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International Electrical Engineering Congress (iEECON)

作者： Wachirawich Tantichaiwanich Keerati Chayakulkheeree School of Electrical Engineering Institute of Engineering Suranaree University of Technology Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper specializes on determining of optimal overcurrent relay coordination, which is imperative for primary and backup overcurrent relays using hybrid particle swam optimization (PSO) and linear programing (LP). Inspection is conducted on a 4-bus radial system connected to distributed energy resources (DER). The pick-up current is determined using PSO, meanwhile the time multiplier setting (TMS) is determined by LP, to solve the overcurrent relay (OCR) coordination problem. The results demonstrate a significant reduction in relay operating time, showing improved efficiency compared to conventional methods.

关键词： Electrical engineering Adaptive systems Optical character recognition Microgrids Inspection linear programming Distributed power generation Relays Particle swarm optimization Optimization

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Optimal Scheduling Scheme for Ore Transshipment Yard Based on Probabilistic Calculation Model 24

Optimal Scheduling Scheme for Ore Transshipment Yard Based o...

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Proceedings of the 2024 4

作者： Ding Tairan Wu Yuhao Zhang Yang Hailing Ma Southwestern University of Finance and Economics Chengdu Sichuan China Monash University Melbourne Melbourne Australia

ISBN: (纸本)9798400718298

Steel is one of the most important basic materials in modern industry, and how to improve its transportation efficiency is an important factor affecting the growth of economic benefits. This paper studies the optimal loading and unloading scheduling problem in ore transshipment yards involving both road and rail transport. A mathematical model was established, and dynamic programming and probabilistic models were used to optimize the loading and unloading process. When facing complex problems, the advantages of both can be leveraged simultaneously. Establishing a dynamic programming framework that incorporates probabilistic factors can more accurately reflect the actual situation and lead to more effective solutions. The research aims to reduce transportation costs and improve logistics efficiency by optimizing scheduling. The paper primarily analyzes issues such as the uncertainty of train arrival times, the need for a second truck to accelerate the ore loading process, and demurrage fees incurred. It presents the optimal loading and unloading scheduling *** the first problem, a dynamic programming model was used to analyze the optimal loading and unloading scheme for iron ore over three days. It is assumed that trains arrive during a fixed time from 8:00 to 18:00, and detailed models are established for train loading time, truck transport time, and other factors. Depending on the train arrival time and ore stockpile at the loading platform, the need for a second truck is determined, and the minimum cost is calculated. Results show the minimum cost on the first day was 24,000 yuan, 26,000 yuan on the second day, and 69,333.33 yuan on the third day. The paper discusses the train arrival times, ore stockpiles, and work arrangements at the loading platform for each day in detail. In the second problem, the train arrival times are assumed to be random between 8:00 and 18:00. A probabilistic model was introduced to deal with the uncertainty in train arrival times.

关键词： linear programming

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Guest Editorial

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IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 2021年第4期16卷 17-18页

作者： Feng, Liang Wang, Handing Ong, Yew-Soon Tan, Kay Chen Jin, Yaochu Chongqing Univ Chongqing Peoples R China Xidian Univ Xian Peoples R China Nanyang Technol Univ Singapore Singapore Hong Kong Polytech Univ Hong Kong Peoples R China Univ Surrey Surrey England

Evolutionary Algorithms (EAs) are nature-inspired population-based search methods which work on Darwinian principles of natural selection. Due to their strong search capability and simplicity of implementation, EAs have been successfully applied to solve many complex optimization problems, which cannot be easily solved by traditional mathematical programming approaches, such as linear programming, quadratic programming, and convex optimization. Despite the great success enjoyed by EAs, it is worth noting that existing EA solvers usually conduct the search process from scratch, regardless of how similar the new problem encountered is to those already solved in the past. Therefore, conventional EAs do not learn from previous problems and the search capabilities of the EA solvers do not automatically grow with problem-solving experiences. However, in reality, since problems seldom exist in isolation, solving one problem may thus yield useful information for solving other related problems. In the literature, there is a growing interest in conducting research on evolutionary transfer optimization (ETO) in recent years: a paradigm that integrates EA solvers with knowledge learning and transfer across related domains to achieve better optimization efficiency and performance.

关键词： Special issues and sections Evolutionary computation Search problems Quadratic programming Problem-solving Mathematical programming linear programming Convex functions

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Design of Crop Planting Strategy Based on Genetic Algorithm

Design of Crop Planting Strategy Based on Genetic Algorithm

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Electronics, Energy Systems and Power Engineering (EESPE), IEEE International Conference on

作者： Yingjia Hu Haoran Wang Jiayi Shi Shandong University of Science and Technology Jinan Shandong China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

With the intensification and intelligent development of agricultural production, optimizing planting strategies to obtain maximum profits has become increasingly important. This study uses Matlab to process data, constructs a linear programming model, and applies genetic algorithms and Monte Carlo simulations to explore strategies for optimizing crop and land allocation to maximize planting income over the next seven years under crop rotation and cost constraints. The study also considered crop market volatility, introduced alternative and complementary factors, and evaluated the impact of these factors on planting strategies and total returns through multiple regression analysis and stochastic dynamic programming models, aiming to provide comprehensive planting strategies for adapting to market and climate change.

