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An efficient manta ray foraging optimization algorithm with individual information interaction and fractional derivative mutation for solving complex function extremum and engineering design problems

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APPLIED SOFT COMPUTING 2024年 150卷

作者： Liu, Jingsen Chen, Yang Liu, Xiaoyu Zuo, Fang Zhou, Huan Henan Univ Henan Int Joint Lab Theories & Key Technol Intelli Kaifeng Peoples R China Henan Univ Coll Software Kaifeng Peoples R China Henan Univ Technol Sch Artificial Intelligence & Big Data Zhengzhou Peoples R China Henan Univ Business Sch Kaifeng Peoples R China

The manta ray foraging optimization algorithm (MRFO) is a recently proposed meta-heuristic algorithm that mimics the foraging process of manta rays. It has yielded good outcomes in solving some optimization problems because its mechanism is clear, no additional parameters need to be set, and the balance between global and local search is good. Nonetheless, while dealing with high-dimensional global optimization and complex engineering optimization problems, there are also issues such as premature convergence, low optimization-seeking accuracy, or unstable solutions. To this end, this article proposes an efficient manta ray foraging optimization algorithm (NIFMRFO) by incorporating individual information interaction and fractional derivative mutation. First, to prevent premature convergence of the algorithm, a nonlinear cosine adjustment parameter is presented, which is intended to make the demand relationship between global exploration and local development more reasonable. Then, an information interaction strategy among random individuals is employed to expedite the rate at which the algorithm converges. Finally, a fractional derivative mutation strategy is utilized to continually enhance individuals' quality in each iteration, which not only increases the population diversity but also helps to improve the precision and stability of the search results. Theoretical analysis indicates that the improved NIFMRFO algorithm and basic MRFO algorithm have the same time complexity. In simulation experiments, the CEC2017 suite is used to conduct comparison tests with six superior-performance representative comparison algorithms in several dimensions. In terms of the optimization-seeking accuracy, convergence curve, violin plot, and Friedman average ranking, the analysis of these graphs and data shows that the NIFMRFO algorithm's ameliorated strategy improves superiority-seeking power, convergence speed, and steadiness. Meanwhile, the Wilcoxon rank-sum test result illustrates

关键词： Manta ray foraging optimization algorithm Information interaction Fractional derivative mutation optimization-seeking accuracy Engineering design optimization problems

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high level software synthesis of affine iterative algorithms onto parallel architectures 8th

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8th International conference on high performance Computing and Networking

作者： Marongiu, A Palazzari, P Cinque, L Mastronardo, F Univ Roma La Sapienza Dept Elect Engn Rome Italy Univ Roma La Sapienza Dipartimento Sci Informaz Rome Italy

ISBN: (纸本)3540675531

In this work a high Level software Synthesis (HLSS) methodology is presented. HLSS allows the automatic generation of a parallel program starting from a sequential C program. HLSS deals with a significant class of iterative algorithms, the one expressible through nested loops with affine dependencies, and integrates several techniques to achieve the final parallel program. The computational model of the System of Affine Recurrence Equations (SARE) is used. As first step in HLSS, the iterative C program is converted into SARE form;parallelism is extracted from the SARE through allocation and scheduling functions which are represented as unimodular matrices and are determined by moans of an optimization process. A clustering phase is applied to fit the parallel program onto a parallel machine with a fixed amount of resources (number of processors, main memory, communication channels). Finally, the parallel program to be executed on the target parallel system is generated.

关键词： Parallel architectures

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SIMBO - A FRAMEWORK FOR SIMULATION-BASED optimization USING BAYESIAN optimization 37

SIMBO - A FRAMEWORK FOR SIMULATION-BASED OPTIMIZATION USING ...

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37th ECMS International conference on Modelling and Simulation, ECMS 2023

作者： Zmijewski, Philipp Meseth, Nicolas University of Applied Sciences Osnabrück Oldenburger Landstraße 62 Osnabrück49090 Germany

ISBN: (纸本)9783937436807

SimBO is a flexible framework for optimizing discrete event-driven simulations (DES) using sequential optimization algorithms. While specifically designed for Bayesian optimization (BO) in the context of DES, SimBO can be applied to any black-box problem with other optimization algorithms. The framework consists of four encapsulated components - the black-box problem, the sequential optimization algorithm, a database for experiment configuration and results, and a web-based graphical user interface - that communicate via well-defined interfaces. Each component can be run in different environments, allowing for cooperation between different hardware- and software configurations. In our research context, SimBO’s architecture enabled BO algorithms to be run on a high-performance cluster with GPU support, while the simulation is executed on a local Windows machine using the Simio simulation software. The framework’s flexibility also makes it suitable for evolving from a research-focused tool to a production-ready, cloud-based optimization tool for modern algorithms. © ECMS Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni (Editors) 2023.

