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Distributed parallel algorithms for online virtual network embedding applications

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2023年第1期36卷 e4325-e4325页

作者： Lu, Qiao Nguyen, Khoa Huang, Changcheng Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

Network virtualization (NV) has ubiquitously emerged as an indispensable attribute to enable the success of the forthcoming virtualized networks (eg, 5G network and smart Internet of Things [IoT]). Virtual network embedding (VNE) is the major challenge in NV that allows multiple heterogeneous virtual networks (VNs) to simultaneously coexist on a shared substrate infrastructure. A great number of VNE algorithms have been proposed, but over the past decades, most of them are only targeting for VNE node mapping. In this paper, we propose two distributed parallel genetic algorithms, which are based on two versions of crossover and mutation schemes, for online VN link embedding problems with low latency and high efficiency. Furthermore, we conduct a time analysis on the executing time of independently distributed parallel computing machines in details. This comprehensive analysis validates the parallel computing scalability on an identical number of predefined parallel machines. Extensive simulations have shown that our proposed algorithms can achieve better performance than integer linear programming (ILP)-based solutions while meeting the stringent time requirements for online VN embedding applications. Our proposed algorithms yield superior performance in running time with 32.78% up to 1727.8% faster than existing popular VNE algorithms. Additionally, the theoretical analysis indicates that the execution time can be reduced to logarithmic times by applying proposed distributed parallel algorithms.

关键词： 5G network distributed parallel algorithm metaheuristic network virtualization parallel genetic algorithm

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A Data-Driven Bilevel Model for Estimating Operational Information of a Neighboring Rival's Reservoir in a Competitive Context

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IEEE ACCESS 2021年 9卷 159640-159651页

作者： Li, Yapeng Wang, Xiangzhen Cheng, Chuntian Liu, Benxi Li, Gang Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116024 Peoples R China Dalian Univ Technol Inst Hydropower & Hydroinformat Dalian 116024 Peoples R China

Uncertainties from neighboring rival's reservoirs challenge hydropower companies in participating in competitive markets. Cooperative behaviors are generally impractical due to stakeholders' self-interest and regulatory requirements. Considering this obstacle, this paper proposes a data-driven bilevel model, in a competitive context, to estimate the operational information of the neighboring rival's reservoir, including its historical operating states and operational functions. The proposed bilevel model is an inverse problem of the conventional hydropower scheduling model. The upper-level model is designed to find the most appropriate operational parameters of the estimated reservoir that fit its historical generation volumes. The lower-level model simulates the profit-maxing operation of the estimated reservoir. Since the lower simulating model is nonconvex, an Enhanced parallel genetic algorithm (EPGA) is proposed. It avoids infeasible situations through several strategies and uses multiple CPU threads simultaneously in solving. A case study in China's market demonstrates that the proposed model and solving method can efficiently obtain accurate state series and (near-)optimal operational parameters. More experiments are also taken to validate the parallel design.

关键词： Reservoirs Hydroelectric power generation Data models Stakeholders Solid modeling Predictive models Inverse problems Bilevel problem data-driven model hydropower operation inverse optimization parallel genetic algorithm

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GeNESiS: gene network evolution simulation software

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BMC BIOINFORMATICS 2008年第1期9卷 541-541页

作者： Kratz, Anton Tomita, Masaru Krishnan, Arun Keio Univ Inst Adv Biosci Tsuruoka Yamagata 9970035 Japan

Background: There has been a lot of interest in recent years focusing on the modeling and simulation of Gene Regulatory Networks (GRNs). However, the evolutionary mechanisms that give rise to GRNs in the first place are still largely unknown. In an earlier work, we developed a framework to analyze the effect of objective functions, input types and starting populations on the evolution of GRNs with a specific emphasis on the robustness of evolved GRNs. Results: In this work, we present a parallel software package, GeNESiS for the modeling and simulation of the evolution of gene regulatory networks (GRNs). The software models the process of gene regulation through a combination of finite-state and stochastic models. The evolution of GRNs is then simulated by means of a genetic algorithm with the network connections represented as binary strings. The software allows users to simulate the evolution under varying selective pressures and starting conditions. We believe that the software provides a way for researchers to understand the evolutionary behavior of populations of GRNs. Conclusion: We believe that GeNESiS will serve as a useful tool for scientists interested in understanding the evolution of gene regulatory networks under a range of different conditions and selective pressures. Such modeling efforts can lead to a greater understanding of the network characteristics of GRNs.

关键词： genetic algorithm Gene Regulatory Network Pane Message Passing Interface parallel genetic algorithm

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Paralelizace genetických algoritmů

Paralelizace genetických algoritmů

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作者： Haupt, Daniel Brno University of Technology

Tato práce se zabývá možností paralelizace genetického Algoritmu a jeho ná-sledné evaluace pomocí testovacích účelových funkcí. První část je teore... 详细信息

Tato práce se zabývá možností paralelizace genetického Algoritmu a jeho ná-sledné evaluace pomocí testovacích účelových funkcí. První část je teoretická a shrnuje základní poznatky z oblasti genetických Algoritmů, paralelních archi-tektur, paralelních výpočtů a optimalizace. A dále je tato část doplněna o mož-nosti paralelizace genetického Algoritmu. V následující praktické části je rozebrán algoritmus paralelního genetického Algoritmu, jenž je použitý při experimentu a také je diskutována struktura a účel zvoleného experimentu. Následně jsou diskutovány výsledky získané z běhu experimentu na Eridani Clusteru z pohledu zrychlení výpočtu, kvality nalezeného řešení a závislosti kvality řešení na migračním schématu.

