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World Congress on Computing and Communication Technologies (WCCCT)

作者： Wang Ping Hong-Yan Sang Qiu-Yun Tao Sun Qun School of Computer Science Liaocheng University Liaocheng China School of Mechanical & Automotive Engineering Liaocheng University Liaocheng China

ISBN: (数字)9781728197388

ISBN: (纸本)9781728197395

Under low-carbon scheduling, to solve the hybrid lot-streaming flowshop scheduling (HLFS) problem, an improved migrating birds optimisation (IMBO) algorithm is proposed to minimise the total flow time. The neighbourhood solution of the population is generated by insertion and exchange, and the population is further optimised by a local search method. In addition, a reset mechanism is added to avoid falling into a local optimum. Extensive computational results demonstrate the feasibility and effectiveness of the proposed algorithm.

关键词： lot-streaming hybrid flowshop scheduling low-carbon migrating birds optimisation algorithm low-carbon Search methods reset mechanism optimization algorithms migrant bird Population falls Falling Solve

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Dynamic Dimension Wrapping (DDW) Algorithm: A Novel Approach for Efficient Cross-Dimensional Search in Dynamic Multidimensional Spaces

arXiv

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arXiv 2024年

作者： Jin, Dongnan Liu, Yali Song, Qiuzhi Ma, Xunju Liu, Yue Wu, Dehao School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China

To effectively search for the optimal motion template in dynamic multidimensional space, this paper proposes a novel optimization algorithm, Dynamic Dimension Wrapping (DDW). The algorithm combines Dynamic Time Warping (DTW) and Euclidean distance, and designs a fitness function that adapts to dynamic multidimensional space by establishing a time-data chain mapping across dimensions. This paper also proposes a novel update mechanism-Optimal Dimension Collection (ODC), combined with the search strategy of traditional optimization algorithms, enables DDW to adjust both the dimension values and the number of dimensions of the population individuals simultaneously. In this way, DDW significantly reduces computational complexity and improves search accuracy. Experimental results show that DDW performs excellently in dynamic multidimensional space, outperforming 31 traditional optimization algorithms. This algorithm provides a novel approach to solving dynamic multidimensional optimization problems and demonstrates broad application potential in fields such as motion data analysis. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

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Block and Group Regularized Sparse Modeling for Dictionary Learning

Block and Group Regularized Sparse Modeling for Dictionary L...

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Yu-Tseh Chi Mohsen Ali Ajit Rajwade Jeffrey Ho University of Florida Gainesville FL U. S. A. Dhirubhai Ambani Institute of Information and Communication Technology Gandhinagar India

ISBN: (纸本)9781467364102

This paper proposes a dictionary learning framework that combines the proposed block/group (BGSC) or reconstructed block/group (R-BGSC) sparse coding schemes with the novel Intra-block Coherence Suppression Dictionary Learning (ICS-DL) algorithm. An important and distinguishing feature of the proposed framework is that all dictionary blocks are trained simultaneously with respect to each data group while the intra-block coherence being explicitly minimized as an important objective. We provide both empirical evidence and heuristic support for this feature that can be considered as a direct consequence of incorporating both the group structure for the input data and the block structure for the dictionary in the learning process. The optimization problems for both the dictionary learning and sparse coding can be solved efficiently using block-gradient descent, and the details of the optimization algorithms are presented. We evaluate the proposed methods using well-known datasets, and favorable comparisons with state-of-the-art dictionary learning methods demonstrate the viability and validity of the proposed framework.

关键词： BGSC R-BGSC ICS-DL Dictionaries as Topic Masonry construction Group Structure Sparse coding optimization algorithms data grouping

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ABC and PSO: A comparative analysis

ABC and PSO: A comparative analysis

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IEEE International Conference on Computational Intelligence and Computing Research

作者： Vaishali R Kulkarni Veena Desai Department of Computer Science Engineering KLS Gogte Institute of Technology Belagavi India Department of Electronics and Communication Engineering KLS Gogte Institute of Technology Belagavi India

ISBN: (纸本)9781509006137

Multidimensional optimization is critically important in engineering, economics and other disciplines. There are several deterministic approaches to optimization, each of which has its own pros and cons. Swarm intelligence-based optimization algorithms have been gained popularity for their effectiveness and resource efficiency. Two such popular algorithms, namely artificial bee colony (ABC) and particle swarm optimization (PSO) have been investigated in this paper. These algorithms have been implemented to minimize a few unimodal and multimodal benchmark functions. Results have been analyzed and the comparative analysis has been performed in terms of quality of solutions and computing time. Matlab simulation results show that the ABC algorithm delivers more accurate optimization than PSO does; but it suffers from delayed convergence. On the other hand, PSO is faster; but its accuracy is relatively inferior.