关键词： Graphical user interfaces

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Stencil Computation optimization and Auto-tuning on State-of-the-Art Multicore Architectures

Stencil Computation Optimization and Auto-tuning on State-of...

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International conference for high performance Computing, Networking, Storage and Analysis

作者： Datta, Kaushik Murphy, Mark Volkov, Vasily Williams, Samuel Carter, Jonathan Oliker, Leonid Patterson, David Shalf, John Yelick, Katherine NERSC CRD Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Univ Calif Berkeley Div Comp Sci Berkeley CA 94720 USA

ISBN: (纸本)9781424428342

Understanding the most efficient design and utilization of emerging multicore systems is one of the most challenging questions faced by the mainstream and scientific computing industries in several decades. Our work explores multicore stencil (nearest-neighbor) computations - a class of algorithms at the heart of many structured grid codes, including PDE solvers. We develop a number of effective optimization strategies, and build an auto-tuning environment that searches over our optimizations and their parameters to minimize runtime, while maximizing performance portability. To evaluate the effectiveness of these strategies we explore the broadest set of multicore architectures in the current HPC literature, including the Intel Clovertown, AMD Barcelona, Sun Victoria Falls, IBM QS22 PowerWell 8i, and NVIDIA GTX280. Overall, our auto-tuning optimization methodology results in the fastest multicore stencil performance to date. Finally, we present several key insights into the architectural trade-offs of emerging multicore designs and their implications on scientific algorithm development.

关键词： software architecture

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performance analysis of the ATLAS Second-Level Trigger software

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2002年第2期49卷 383-388页

作者： Bogaerts, JAC Botterill, DR Li, WD Middleton, RP Werner, P Wickens, FJ Zobernig, H CERN European Ctr Nucl Res CH-1211 Geneva 23 Switzerland Rutherford Appleton Lab Didcot OX11 0QX Oxon England Univ Wisconsin Dept Phys Madison WI 53706 USA

In this paper, we analyze the performance of the prototype software developed for the ATLAS Second-Level Trigger. The 00 framework written in C++ has been used to implement a distributed system which collects (simulated) detector data on which it executes event selection algorithms. The software has been used on testbeds of up to 100 nodes with various interconnect technologies. The final system will have to sustain traffic of similar to40 Gb/s and require an estimated number of similar to750 processors. Timing measurements are crucial for issues such as trigger decision latency, assessment of required CPU and network capacity, scalability, and load-balancing. In addition, final architectural and technological choices, code optimization, and system tuning require a detailed understanding of both CPU utilization and trigger decision latency. In this paper, we describe the instrumentation used to disentangle effects due to such factors as OS system intervention, blocking on interlocks (applications are multithreaded), multiple CPUs, and I/O. This is followed by an analysis of the measurements and concluding with suggestions for improvements to the ATLAS T)rigger/DAQ dataflow components in the next phase of the project.

关键词： computer network performance data acquisition data communication high-energy physics timing trigger

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Approximation of nonlinear Term in Model Robotic

Approximation of Nonlinear Term in Model Robotic

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International conference on Control, Automation and Diagnosis (ICCAD)

作者： Krifa, Abdelkader Bouzrara, Kais Univ Monastir ISIMA LARATSI Monastir Tunisia Univ Monastir ENIM LARATSI Monastir Tunisia

ISBN: (纸本)9781509059874

This paper focuses on the application of quadratic optimization for the approximation of uncertain nonlinear robotic function. This function will be used to perform the task of motion control to feedback control of robotic systems. To achieve this task, we are trying, through the study and simulation four approximation approaches: Power Series Polynomial Approximation (PSPA), Orthogonal Neural Network Approximation (ONNA), Chebyshev Polynomials and Series Approximation (CP& SA) and Least Squares Chebyshev polynomial approximation (LSCPA). In each case mentioned above, we use the orthogonal polynomial approximation in higher dimension spaces, which enable us to modify classical differential equation solvers to perform high precision, nonlinear term in model robotic. We unify and extend classical results from function approximation theory and consider their utility in robotics. Then, we could use an efficient algorithms for solving any robust control problems of manipulator robot. Simulation results from a two-link robot manipulator show the satisfactory performance of the approach of approximation the nonlinear term in model robotic..

关键词： Orthogonal polynomial approximation nonlinear robotic function Orthogonal neural network

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Maximizing Hardware Prefetch Effectiveness with Machine Learning 17

Maximizing Hardware Prefetch Effectiveness with Machine Lear...

