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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 ...

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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ACM Transactions on Algorithms 2025年第2期21卷

作者： Kavitha, Telikepalli Makino, Kazuhisa Schlotter, Ildikó Yokoi, Yu School of Technology and Computer Science Tata Institute of Fundamental Research Mumbai India Kyoto University Kyoto Japan HUN-REN Centre for Economic and Regional Studies Budapest Hungary School of Computing Institute of Science Tokyo Meguro-ku Japan

Consider a directed, rooted graph G = (V ∪ {r}, E) where each vertex in V has a partial order preference over its incoming edges. The preferences of a vertex naturally extend to preferences over arborescences rooted at r. We present a polynomial-time algorithm that decides whether a given input instance admits a popular arborescence, i.e., one for which there is no "more popular"arborescence. In fact, our algorithm solves the more general popular common base problem in the intersection of two matroids: we are given an arbitrary matroid and a partition matroid Mpart over E, where partition classes correspond to a set V of agents with |V| = rank(M) and each agent has a partial order preference over its associated partition class;the problem asks for a common base of M and Mpart such that there is no "more popular"common base. Our algorithm is combinatorial and can be regarded as a primal-dual algorithm. It searches for a solution along with its dual certificate, a chain of subsets of E, witnessing its popularity. Our generalized results, expressed in terms of matroids, demonstrate that the identification of agents with vertices of the graph in the popular arborescence problem is not essential. We also study the related popular common independent set problem. For the case with weak rankings, we formulate the popular common independent set polytope and thus show that a minimum-cost popular common independent set can be computed efficiently. By contrast, we prove that it is NP-hard to compute a minimum-cost popular arborescence, even when rankings are strict. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： 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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An exploratory computational analysis of dual degeneracy in mixed-integer programming

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EURO JOURNAL ON COMPUTATIONAL OPTIMIZATION 2020年第3-4期8卷 241-261页

作者： Gamrath, Gerald Berthold, Timo Salvagnin, Domenico Zuse Inst Berlin D-14195 Berlin Germany I2DAMO GmbH Englerallee 19 D-14195 Berlin Germany Fair Isaac Germany GmbH Stubenwald Allee 19 D-64625 Bensheim Germany DEI Via Gradenigo 6-B I-35131 Padua Italy

Dual degeneracy, i.e., the presence of multiple optimal bases to a linear programming (LP) problem, heavily affects the solution process of mixed integer programming (MIP) solvers. Different optimal bases lead to different cuts being generated, different branching decisions being taken and different solutions being found by primal heuristics. Nevertheless, only a few methods have been published that either avoid or exploit dual degeneracy. The aim of the present paper is to conduct a thorough computational study on the presence of dual degeneracy for the instances of well-known public MIP instance collections. How many instances are affected by dual degeneracy? How degenerate are the affected models? How does branching affect degeneracy: Does it increase or decrease by fixing variables? Can we identify different types of degenerate MIPs? As a tool to answer these questions, we introduce a new measure for dual degeneracy: the variable-constraint ratio of the optimal face. It provides an estimate for the likelihood that a basic variable can be pivoted out of the basis. Furthermore, we study how the so-called cloud intervals-the projections of the optimal face of the LP relaxations onto the individual variables-evolve during tree search and the implications for reducing the set of branching candidates.

关键词： Mixed integer programming Dual degeneracy linear programming Branch-and-bound

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Blood Pressure Regulation Using a FOPID Controller Based on a Hybrid-Evolutionary Optimization Algorithm

Blood Pressure Regulation Using a FOPID Controller Based on ...

