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Nonlinear Constrained optimization Method for Dynamic Conversion Corridor of Tilt-Rotor Aircraft

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2024年第12期47卷 2661-2666页

作者： Song, Wei Sun, Liguo Wang, Qi Beihang Univ Sch Aeronaut Sci & Engn Beijing 100191 Peoples R China Nanchang Hangkong Univ Sch Aeronaut Sci & Engn Nanchang 330063 Peoples R China Nanchang Hangkong Univ Sch Aircraft Engn Nanchang 330063 Peoples R China

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SR-PSO: server residual efficiency-aware particle swarm optimization for dynamic virtual machine scheduling

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JOURNAL OF SUPERCOMPUTING 2023年第14期79卷 15459-15495页

作者： Ajmera, Kashav Tewari, Tribhuwan Kumar Jaypee Inst Informat Technol Dept Comp Sci & Engn & Informat Technol Sect 62 Noida India

A dynamic virtual machine scheduling is the discrete optimization problem that schedules virtual machines over the set of physical servers at each discrete scheduling interval. As this problem is NP-complete, heuristic and greedy approaches may get stuck at the local minima and produce the suboptimal solution. Therefore, we proposed server residual efficiency-aware particle swarm optimization (SR-PSO) algorithm for dynamic virtual machine scheduling in this work. The classical PSO operators are tuned to suit dynamic virtual machine scheduling. The proposed bi-objective fitness function guides the proposed algorithm during the exploration of global solution space and schedules virtual machines over the physical servers operating at optimum energy efficiency or near it with minimum virtual machine migrations. A virtual machine selection algorithm is proposed that selects the virtual machines whose migration results in servers' optimum energy efficiency. The server underload detection algorithm is proposed that categorizes servers as underloaded if they operate with energy inefficiency. The SR-PSO algorithm is aware of discrete scheduling intervals, and at each scheduling interval, only those VMs are rescheduled that are prone to service level agreement SLA violation or lower server utilization. We have used a cloudsim simulator to simulate our proposed work, and the results show significant improvement in energy consumption for the dynamic VM scheduling. More specifically, our proposed approach is 45.4% and 50% more energy efficient than the previous dynamic virtual machine scheduling approaches.

关键词： Dynamic VM scheduling Energy-efficient computing Cloud computing Cloudsim Particle swarm optimization (PSO) Metaheuristic algorithm optimization algorithm

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NM-MF: Non-Myopic Multifidelity Framework for Constrained Multi-Regime Aerodynamic optimization

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AIAA JOURNAL 2023年第3期61卷 1270-1280页

作者： Di Fiore, Francesco Mainini, Laura Politecn Torino Dept Mech & Aerosp Engn I-10129 Turin Italy

The exploration and trade-off analysis of different aerodynamic design configurations requires solving optimization problems. The major bottleneck to assess the optimal design is the large number of time-consuming evaluations of high-fidelity computational fluid dynamics (CFD) models, necessary to capture the non-linear phenomena and discontinuities that occur at higher Mach number regimes. To address this limitation, we introduce an original non-myopic multifidelity Bayesian framework aimed at including expensive high-fidelity CFD simulations for the optimization of the aerodynamic design. Our scheme proposes a novel two-step lookahead policy to maximize the improvement of the solution quality considering the rewards of future steps, and combines it with utility functions informed by the fluid dynamic regime and the information extracted from data, to wisely select the aerodynamic model to interrogate. We validate the proposed framework for the case of a constrained drag coefficient optimization problem of a NACA 0012 airfoil, and compare the results to other popular multifidelity and single-fidelity optimization frameworks. The results suggest that our strategy outperforms the other approaches, allowing to significantly reduce the drag coefficient through a principled selection of limited evaluations of the high-fidelity CFD model.

关键词： Aerodynamic Coefficients Computational Fluid Dynamics Bayesian optimization Compressible Fluid optimization algorithm Multidisciplinary Design optimization Aerodynamic Design Transonic Aerodynamics Derivative Free optimization

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Maximizing efficiency and performance of water distribution systems through the implementation of optimization algorithms: A comprehensive analysis of valve and chlorine booster placement and management

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Heliyon 2025年第3期11卷 e42306页

作者： Najarzadegan, Mohamadreza Eskandaripour, Mehrtash Department of Civil Engineering Faculty of Civil Engineering and Transportation University of Isfahan (UI) Isfahan Iran Department of Civil Engineering Isfahan University of Technology (IUT) Isfahan Iran

Efficient management of water distribution systems (WDSs) is critical to ensuring sustainable access to safe and reliable water. This study addresses the dual challenge of optimizing hydraulic performance and water quality in WDSs by integrating pressure management through flow control valves (FCVs) and chlorine booster optimization. By employing advanced optimization algorithms, specifically the Genetic algorithm (GA) and Slime Mould algorithm (SMA), the research identifies optimal configurations across two benchmark networks, Jowitt and Xu and GoYang. The results demonstrate a 15 % reduction in average pressure fluctuations and a 20 % improvement in chlorine residual uniformity, validating the effectiveness of the integrated optimization approach. Additionally, the SMA algorithm outperformed GA in terms of computational efficiency and solution quality, achieving a 12 % lower error margin and a 33 % reduction in computational time. The study also explores critical trade-offs between the number of FCVs and chlorine boosters, highlighting their impact on cost and system reliability. This work is novel in its integration of hydraulic and water quality objectives into a unified framework, which is further validated through comprehensive case studies. The findings provide practical insights for water utilities to enhance system efficiency, reduce costs, and ensure water safety while advancing the frontier of WDS optimization by addressing previously overlooked interactions between hydraulic and water quality objectives. © 2025 The Authors