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2015 IEEE 17th International conference on high performance Computing and Communications (HPCC)

作者： Rahman, Saami Burtscher, Martin Zong, Ziliang Qasem, Apan Texas State Univ Dept Comp Sci San Marcos TX 78666 USA

ISBN: (纸本)9781479989379

Modern processors are equipped with multiple hardware prefetchers, each of which targets a distinct level in the memory hierarchy and employs a separate prefetching algorithm. However, different programs require different subsets of these prefetchers to maximize their performance. Turning on all available prefetchers rarely yields the best performance and, in some cases, prefetching even hurts performance. This paper studies the effect of hardware prefetching on multithreaded code and presents a machine-learning technique to predict the optimal combination of prefetchers for a given application. This technique is based on program characterization and utilizes hardware performance events in conjunction with a pruning algorithm to obtain a concise and expressive feature set. The resulting feature set is used in three different learning models. All necessary steps are implemented in a framework that reaches, on average, 96% of the best possible prefetcher speedup. The framework is built from open-source tools, making it easy to extend and port to other architectures.

关键词： learning (artificial intelligence) multi-threading performance evaluation public domain software storage management feature set hardware performance events hardware prefetch effectiveness maximization learning models machine learning memory hierarchy multiple hardware prefetchers multithreaded code open-source tools optimal prefetcher prediction performance maximization prefetcher speedup program characterization pruning algorithm algorithms Hardware optimization Prefetching Testing Training prefetching memory hierarchy Computer hardware Multithreading open source software Store management Machine Learning performance Evaluation Artificial Intelligence Functional training

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Edge Computing Task Scheduling Based on Improved African Vulture optimization Algorithm 2

Edge Computing Task Scheduling Based on Improved African Vul...

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2nd International conference on Cloud Computing, Big Data Application and software Engineering, CBASE 2023

作者： Shi, Yuhu Huang, Li Gu, Jinguang Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798350331448

With the growth of the Internet of Things (IoT), how edge computing is currently appropriate to handle computationally intensive tasks to ensure the quality of service requested by users is one of the key issues faced today. For the task requires CPU, memory, bandwidth and disk multiple types of resources on the computing node load factor and cost. In this paper, we propose an edge computing scheduling strategy based on Chaotic Selective nonlinear Weighted African Vulture optimization Algorithm (CSNW-AVOA). The algorithm firstly uses Tent chaotic mapping to initialize the population, secondly proposes selective nonlinear weights based on the algorithm properties, and finally uses Lévy flights in the development phase. The results of simulation experiments show that the performance of CSNW-AVOA is significantly improved compared to four mainstream scheduling algorithms, namely, AVOA, OAWOA, IGWO, and PSO, and its load balancing rate and cost consumption are both improved and reduced compared to AVOA. The algorithm effectively utilizes the resources of edge computing nodes and improves the resource utilization. © 2023 IEEE.

关键词： Edge computing

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Multi-objective optimization of double-folded Yagi antenna using genetic algorithms

Multi-objective optimization of double-folded Yagi antenna u...

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International conference on Computational Intelligence and software Engineering

作者： Yuan, Bin Wang, Xiu Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781424453924

The novel compact folded Yagi antenna, for its high gain and superiority in small size, is being interested in various electromagnetic application areas. But for its complex structure, as well as the strong coupling among the elements, making the optimization and design rather difficult. In this paper, the impact of multi-scale semi-solutions to the final performance of antenna is taken into account, a genetic algorithm applied to the multi-objective optimization of folded Yagi antenna is realized. To illustrate the feasibility and effectiveness of the algorithm, a three-element double-folded Yagi antenna is designed and fabricated, the measurements show good results in gain(7.40dBi) and perfect match in impedance(49.87-j0.49Ω) , which fully verify the reliability of the algorithm. ©2010 IEEE.

关键词： Genetic algorithms

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Using Static Analysis Data for performance Modeling and Prediction

Using Static Analysis Data for Performance Modeling and Pred...

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International conference on high performance Computing & Simulation (HPCS)

作者： Noudohouenou, Jose Jalby, William Univ Versailles Saint Quentin En Yvelines Exascale Comp Res Versailles France

ISBN: (纸本)9781479953134

In general, high performance computing applications have large codebases composed of various scientific algorithms which must be tuned to achieve optimal speed. Therefore, a programmer extracts pieces of code from large programs, as candidates for the performance tuning. Maximizing such code performance requires measurement, analysis, and optimization strategies, targeting hardware components. Furthermore, computer architecture improvement raises hardware co-design issues such as measuring detailed computer performance. Currently, code execution time is well measured, but it is much harder to break out the performance contributory details per hardware resource in order to predict a code performance. This paper presents the Ubenchface tool, a framework for performance prediction and knowledge discovery. Inversely to traditional measurement methods and modeling, the proposed tool considers static metrics to analyze and tune application performance. This framework is more informative than simple benchmarking, or microbenchmarking. It is useful for performance investigations in similarity and redundancy study concerning benchmark suites, predicting, understanding scaling, and tuning.

关键词： Modeling Simulation and Evaluation Techniques of HPC Systems Benchmarking and Assessment software Monitoring and Measurement Data Mining and Searching

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