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Applied Research in Electrical Engineering (AREE), National and International Conference on

作者： Abolfazl Abouali Hamed Mojallali Electrical Engineering Department Faculty of Engineering University of Guilan Rasht Iran

ISBN: (数字)9798350377170

ISBN: (纸本)9798350377187

In recent years, due to the strong need to control blood pressure in patients, especially patients with heart problems, several control structures have been designed and implemented to control their blood pressure. The reason for this is the instability of blood pressure in this group of patients, and nurses and doctors need to use methods to prevent this instability, which can be a fatal factor for patients. To achieve this goal, a fractional-order PID controller whose coefficients are calculated using a hybrid-evolutionary algorithm called BH-PSO has been used in this paper. This control combination can excessively improve the system’s output response. This control structure can significantly reduce the error rate and settling time. Also, for the objective function, three functions integral absolute error (IAE), integral squared error (ISE), and integral time absolute error (ITAE) have been used to compare and reduce the system’s error rate.

关键词： Sensitivity Error analysis Transfer functions Process control Medical services linear programming Pressure control Blood pressure Regulation Optimization

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CorrFill: Enhancing Faithfulness in Reference-Based Inpainting with Correspondence Guidance in Diffusion Models

CorrFill: Enhancing Faithfulness in Reference-Based Inpainti...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Kuan-Hung Liu Cheng-Kun Yang Min-Hung Chen Yu-Lun Liu Yen-Yu Lin National Yang Ming Chiao Tung University National Taiwan University MediaTek Inc. Taiwan NVIDIA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

In the task of reference-based image inpainting, an additional reference image is provided to restore a damaged target image to its original state. The advancement of diffusion models, particularly Stable Diffusion, allows for simple formulations in this task. However, existing diffusion-based methods often lack explicit constraints on the correlation between the reference and damaged images, resulting in lower faithfulness to the reference images in the inpainting results. In this work, we propose CorrFill, a training-free module designed to enhance the awareness of geometric correlations between the reference and target images. This enhancement is achieved by guiding the inpainting process with correspondence constraints estimated during inpainting, utilizing attention masking in self-attention layers and an objective function to update the input tensor according to the constraints. Experimental results demonstrate that CorrFill significantly enhances the performance of multiple baseline diffusion-based methods, including state-of-the-art approaches, by emphasizing faithfulness to the reference images.

关键词： Computer vision Correlation Tensors Diffusion models linear programming Image restoration 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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A Structural Topology Optimization Method Integrating Genetic Algorithm and Bidirectional Evolutionary Structural Optimization

A Structural Topology Optimization Method Integrating Geneti...

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Computational Intelligence on Engineering/Cyber Physical Systems (CIES), IEEE Symposium on

作者： Bingcheng Wang Satoru Hiwa Tomoyuki Hiroyasu Faculty of Life and Medical Sciences Doshisha University Kyoto Japan

ISBN: (数字)9798331508272

ISBN: (纸本)9798331508289

This study introduces a novel framework for topology optimization in structural design by integrating global and local search algorithms. Specifically, a genetic algorithm (GA) is employed as the global search method, leveraging its strengths in handling diverse objective functions while preserving interpretability throughout the optimization process. As a specific example of the framework, a system integrating GA as the global search algorithm and Bidirectional Evolutionary Structural Optimization (BESO) as the local search algorithm is introduced. GA is employed for its global exploration capability, enabling exploration on a diverse set of solutions for a wide range of optimization problems. BESO is applied as a local search method to refine solutions, enhancing the optimization results by precisely adjusting the structural design during the searching process. The effectiveness of this approach is demonstrated through two numerical examples focusing on their own primary objectives: one maximizing the structural stiffness, and the other maximizing the displacement. The results show that the combination of GA and BESO effectively meets the set design goals, highlighting the potential for significant structural design improvements through their synergistic effect, confirming the benefits of combining GA's ability to conduct global exploration with BESO's capacity to fine-tune solutions through local search. This study demonstrates the effectiveness of integrating GA and BESO in structural topology optimization, providing a powerful tool for advancing generative structural design.

关键词： Refining linear programming Three-dimensional printing Search problems Topology Optimization Stress Standards Genetic algorithms Pragmatics

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