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2022 international Conference on System Science and engineering, ICSSE 2022

作者： Su, Yu-Ting Liu, En-Hauh Li, Tzuu-Hseng S. National Cheng Kung University AiRobots Lab. Dept. of Electrical Eng. Tainan Taiwan

ISBN: (数字)9781665488525

ISBN: (纸本)9781665488525

Political Optimizer is a metaheuristic algorithm proposed by scholars recently. It reflects good convergence speed as well as exploitation and exploration capabilities in problems relevant to finding optimal solutions. However, regarding the global optimum, it still has spaces to be improved. This study proposed a Hybrid Political Algorithm, which makes balanced modifications to the exploration process and algorithm functions to effectively improve searching for the optimal solutions in the mathematic problems of congress on Evolutionary computation and engineering problems by enabling the particles to move smarter in the computation process. Based on the experimental results, improving the performance of algorithm functions using the methods proposed in this study can also help avoid falling into the local optimum. In addition, compared to other algorithms, this improved algorithm can generate more accurate results. © 2022 IEEE.

关键词： Chaotic systems

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Enabling Dual Subpopulations and Clustering for Multimodal Multiobjective Optimization 13

Enabling Dual Subpopulations and Clustering for Multimodal M...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Su, Yu-Cheng Chen, Yi-Ruei Ting, Chuan-Kang National Tsing Hua University Department of Power Mechanical Engineering Hsinchu30013 Taiwan National Tsing Hua University Department of Computer Science Hsinchu30013 Taiwan

ISBN: (纸本)9798350308365

Multimodal multiobjective optimization problems (MMOPs) present a specific category of multiobjective problems. Unlike conventional multiobjective problems where Pareto opti-mal solutions gather in one or few regions in the decision space, MMOPs aim to find solutions spread across various regions. In particular, these solutions may share similar locations in objective space, making the problem more complex and challenging. Most multiobjective optimization evolutionary algorithms (MOEAs) barely preserve diversity in decision space, whereas multimodal multiobjective optimization evolutionary algorithms (MMOEAs) require additional techniques such as clustering to maintain diversity in the decision space. In this study, we propose a new method that enables two sub populations and a clustering technique for diversity preservation. Furthermore, different clustering methods are compared to investigate their impact on performance. The experimental results show that the proposed method achieves better inverted generational distance in decision space (IGDX), indicating the effectiveness of the diversity preservation approach. In addition, the varying IGDX and inverted generational distance (IGD) obtained from different clustering methods reveal their different strengths in solving MMOPs. © 2024 IEEE.

关键词： Optimization algorithms

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Decomposition of Difficulties in Complex Optimization Problems using a Bilevel Approach 13

Decomposition of Difficulties in Complex Optimization Proble...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Sinha, Ankur Pujara, Dhaval Singh, Hemant Kumar Centre for Data Science and Ai Indian Institute of Management Gujarat Ahmedabad380015 India School of Engineering and Technology The University of New South Wales CanberraACT2610 Australia

ISBN: (纸本)9798350308365

Practical optimization problems may contain differ-ent kinds of difficulties that are often not tractable if one relies on a particular optimization method. Different optimization approaches offer different strengths that are good at tackling one or more difficulty in an optimization problem. For instance, evolutionary algorithms have a niche in handling complexities like discontinuity, non-differentiability, discreteness and non-convexity. However, evolutionary algorithms may get computationally expensive for mathematically well behaved problems with large number of variables for which classical mathematical programming approaches are better suited. In this paper, we demonstrate a decomposition strategy that allows us to synergistically apply two complementary approaches at the same time on a complex optimization problem. Evolutionary algorithms are useful in this context as their flexibility makes pairing with other solution approaches easy. The decomposition idea is a special case of bilevel optimization that separates the difficulties into two levels and assigns different approaches at each level that is better equipped at handling them. We demonstrate the benefits of the proposed decomposition idea on a wide range of test problems. © 2024 IEEE.

关键词： Optimization algorithms

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computation OF PROP-FAN ENGINE INSTALLATION AERODYNAMICS.

COMPUTATION OF PROP-FAN ENGINE INSTALLATION AERODYNAMICS.

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ICAS Proceedings 1984, 14th congress of the international Council of the Aeronautical Sciences.