关键词： Costs Monte Carlo methods Crops Stochastic processes Production linear programming Mathematical models Regression analysis Optimization Genetic algorithms Climate change

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Optimization Model for Hydropower Boundary Conditions for Minimizing Abandoned Water Volume

Optimization Model for Hydropower Boundary Conditions for Mi...

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Advances in Electrical, Electronics and Computing Technology (EECT), International Conference on

作者： Li Yixin Wu Jiekang Lei Zhen Kan Dongwang Tang Wenhao Yang Yiehao Strategic Planning Department Guangdong Power Grid Co. Ltd Guangzhou China School of Automation Guangdong University of Technology Guangzhou China

ISBN: (数字)9798331541545

ISBN: (纸本)9798331541552

A scheduling optimization model for medium and long-term hydropower generation is presented in this paper, Aiming at the complex nonlinear constraints faced by hydropower participation in spot market, the subjective setting of ahead-day spot market boundary, and the decoupling from the medium and long-term scheduling plan. Based on the operating mechanism of ahead-day spot market, this paper decouples multiple nonlinear constraints of hydropower from the clearing model of spot market by setting boundary conditions for spot market of hydropower. Based on the interval prediction values of daily operating boundary conditions such as daily inflow and daily load, and from the perspectives of hydropower and scheduling, a boundary optimization model for the hydropower spot market was constructed with the objective functions of maximizing power generation efficiency and minimizing abandoned water volume, forming the initial boundary for hydropower participation in spot market.

关键词： Economics Computers Processor scheduling Optimization models Hydroelectric power generation Boundary conditions linear programming Consumer electronics Load modeling

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Non Dominated Sorting Genetic Algorithm (NSGA-II) Modelling for AZ 31 Magnesium Alloy Composite

Non Dominated Sorting Genetic Algorithm (NSGA-II) Modelling ...

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Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Amit Tiwari Payal Bansal Himanshu Vasnani Neny Pandel Department of Mechanical Engineering Suresh Gyan Vihar University Jaipur Rajasthan India Department of Electronics Communication & Engineering Poornima Institute of Engineering & Technology Jaipur Rajasthan India Department of Computer Science Engineering & IT Suresh Gyan Vihar University Jaipur Rajasthan India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

The rolling process has proven to have a significant effect on the mechanical properties of metallic alloys especially in their strength. In the present research work, process parameters are optimized with a view to maximize hardness and tensile strength of experimental alloy by employing a Multi-Objective Genetic Algorithm (NSGA-II) along with linear regression. The experimental data used in this predictive modeling was from fabricated magnesium AZ31 alloy composite produced through stir casting. As-cast alloys were homogenized at 500°C for 12 hours, followed by quenching in ice water. The as-quenched material was then hot rolled at different rolling temperatures, number of rolling passes, and initial stock thickness to study the process. The rolled materials were subjected to hardness and tensile strength measurements as dependent variables. Instead of training an artificial neural network to specify the objective function for NSGA-II, we created a mathematical model using regression analysis. This equation is used as the objective function for multi-objective optimization with NSGA-II. The NSGA-II predicted value for the maximum hardness of 107.41 VHN, is in close agreement with the obtained result of 106.6 VHN. Similarly, the maximum experimental tensile strength was 266.82 MPa whereas NSGA-II said that it will be approximately 253 MPa for this value. Under these circumstances, the predictive accuracy of NSGA-II was within 1-5% deviation from experimental results (i.e. good correspondence between the optimization strategy with experimental data).

关键词： Temperature measurement Training Composite materials Predictive models linear programming Mathematical models Magnesium Optimization Genetic algorithms Sorting

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Multi-Objective Optimization for Inverse Kinematics of Robotic Arm with Joint Configuration

Multi-Objective Optimization for Inverse Kinematics of Robot...

