validate the efficacy of the DSA optimisation technique for calculating more precise switching angles that totally eliminate 5th- and 7th-order harmonics with fulfilling the requested fundamental component.

关键词： PWM invertors switching convertors harmonics suppression search problems genetic algorithms particle swarm optimisation SHE-PWM three-phase seven-level cascaded H-bridge inverter selective harmonic elimination pulse-width modulation multilevel inverter medium-voltage high-power industrial energy conversion application energy conversion efficiency low-order harmonic elimination complex equation nonlinear equation backtracking search algorithm DSA differential search algorithm genetic algorithm BEE algorithm particle swarm optimisation 5th-order harmonic elimination 7th-order harmonic elimination

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A novel inverse modeling control for piezo positioning stage

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2018年第12期32卷 5875-5888页

作者： To Xuan Dinh Nguyen Phi Luan Kyoung Kwan Ahn Univ Ulsan Sch Mech & Automot Engn Ulsan South Korea T Robot Co Ltd Gyeonggi Do South Korea

This paper focuses on the application of a novel inverse nonlinear autoregressive with exogenous input (NARX) structure and a fuzzy inference system on position control of a piezo positioning stage (PPS) system. The highly relationship between input voltage and output displacement is thoroughly modeled by using the inverse fuzzy NARX model-based identification process with the experiment training data. The unknown parameters of the proposed NARX fuzzy model was obtained base on a hybridization between backtracking search algorithm and gradient descent technique. A combination between fuzzy propositional-integral-derivative controller and inverse modeling feedforward controller was then applied to control PPS with several challenged working condition. The results show efficiency of the proposed method for the PPS system in terms of high tracking precision and excellent dynamic performance.

关键词： Piezo positioning stage backtracking search algorithm Inverse modeling Nonlinear auto regressive exogenous

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A backtracking search hyper-heuristic for the distributed assembly flow-shop scheduling problem

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SWARM AND EVOLUTIONARY COMPUTATION 2017年 36卷 124-135页

作者： Lin, Jian Wang, Zhou-Jing Li, Xiaodong Zhejiang Univ Finance & Econ Sch Informat Hangzhou 310018 Zhejiang Peoples R China RMIT Univ Sch Sci Comp Sci & IT Melbourne Vic 3001 Australia

Distributed assembly permutation flow-shop scheduling problem (DAPFSP) is recognized as an important class of problems in modern supply chains and manufacturing systems. In this paper, a backtracking search hyper heuristic (BS-HH) algorithm is proposed to solve the DAPFSP. In the BS-HH scheme, ten simple and effective heuristic rules are designed to construct a set of low-level heuristics (LLHs), and the backtracking search algorithm is employed as the high-level strategy to manipulate the LLHs to operate on the solution space. Additionally, an efficient solution encoding and decoding scheme is proposed to generate a feasible schedule. The effectiveness of the BS-HH is evaluated on two typical benchmark sets and the computational results indicate the superiority of the proposed BS-HH scheme over the state-of-the-art algorithms.

关键词： Hyper-heuristic backtracking search algorithm Distributed assembly Flow-shop scheduling

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An exact penalty function method for optimising QAP formulation in facility layout problem

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INTERNATIONAL JOURNAL OF PRODUCTION REsearch 2017年第10期55卷 2913-2929页

作者： Zhou, Jingyang Love, Peter E. D. Teo, Kok Lay Luo, Hanbin Curtin Univ Dept Civil & Mech Engn Perth WA Australia Curtin Univ Dept Math & Stat Perth WA Australia Huazhong Univ Sci & Technol Dept Engn & Mech Wuhan Peoples R China

A quadratic assignment problem (QAP), which is a combinatorial optimisation problem, is developed to model the problem of locating facilities with material flows between them. The aim of solving the QAP formulation for a facility layout problem (FLP) is to increase a system's operating efficiency by reducing material handling costs, which can be measured by interdepartmental distances and flows. The QAP-formulated FLP can be viewed as a discrete optimisation problem, where the quadratic objective function is optimised with respect to discrete decision variables subject to linear equality constraints. The conventional approach for solving this discrete optimisation problem is to use the linearisation of the quadratic objective function whereby additional discrete variables and constraints are introduced. The adoption of the linearisation process can result in a significantly increased number of variables and constraints;solving the resulting problem can therefore be challenging. In this paper, a new approach is introduced to solve this discrete optimisation problem. First, the discrete optimisation problem is transformed into an equivalent nonlinear optimisation problem involving only continuous decision variables by introducing quadratic inequality constraints. The number of variables, however, remains the same as the original problem. Then, an exact penalty function method is applied to convert this transformed continuous optimisation problem into an unconstrained continuous optimisation problem. An improved backtracking search algorithm is then developed to solve the unconstrained optimisation problem. Numerical computation results demonstrate the effectiveness of the proposed new approach.