作者： Boppe, C.W. Rosen, B.S. Grumman Aerospace Corp Bethpage NY USA Grumman Aerospace Corp Bethpage NY USA

ISBN: (纸本)0915928892

A computational method has been enhanced to permit numerical evaluations of transonic aerodynamic phenomena associated with prop-fan engine installations. Comparisons with experimental data and results using other algorithms provide a basis for augmenting intuition to determine which aspects of the physical flow and the computerized flow simulation are most important. Features of the prop-fan slipstream and the engine nacelle are modeled. In addition, a wind tunnel test was conducted to study the interference effect generated by a simple nacelle which is embedded in a wing surface. Observations that relate to future engineering applications are included. (Author abstract. )

关键词： AERODYNAMICS

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GPU-Accelerated Simulation of Two-Phase Incompressible Fluid Flow using a Level-Set Method for Interface Capturing

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ASME international Mechanical engineering congress and Exposition

作者： Kelly, Jesse Univ Cent Florida Orlando FL 32816 USA

ISBN: (纸本)9780791843826

computational fluid dynamics has seen a surge of popularity as a tool for visual effects animators over the past decade since Stam's seminal Stable Fluids paper [1]. Complex fluid dynamics simulations can often be prohibitive to run due to the time it takes to perform all of the necessary computations. This project proposes an accelerated two-phase incompressible fluid flow solver implemented on programmable graphics hardware. Modern graphics-processing units (GPUs) are highly parallel computing devices, and in problems with a large potential for parallel computation the GPU may vastly out-perform the CPU. This project will use the potential parallelism in the solution of the Navier-Stokes equations in writing a GPU-accelerated flow solver. NVIDIA's Compute-Unified-Device-Architecture (CUDA) language will be used to program the parallel portions of the solver. CUDA is a C-like language introduced by the NVIDIA Corporation with the goal of simplifying general-purpose computing on the GPU. CUDA takes advantage of data-parallelism by executing the same or near-same code on different data streams simultaneously, so the algorithms used in the flow solver will be designed to be highly data-parallel. Most finite difference-based fluid solvers for computer graphics applications have used the traditional staggered marker-and-cell (MAC) grid, introduced by Harlow and Welsh [2]. The proposed approach improves upon the programmability of solvers such as these by using a non-staggered (collocated) grid. An efficient technique is implemented to smooth the pressure oscillations that often result from the use of a collocated grid in the simulation of incompressible flows. To be appropriate for visual effects use, a fluid solver must have some means of tracking fluid interfaces in order to have a renderable fluid surface. This project uses the level-set method [3] for interface tracking. The level set is treated as a scalar property, and so its propagation in time is computed using

关键词： Simulation Flow (Dynamics) algorithms Modal assurance criterion Navier-Stokes equations Hardware Surges Pressure Scalars Fluids Graphics processing units computation Incompressible fluids Computers Oscillations Fluid dynamics computational fluid dynamics

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Classification, analysis and comparison of non-blind image quality measure

International Journal of Signal Processing, Image Processing...

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international Journal of Signal Processing, Image Processing and Pattern Recognition 2016年第4期9卷 347-360页

作者： Rashid, Aqsa Rahim, Muhammad Khurrum Department of Computer Science and Information Technology The Islamia University of Bahawalpur Pakistan Department of Electrical Engineering NUCES FAST Pakistan

computation of extent of image visual excellence is of essential importance for many image and video processing appliances, where the objective of quality evaluation algorithms is to automatically evaluate the excellence of images. This paper is the detailed experimental study, classification, analysis and comparison of the subjective non-blind image quality measures. After analysis, evaluation and comparison, these schemes are classified into two groups on the basis of similarity and dissimilarity check. It also scrutinizes the statistical recital of all the quality measures. © 2016 SERSC.

关键词： Quality control

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Interference-aware virtual machine placement in cloud computing systems

Interference-aware virtual machine placement in cloud comput...

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2012 international Conference on Computer and Information Science, ICCIS 2012 - A Conference of World engineering, Science and Technology congress, ESTCON 2012

作者： Lin, Jenn-Wei Chen, Chien-Hung Department of Computer Science and Information Engineering Fu Jen Catholic University Taiwan

ISBN: (纸本)9781467319386

Cloud computing provides scalable computing and storage resources. These scalable resources can be dynamically organized as many virtual machines (VMs) to run user applications based on a pay-per-use basis. This paper investigates the interference-aware VM placement (IAVMP) problem. In addition to fully exploiting the resources of PMs, the IAVMP problem considers the quality of service (QoS) requirements of user applications and the VM interference reduction. We first formulate the IAVMP problem by an Integer Linear Programming (ILP) model to solve it optimally. Due to the computation complexity of the ILP model, we also propose a polynomial-time heuristic algorithm to efficiently solve the IAVMP problem. Finally, simulation experiments are performed to demonstrate the effectiveness of the proposed heuristic algorithm by comparing with other VM placement algorithms. © 2012 IEEE.