关键词： paralelní genetický algoritmus cluster migrační schéma optimalizace testovací funkce parallel genetic algorithm cluster migration scheme optimization test functions Text

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Collaborative optimization method and operation performances for a novel integrated energy system containing adiabatic compressed air energy storage and organic Rankine cycle

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JOURNAL OF ENERGY STORAGE 2021年 41卷

作者： Han, Zhonghe Ma, Fanfan Wu, Di Zhang, Han Dong, Fuxiang Li, Peng Xiao, Liehui North China Elect Power Univ Dept Power Engn Sch Energy Power & Mech Engn Baoding 071003 Peoples R China Dongguan Univ Technol Guangdong Prov Key Lab Distributed Energy Syst Dongguan 523808 Peoples R China

Integrated energy system (IES) coupled with advanced adiabatic compressed air energy storage (AA-CAES) and organic Rankine cycle (ORC) has the superiority to peak-load regulation and improve system performance. Therefore, a novel IES-ORC-CAES system, which includes AA-CAES, ORC and ICE, is proposed in this paper. In this system, the user return water is used to recover the compression heat generated by AA-CAES. The ICE exhaust smoke flows through three parts successively, the heat exchangers of AA-CAES energy release side, the ORC and absorption heat pump arranged in parallel and the tail heat exchanger, realizing the cascade utilization of energy and meeting the diversified energy demand of users. Considering the time of use electricity price and aiming at economy, environmental protection and energy efficiency, a collaborative optimization method that can dynamically adjust the energy output of the system is proposed. The results indicate that compared with the IES-ORC system (without AA-CAES), the annual operation cost, CO2 emission and primary energy consumption of the IES ORC-CAES system can be reduced by 14.84%, 13.06% and 11.69% under the economic, environmental and energy efficiency objectives. Furthermore, by coordinating the regulation of ICE, AA-CAES and ORC, the flexibility of the system energy supply is realized, and the matching of the electric-heat ratio on the source and load side is improved. Finally, the thermodynamic analysis of the IES-ORC-CAES system is carried out through parameter analysis. The research results can provide the reference for the operation optimization of the complex IES.

关键词： integrated energy system advanced adiabatic compressed air energy storage organic Rankine cycle collaborative optimization method parallel genetic algorithm

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Glubam roof truss with riveted glubam connections adopting thin-walled steel tube: Experiment, modeling, and model-updating

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JOURNAL OF BUILDING ENGINEERING 2024年 96卷

作者： Shi, Da Xu, Yongjia Demartino, Cristoforo Lu, Guoshan Li, Zhi Zhejiang Univ Coll Civil Engn & Architecture 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China Politecn Torino DISEG Dipartimento Ingn Strutturale Edile & Geotec Corso Duca Degli Abruzzi 24 I-10128 Turin Italy Zhejiang Univ Univ Illinois Urbana Champaign Inst Haining 314400 Zhejiang Peoples R China Univ Illinois Dept Civil & Environm Engn Urbana IL 61801 USA Roma Tre Univ Dept Architecture I-00153 Rome Italy Nanjing Tech Univ Coll Civil Engn Nanjing 211800 Peoples R China

Bio-based laminated structures offer a timely solution to meet the pressing demand for resource-efficient and environmentally responsible construction practices in civil engineering. Riveted connections are a kind of commonly used joint and significantly impact the mechanical behaviors of global structure. However, existing studies on the complex behavior of such joints for bamboo-based structures are insufficient. This study investigated the hysteretic behavior of riveted Glued Laminated Bamboo (glubam) connections using thin-walled steel tube and corresponding planar roof truss with thin-walled steel tube connections by experimental and numerical methods. Firstly, cyclic tests were conducted on the riveted glubam connections with thin-walled steel tube to investigate their hysteretic behavior. The test matrix encompasses four distinct connection configurations, carefully selected to assess the connection behavior of glubam. After the investigation of connection behavior, cyclic tests were further conducted on glubam planar roof truss structures to evaluate their global and local hysteretic behavior. Subsequently, the numerical hysteretic models for joints and hybrid roof trusses were developed within the OpenSeesPy framework. To accurately and efficiently calibrate the model parameters, a novel parallel genetic algorithm (PGA) was proposed to carry out an initial calibration on model parameters. Based on PGA, a more comprehensive model updating framework combining the neural network, prior knowledge and PGA was developed to obtain optimal parameters. Finally, the numerical models were successfully validated against the experimental data, demonstrating the effectiveness of the established numerical model and model-updating framework.