关键词： Swarm intelligence artificial bee colony particle swarm optimization benchmark functions Particle swarm optimization activity based costing parametric subharmonic oscillator bee colonies optimization algorithms

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Optimal Dispatch of Reactive Sources in Wind Farms

Optimal Dispatch of Reactive Sources in Wind Farms

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IEEE Power and Energy Society General Meeting

作者： I. Erlich W. Nakawiro M. Martinez Institute of Electric Power Systems University of Duisburg-Essen CITCEA-UPC Universitat Politecnica de Catalunya

ISBN: (纸本)9781457710001

This paper suggests three different approaches for online reactive power dispatch in wind farms by using a new heuristic optimization algorithm called Mean-Variance Mapping optimization (MVMO). optimization for any given operating point will result in minimum losses but doesn't allow the consideration of the cost of on-load tap changer (OLTC) movements. To solve this problem the authors propose a predictive optimization where the objective is extended by the number of OLTC tap changes. Besides several time steps ahead are optimized simultaneously by using the information provided by a short term power forecast. In addition the authors suggest a method for incorporating the reactive power optimization into the wind farm control loop so that it can be used online to determine the optimal distribution of reactive power generation by each wind turbine. The proposed methods are demonstrated in a representative offshore wind farm.

关键词： Reactive power dispatch Offshore wind farms Mean-Variance Mapping optimization Heuristic optimization Wind farms offshore wind farm Power dispatch Reactive power Dispatching optimal distribution Workstations On load tap changers optimization algorithms

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Non-contact electric-coupling-based and magnetic-field-sensing-assisted technique for monitoring voltage of overhead power transmission lines

Non-contact electric-coupling-based and magnetic-field-sensi...

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IEEE Sensors

作者： Ke Zhu Wing Kin Lee P. W. T. Pong Dept. of Electr. & Electron. Eng. Univ. of Hong Kong Hong Kong China

ISBN: (纸本)9781479982042

Adopting non-contact electric coupling for voltage monitoring avoids electrical connection to high-voltage transmission lines, making installation of the sensing platforms easier and enabling wide-area deployment. However, coupled transformation matrix is difficult to determine in practice as the exact spatial positions of the transmission lines are typically unknown and dynamic. This problem can be overcome by integrating magnetic field sensing with stochastic optimization algorithm. In this technique, magnetic signals emanated from the overhead transmission lines are measured by magnetic sensor arrays to determine the coupling coefficients so that the high voltage of the transmission lines can be deduced from the induced voltage in the copper bars on the ground. This proposed technique can be potentially implemented with low-cost copper induction bars and magnetoresistive (MR) sensors. Thus sectional and wide-area deployment may become possible and Ferranti effect and travelling lightning waves can then be monitored. Its enhanced sensing ability over potential transformers (PTs) can largely improve transient-fault identification and save cost for transmission-network inspection.

关键词： electric coupling magnetic field sensing overhead transmission line voltage monitoring Power transmission lines GALVANIC voltage monitoring Transmission Lines high voltage transmission line magnetic field optimization algorithms

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Nonlinear Model Predictive Torque Control of a Load Commutated Inverter and Synchronous Machine

Nonlinear Model Predictive Torque Control of a Load Commutat...

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International Power Electronics Conference

作者： Stefan Almer Thomas Besselmann Joachim Ferreau ABB Corporate Research

ISBN: (纸本)9781479927067

The paper at hand considers the design of a controller for torque regulation of a variable speed synchronous machine fed by a line commutated rectifier and a load commutated inverter. The control approach is model predictive control where a constrained optimal control problem is solved to minimize the deviation of the torque from its reference. The dynamic model of the converters and drive is nonlinear and considers both the rectifier and inverter firing angles as control input. Controlling both firing angles simultaneously, as opposed to in a cascaded manner, implies improved potential for dynamic performance and disturbance rejection. In particular, the controller handles voltage dips on the line better than a conventional PI controller. The nonlinear MPC solution is implemented through on-line optimization. The optimization algorithm has been implemented on an embedded system and has been shown to execute sufficiently fast for the targeted control frequency.