关键词： Quality of service

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Feature Selection for Computer-Aided Diagnosis via a Novelty Designed Binary Harris Hawk Algorithm 13

Feature Selection for Computer-Aided Diagnosis via a Novelty...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Dong, Minhui Todo, Yuki School of Electrical and Computer Engineering Kanazawa University Ishikawa Japan

ISBN: (纸本)9798350308365

Computer-aided diagnosis (CAD) has become one of the hot research directions in the combination of medicine and machine learning (ML). While the characteristic of high feature number and low sample size in disease datasets makes it difficult for ML to make effective predictions. Currently, one of the most commonly used methods to address this issue is the use of heuristic algorithms for feature selection (FS). However, traditional heuristic algorithms still have certain limitations. On one hand, single-objective algorithms only focus on model's accu-racy, which always leads to a large number of redundant features remaining after FS. On the other hand, while multi-objective algorithms may result in fewer features, they often significantly reduce accuracy compared to single-objective algorithms. To solve these shortcoming, we proposes a novel FS algorithm based on harris hawk optimization (HHO) algorithm, attempting to achieve a better balance between the number of features and model's accuracy. Specifically, since the original HHO algorithm was only used for continuous numerical problems, we defines a new set of discrete operations for HHO to make it suitable for feature selection. Additionally, we introduce an aggregation function that allows the model to consider both model's accuracy and the number of features with a single objective. To mitigate the accuracy degradation caused by the aggregation function, a scale-free network was employed to improve the model's optimization capability. To comprehensively evaluate the proposed algorithm, we compare it with commonly used single-objective algorithms and multi-objective algorithms. Experimental results show that the proposed algorithm offers a better balance compared to commonly used heuristic algorithms. © 2024 IEEE.

关键词： Optimization algorithms

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Big RDF Data Storage, computation, and Analysis: A Strawman's Arguments 39

Big RDF Data Storage, Computation, and Analysis: A Strawman'...

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39th IEEE international Conference on Distributed Computing Systems (ICDCS)

作者： Yuan, Pingpeng Lin, Longlong Kou, Zhijuan Jin, Hai Liu, Ling Huazhong Univ Sci & Technol Sch Comp Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol & Syst LabCluster & Grid Comp Wuhan Peoples R China Georgia Inst Technol Coll Comp Sch Comp Sci Distributed Data Intens Syst Lab Atlanta GA 30332 USA

ISBN: (纸本)9781728125190

RDF data is big and continues to grow rapidly. RDF data sets are typically viewed as heterogeneous graph data sets with complex correlations and multifaceted heterogeneity. Although there have been flurry of research on processing and analyzing RDF data, efficient storage, computation, analysis of big and growing RDF data continue to challenge multiple computer science disciplines, ranging from systems, network computing, data management to data analytics. In this paper, we make use of strawman arguments on big RDF data challenges with respect to storage, computation, analysis, focusing on identifying the grand challenges in developing high performance RDF storage, parallel computation, efficient distribution, and smart RDF analytics. We attempt to answer a number of important and frequently asked questions: (1) Do we really need RDF-specific storage techniques and algorithms for building efficient and high performance RDF stores? (2) How hard can it be to effectively parallelize big RDF data for high performance storage, computation, and mining? (3) What type of distribution models can we employ to scale RDF processing models and algorithms for real-time querying (subgraph pattern matching) and mining of big RDF data? (4) Can machine learning algorithms be leveraged to effectively mine and dive into RDF data? We attempt to answer these questions by sharing some of our results, our unique experiences, and our lessons learned from a collection of research projects in RDF systems research and development. We will also discuss our ongoing research endeavors, potential applications and avenues for future work.

关键词： Storage Graph Partitioning computation Framework Query Processing Graph Mining

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A hybrid genetic algorithm for color image denoising

A hybrid genetic algorithm for color image denoising

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2016 IEEE congress on Evolutionary computation, CEC 2016

作者： Fajardo-Delgado, Daniel Sanchez, Maria Guadalupe Molinar-Solis, Jesus Ezequiel Fernandez-Zepeda, Jose Alberto Vidal, Vicente Verdiu, Gumersindo Department of Systems and Computation Instituto Tecnológico de Cd. Guzmán Cd. Guzman Jal.49000 Mexico Department of Electric and Electronics Instituto Tecnológico de Cd. Guzmán Cd. Guzman Jal.49000 Mexico Department of Computer Science Centro de Investigatión Científicay de Educación Superior de Ensenada Ensenada BC22860 Mexico Department of Informatics Systems and Computing Universitat Politècnica de València Valencia46022 Spain Department of Chemical and Nuclear Engineering Universitat Politècnica de València Valencia46022 Spain

ISBN: (纸本)9781509006229

In this paper, we address the problem of impulsive noise reduction in color images through an evolutionary approach. We designed a new hybrid genetic algorithm, called GARIN, which takes as input a noisy image and generates as output a reduced noise version of the same image. As part of its evolutionary process, GARIN integrates the execution of robust and adaptive filters from literature with the aim to combine and improve their results. Unlike most genetic algorithms designed for color image denoising, our algorithm evolves a set of images instead of the set of parameters of the filters. Experimental results show that, compared with other filters of literature, the algorithm GARIN efficiently removes impulsive noise in color images while, at the same time, it preserves their main features. © 2016 IEEE.

关键词： Image denoising

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