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IEEE Southeastcon

作者： Xibin Zhou Tanyaradzwa Chinyai Lizhe Tan Department of Electrical and Computer Engineering Purdue University Northwest Hammond IN USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The hardware configuration of modern robots is rapidly developing, yet the fundamental problem of inverse kinematics is still mostly based on analytical or numerical solutions applied to mainstream robotic arms. These traditional methods for inverse kinematics result in an infinite number of solutions when encountering singularities, leading to uncertainty in which solution the robotic arm should use. In this paper, the inverse kinematics of two collaborative robot arm models are investigated using a multi-objective optimization weighted sum method and a particle swarm optimization algorithm. The objective functions include the position and orientation errors of the end-effector and the total rotation amount of the robotic joints. Experimental results show that including a total rotation amount of robotic joints in the objective functions effectively reduces energy consumption. Finally, through simulations and physical model validations according to a series of trajectories obtained, the developed algorithm is proven to achieve the inverse kinematics solution within the allowable error range for engineering requirements.

关键词： Program processors Uncertainty Kinematics Arms linear programming End effectors Trajectory Particle swarm optimization Robots Optimization

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Using the quasi-oppositional artificial hummingbird algorithm, a probabilistic optimal power flow for an integrated renewable power system comprising wind and solar

Using the quasi-oppositional artificial hummingbird algorith...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Manas Mahato Alok Kumar Manish Kumar Singh Sneha Sultana Sourav Paul Susanta Dutta Provas Kumar Roy Electrical Engineering Dr. B. C. Roy Engineering College Durgapur India Electrical Engineering Kalyani Government Engineering College Kalyani India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Optimal Power Flow (OPF) is crucial for efficient and sustainable power system management, aiming to minimize operational costs and emissions while meeting system constraints. This paper introduces the artificial hummingbird algorithm (AHA) to solve the OPF problem, enhanced with Quasi-Oppositional Based Learning (QOBL) for improved convergence and solution accuracy. The proposed QOAHA is validated on the IEEE 57-bus system, demonstrating superior performance compared to existing optimization techniques in cost and emission reduction. By combining the exploration capability of AHA with QOBL’s accelerated search, the algorithm achieves robust and efficient results. This hybrid approach offers a promising direction for addressing complex power system challenges.

关键词： Renewable energy sources Costs Metaheuristics Learning (artificial intelligence) Probabilistic logic Minimization linear programming Hybrid power systems Topology Load flow

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GeoDiffuser: Geometry-Based Image Editing with Diffusion Models

GeoDiffuser: Geometry-Based Image Editing with Diffusion Mod...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Rahul Sajnani Jeroen Vanbaar Jie Min Kapil Katyal Srinath Sridhar Brown University Amazon Robotics

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

The success of image generative models has enabled us to build methods that can edit images based on text or other user input. However, these methods are imprecise, require additional information, or are limited to only 2D image edits. We present GeoDiffuser, a zero-shot optimization-based method that unifies common 2D and 3D image-based object editing capabilities into a single method. Our key insight is to view image editing operations as geometric transformations. We show that these transformations can be directly incorporated into the attention layers in diffusion models to implicitly perform editing operations. Our training-free optimization method uses an objective function that seeks to preserve object style but generate plausible images, for instance with accurate lighting and shadows. It also inpaints disoccluded parts of the image where the object was originally located. Given a natural image and user input, we segment the foreground object [27] and estimate a corresponding transform which is used by our optimization approach for editing. Figure 1 shows that GeoDiffuser can perform common 2D and 3D edits like object translation, 3D rotation, and removal. We present quantitative results, including a perceptual study, that shows how our approach is better than existing methods.

关键词： Image segmentation Computer vision Three-dimensional displays Accuracy Translation Optimization methods Lighting Transforms Diffusion models linear programming

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Practical Application of Out-of-Kilter Algorithm

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Chinese Business Review 2023年第2期22卷 86-94页

作者： Irma Ibrišimović Zoran Jasak Aldijana Omerović ElvirČajić University of Tuzla TuzlaBosnia and Herzegovina FINra TuzlaBosnia and Herzegovina

The algorithm under this name, together with the variants, is a method that solves the problems of optimal flow and costs. Examples of such problems are planning and procurement, scheduling by contractors, distribution and supply systems, transport on the road or rail network, electricity transmission, computer and telecommunications networks, pipe transmission systems (water, oil, …), and the like. The main goal of any business organization is to increase profits and satisfy its customers. Because business is an integral part of our environment, their goals will be limited by certain environmental factors and economic conditions. The out-of-kilter algorithm is used to solve a complex allocation problem involving interactive and conflicting personal choices subject to interactive resource constraints. The paper presents an example of successful use of this algorithm and proposes an extension to the areas of corporate and social planning. Customer demand, warehousing, and factory capacity were used as input for the model. First, we propose a linear programming approach to determine the optimal distribution pattern to reduce overall distribution costs. The proposed model of linear programming is solved by the standard simplex algorithm and the Excel-solver program. It is noticed that the proposed model of linear programming is suitable for finding the optimal distribution pattern and total minimum costs.

关键词： out-of-kilter algorithm linear programming minimization minimum cost flow simplex algorithm

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