关键词： Torque control synchronous machines Inventors Frequency control Rectifiers voltage sags LOAD Embedded systems Torque Disturbance rejection Controllers dynamic characteristics Converters optimization algorithms Nonlinear Models

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Scenario-Based Video Event Recognition by Constraint Flow

Scenario-Based Video Event Recognition by Constraint Flow

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Suha Kwak Bohyung Han Joon Hee Han Department of Computer Science and Engineering POSTECH

ISBN: (纸本)9781457703942

We present a novel approach to representing and recognizing composite video events. A composite event is specified by a scenario, which is based on primitive events and their temporal-logical relations, to constrain the arrangements of the primitive events in the composite event. We propose a new scenario description method to represent composite events fluently and efficiently. A composite event is recognized by a constrained optimization algorithm whose constraints are defined by the scenario. The dynamic configuration of the scenario constraints is represented with constraint flow, which is generated from scenario automatically by our scenario parsing algorithm. The constraint flow reduces the search space dramatically, alleviates the effect of preprocessing errors, and guarantees the globally optimal solution for recognition. We validate our method to describe scenario and construct constraint flow for real videos and illustrate the effectiveness of our composite event recognition algorithm for natural video events.

关键词： Recognition (Psychology) Constraint Videotapes optimization algorithms Composites optimal solution Composite Video

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Improved SQP and SLSQP algorithms for Feasible Path-based Process Optimisation

arXiv

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arXiv 2024年

作者： Ma, Yingjie Gao, Xi Liu, Chao Li, Jie Centre for Process Integration Department of Chemical Engineering School of Engineering The University of Manchester ManchesterM13 9PL United Kingdom Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States School of Electronic and Information Engineering Tongji University Shanghai201804 China School of Mechanical and Electrical Engineering Jinggangshan University Jiangxi Ji'an343009 China

Feasible path algorithms have been widely used for process optimisation due to its good convergence. The sequential quadratic programming (SQP) algorithm is usually used to drive the feasible path algorithms towards optimality. However, existing SQP algorithms may suffer from inconsistent quadratic programming (QP) subproblems and numerical noise, especially for ill-conditioned process optimisation problems, leading to a suboptimal or infeasible solution. In this work, we propose an improved SQP algorithm (I-SQP) and an improved sequential least squares programming algorithm (I-SLSQP) that solves a least squares (LSQ) subproblem at each major iteration. A hybrid method through the combination of two existing relaxations is proposed to solve the inconsistent subproblems for better convergence and higher efficiency. We find that a certain part of the dual LSQ algorithm suffers from serious cancellation errors, resulting in an inaccurate search direction or no viable search direction generated. Therefore, the QP solver is used to solve LSQ subproblems in such a situation. The computational results indicates that I-SLSQP is more robust than fmincon in MATLAB, IPOPT, Py-SLSQP and I-SQP. It is also shown that I-SLSQP and Py-SLSQP is superior to I-SQP for ill-conditioned process optimisation problems, whilst I-SQP is more computationally efficient than I-SLSQP and Py-SLSQP for well-conditioned problems. © 2024, CC BY-NC-ND.

关键词： optimization algorithms

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Complexity analysis of regularization methods for implicitly constrained least squares

arXiv

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arXiv 2023年

作者： Onwunta, Akwum Royer, Clément W. Department of Industrial and Systems Engineering Lehigh University 200 West Packer Avenue BethlehemPA18015-1582 United States LAMSADE CNRS Université Paris Dauphine-PSL Place du Maréchal de Lattre de Tassigny Paris75016 France

optimization problems constrained by partial differential equations (PDEs) naturally arise in scientific computing, as those constraints often model physical systems or the simulation thereof. In an implicitly constrained approach, the constraints are incorporated into the objective through a reduced formulation. To this end, a numerical procedure is typically applied to solve the constraint system, and efficient numerical routines with quantifiable cost have long been developed for that purpose. Meanwhile, the field of complexity in optimization, that estimates the cost of an optimization algorithm, has received significant attention in the literature, with most of the focus being on unconstrained or explicitly constrained problems. In this paper, we analyze an algorithmic framework based on quadratic regularization for implicitly constrained nonlinear least squares. By leveraging adjoint formulations, we can quantify the worst-case cost of our method to reach an approximate stationary point of the optimization problem. Our definition of such points exploits the least-squares structure of the objective, and provides new complexity insights even in the unconstrained setting. Numerical experiments conducted on PDE-constrained optimization problems demonstrate the efficiency of the proposed framework. © 2023, CC BY.

关键词： optimization algorithms

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