关键词： Glubam riveted connections Thin-walled steel tube Model updating parallel genetic algorithm Neural network and prior knowledge

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Fast parallel computation of reduced row echelon form to find the minimum distance of linear codes

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期224卷

作者： Cuellar, Manuel P. Lobillo, F. J. Navarro, Gabriel Univ Granada Dept Comp Sci & Artificial Intelligence Calle Periodista Daniel Saucedo Aranda s-n E-18071 Granada Spain Univ Granada Dept Algebra Calle Fuente Nueva S-N E-18071 Granada Spain Univ Granada CITIC Calle Periodista Rafael Gomez Montero 2 E-18071 Granada Spain

Finding the distance of linear codes is a key aspect to build error correcting codes, and also to design attacks in code-based post-quantum cryptography;however, it is a NP-hard problem difficult to be addressed. Metaheuristics, and more specifically genetic algorithms, have proven to be a promising tool to improve the search of an upper bound for the distance of a given linear code. In a previous work, it was demonstrated that the there exists a column permutation of a code matrix whose Reduced Row Echelon Form (RREF) contains a row of minimum weight, i.e. the code distance, although calculating RREF during fitness evaluation increases the time complexity of the algorithm substantially. In this work, we propose parallelization of multiple calculations of Reduced Row Echelon Forms simultaneously, and its integration into a fully parallelized design of a CHC evolutionary algorithm to overcome this limitation. Moreover, we demonstrate empirically a substantial improvement in time complexity for the approach in practical case studies to find the distance of linear codes over different finite fields.

关键词： Linear code Minimum distance genetic algorithm parallel genetic algorithm GPU-based parallel model Post-quantum cryptography

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Solving the Independent-set Problem in a DNA-Based Supercomputer Model

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parallel PROCESSING LETTERS 2005年第4期15卷 469-479页

作者： Chang, Weng-Long Guo, Minyi Wu, Jesse Southern Taiwan Univ Technol Dept Informat Management Tainan 701 Taiwan Univ Aizu Dept Comp Software Aizu Wakamatsu Fukushima 9658580 Japan Natl Kaohsiung Normal Univ Dept Math Kaohsiung 802 Taiwan

In this paper, it is demonstrated how the DNA (DeoxyriboNucleic Acid) operations presented by Adleman and Lipton can be used to develop the parallel genetic algorithm that solves the independent-set problem. The advantage of the genetic algorithm is the huge parallelism inherent in DNA based computing. Furthermore, this work represents obvious evidence for the ability of DNA based parallel computing to solve NP-complete problems.

关键词： DNA based computing parallelism parallel genetic algorithm NP-complete problems

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A SIMPLE parallelIZING SCHEME OF genetic algorithm ON DISTRIBUTED-MEMORY MULTIPROCESSORS

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International Journal of High Speed Computing 1994年第3期6卷 451-474页

作者： JONGHO NANG Department of Computer Science Sogang University 1 Shinsoo-Dong Mapo-Ku Seoul 121–742 Korea

This paper proposes a simple and efficient parallelizing scheme of the genetic algorithm on distributed-memory multiprocessor that maintains the execution behaviours of sequential genetic algorithm. In this parallelizing scheme, the global population is evenly partitioned into several subpopulations, each of which is assigned to the processor to be evolved in parallel. An interprocessor communication pattern, called AAB (All-To-All Broadcasting), is used at each generation in order to exchange the informations on all individuals evolved in all other processors. It allows the processor to reproduce the individuals in a global sense, in other words, the sequential execution behaviours can be maintained in the parallelized genetic algorithm. This paper shows that the genetic algorithms employing widely used selection schemes such as proportionate selection, ranking selection and tournament selection can be efficiently parallelized on distributed-memory multiprocessor using the proposed parallelizing scheme. Some experimental speedups on AP1000 are also presented to show the usefulness of the proposed parallelizing scheme.

关键词： genetic algorithm parallel processing parallel genetic algorithm preserving sequential behaviours multicomputers AP1000

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Acceleration of the Multilayer Network Operator Method Using MPI for Mobile Robot Team Control Synthesis

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Procedia Computer Science 2017年 103卷 88-93页

作者： A.I. Diveev E.Yu. Shmalko D.N. Zakharov Federal Research Center “Computer Science and Control” of Russian Academy of Sciences 44 Vavilova street Moscow 119333 Russia Peoples’ Friendship University of Russia 6 Miklukho-Maklaya str. Moscow 117198 Russia

The paper is devoted to the problem of machine-made synthesis of control for robotic teams. The goal of synthesis is to find a multidimensional control function that depends on the current states of all robots. The synthesised control function provides any time the optimal control values to allow each robot achieving the objectives with the best value of functional quality. The approach is based on multilayer network operator method that belongs to a symbolic regression class. Formations of multi-robot systems require individual robots to satisfy their kinematic equations while constantly maintaining inter-robot dynamic constraints. Verification of these dynamic constraints on each iteration of the evolutionary algorithm greatly increases the computational costs of the numerical synthesis. In the paper we propose to accelerate existing designs through taking advantage of newest programming tools of MPI framework for automatic parallelization. Experiments show that our approach reduces greatly computational time.

关键词： group of robots symbolic regression network operator parallel genetic algorithm MPI

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