关键词： facility layout QAP nonlinear programming continuous optimization exact penalty function method backtracking search algorithm

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Virtual Power Plant and Microgrids controller for Energy Management based on optimization techniques

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JOURNAL OF ELECTRICAL SYSTEMS 2017年第2期13卷 285-294页

作者： Abdolrasol, Maher G. M. Mohamed, Azah Hannan, M. A. Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia Univ Tenaga Nas Coll Engn Dept Elect Power Engn Bandar Baru Bangi 43650 Selangor Malaysia

This paper discuss virtual power plant (VPP) and Microgrid controller for energy management system (EMS) based on optimization techniques by using two optimization techniques namely backtracking search algorithm (BSA) and particle swarm optimization algorithm (PSO). The research proposes use of multi Microgrid in the distribution networks to aggregate the power form distribution generation and form it into single Microgrid and let these Microgrid deal directly with the central organizer called virtual power plant. VPP duties are price forecast, demand forecast, weather forecast, production forecast, shedding loads, make intelligent decision and for aggregate & optimizes the data. This huge system has been tested and simulated by using Matlab simulink. These paper shows optimizations of two methods were really significant in the results. But BSA is better than PSO to search for better parameters which could make more power saving as in the results and the discussion.

关键词： Virtual Power Plant Microgrid Energy Management System backtracking search algorithm particle swarm optimization algorithm

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Optimal Neural Network Approach for Estimating State of Energy of Lithium-ion Battery Using Heuristic Optimization Techniques 6

Optimal Neural Network Approach for Estimating State of Ener...

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6th International Conference on Electrical Engineering and Informatics (ICEEI) - Sustainable Society Through Digital Innovation

作者： Lipu, M. S. Hossain Hussain, Aini Saad, M. H. M. Hannan, M. A. Univ Kebangsaan Malaysia Fac Engn & Built Environm Bangi 43600 Selangor Malaysia Univ Tenaga Nas Dept Elect Power Engn Kajang 43000 Selangor Malaysia

ISBN: (纸本)9781538604755

This paper presents an optimal state of energy (SOE) estimation strategy of a lithium-ion battery using the back-propagation neural network (BPNN). Two heuristic optmization techniques named backtracketing search algorithm (BSA) and particle swarm optimization (PSO) algorithm are applied to improve the accuracy of BPNN model. Optimization algorithms are developed to determine the optimal value of hidden layer neurons and learning rate of BPNN model. Three most influencing factors including current, voltage and temperature are considered as the inputs to the optimal BPNN model. Federal Urban Driving Schedule (FUDS) is used to check the model robustness at 0 degrees C, 25 degrees C and 45 degrees C. The model performance is evaluated based on the root mean square error (RMSE) and mean absolute error (MAE). The results show that the proposed model obtains good accuracy with an absolute error of +/- 5%. The BPNN based BSA model improves the SOE estimation accuracy by reducing RMSE and MAE by 2.8% and 4.4% compared to BPNN based PSO model at 25 degrees C.

关键词： state of energy back-propagation neural network backtracking search algorithm particle swarm optimization Federal Urban Driving Schedule

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Optimized Support Vector Machine & Wavelet Transform for Distribution Grid Fault Location 11

Optimized Support Vector Machine & Wavelet Transform for Dis...

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11th IEEE International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG)

作者： Shafiullah, Md Ijaz, Muhammad Abido, M. A. Al-Hamouz, Z. King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran Saudi Arabia

ISBN: (纸本)9781509049639

The precise knowledge of fault locations is very important as longer period interruption caused by faults are responsible for most of the customer minute losses in the distribution grid. The objective of this paper is to locate different types of faults in a distribution grid by extracting the features of three phase fault currents employing wavelet transform (WT) and feeding the statistical measures of the extracted features as inputs of optimized support vector machine (SVM). backtracking search algorithm is employed to optimize the parameters of the SVM. Finally, the results of the proposed model are compared with the results of a general/non-optimized support vector machine to validate the efficacy of the proposed model.

关键词： backtracking search algorithm Distribution Grid Fault Location Support Vector Machine Wavelet Transform Statistical Measures

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BSA/WDO based optimization of two-area multi-sources automatic generation control 2

BSA/WDO based optimization of two-area multi-sources automat...

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2nd International Conference for Convergence in Technology (I2CT)

作者： Dwivedi, Sandeep Rout, Srikant Anirudh, Kuppili Bhattacharya, Aniruddha Natl Inst Technol Dept Elect Engn Agartala India

ISBN: (纸本)9781509043071

This paper presents comparative performance analysis of Automatic generation control with two recently developed meta-heuristic nature inspired algorithms called backtracking search algorithm/Wind driven optimization algorithm. An attempt has been made to show the superiority of Wind driven optimization algorithm over backtracking search algorithm to improve the transient performance of automatic generation control of an interconnected two-area multi-source power system with physical constraints governor dead band, generation rate constraints, reheat system. Comparative studies of BSA and WDO with PI/PID controller reveals that WDO based PID controller in both the areas improve the transient performance to a greater extent following small load perturbation(s).

关键词： Autoniatic genearation control backtracking search algorithm Boiler dynamics Generation rate constraints governor dead band wind driven optimization

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