关键词： Chlorine booster management Efficiency and performance optimization algorithm Valve placement and setting Water distribution system

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Topology optimization Benchmark Problems for Assessing the Performance of optimization algorithms

Topology Optimization Benchmark Problems for Assessing the P...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Kennedy, Graeme J. Fu, Yicong Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

This paper presents a set of benchmark topology optimization problems that are used to evaluate the performance of optimization software. The topology optimization formulations considered in this benchmark set include mass-constrained compliance minimization, stress-constrained mass minimization, mass and stress-constrained compliance minimization, and mass and frequency-constrained compliance minimization. Both structured and unstructured quadrilateral and hexahedral meshes are used with conventional and non-conventional topology optimization design domains. In total, the benchmark set contains 108 2D and 72 3D domain and mesh combinations. In this preliminary work, the performance of the optimizers SNOPT, IPOPT and ParOpt are evaluated. Performance profiles are used to assess the performance of an optimizer across the full benchmark set. We find that SNOPT performs the best overall among all optimizers. Furthermore, a modification of the quasi-Newton Hessian update provides a considerable performance benefit in both objective function value and discreteness measure.

关键词： Topology optimization optimization algorithm Aspect Ratio FORTRAN Cantilever Beam Computing Finite Element Analysis Structural Displacement Mechanism Design Stress Distribution

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Effectiveness of Surrogate-Based optimization algorithms for System Architecture optimization

Effectiveness of Surrogate-Based Optimization Algorithms for...

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AIAA Aviation Forum and Exposition

作者： Bussemaker, Jasper H. Barton, Nathalie Lefebvre, Thierry Ciampa, Pier D. Nagel, Bjoern DLR German Aerosp Ctr Inst Syst Architectures Aeronaut Aircraft Design & Syst Integrat Hamburg Germany Univ Toulouse DTIS ONERA Informat Proc & Syst Dept Toulouse France DLR German Aerosp Ctr Inst Syst Architectures Aeronaut Hamburg Germany

ISBN: (数字)9781624106101

ISBN: (纸本)9781624106101

The design of complex system architectures brings with it a number of challenging issues, among others large combinatorial design spaces. optimization can be applied to explore the design space, however gradient-based optimization algorithms cannot be applied due to the mixed-discrete nature of the design variables. It is investigated how effective surrogate-based optimization algorithms are for solving the black-box, hierarchical, mixed-discrete, multiobjective system architecture optimization problems. Performance is compared to the NSGAII multi-objective evolutionary algorithm. An analytical benchmark problem that exhibits most important characteristics of architecture optimization is defined. First, an investigation into algorithm effectiveness is performed by measuring how accurately a known Pareto-front can be approximated for a fixed number of function evaluations. Then, algorithm efficiency is investigated by applying various multi-objective convergence criteria to the algorithms and establishing the possible trade-off between result quality and function evaluations needed. Finally, the impact of hidden constraints on algorithm performance is investigated. The code used for this paper has been published.

关键词： optimization algorithm Multi Objective Evolutionary algorithms Black Box algorithms Convergence Surrogate Model Gaussian Process Design of Experiments Computational Fluid Dynamics Systems Engineering Genetic algorithm

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Reliability optimization of a Mars Oxygen Production Plant

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JOURNAL OF SPACECRAFT AND ROCKETS 2023年第3期60卷 982-990页

作者： Hinterman, Eric D. Khopkar, Piyush P. MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA MathWorks Inc Dept Aeronaut & Astronaut Natick MA 01760 USA

The Mars Oxygen ISRU Experiment (MOXIE) is an instrument onboard NASA's Perseverance rover. On April 20th, 2021, MOXIE generated oxygen on Mars from the carbon dioxide present in the Martian atmosphere, demonstrating, for the first time, in-situ resource utilization (ISRU) on the surface of another celestial body. Learnings from MOXIE on Mars have aided in the design of a scaled-up version of MOXIE. Oxygen generated from this scaled-up system would be used as propellant in a Mars Ascent Vehicle that would enable the crew to return to Earth once their mission was complete, as well as in life support systems. Failure of any of its subsystems would result in a loss of mission due to the inability of the crew to return to Earth. Accordingly, risk analysis is one of the most crucial steps in the design of the scaled-up MOXIE that must be completed and understood before building and launching the system to Mars. The intent of this paper is to present a comprehensive, quantitative analysis of the operational risks associated with this Mars ISRU plant. We then present an approach to optimize the reliability of each subsystem using a modified probabilistic risk assessment and heuristics-based optimization algorithm.

关键词： Space Exploration and Technology Mars Ascent Vehicle Propellant optimization algorithm Life Support System Discrete Probability Distributions Failure Modes and Effects Analysis Mars Missions In Situ Resource Utilization MOXIE

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Identification of gene knockout strategies using a hybrid of an ant colony optimization algorithm and flux balance analysis to optimize microbial strains

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2014年第PartB期53卷 175-183页

作者： Lu, Shi Jing Salleh, Abdul Hakim Mohamed Mohamad, Mohd Saberi Denis, Safaai Omatu, Sigeru Yoshioka, Michifumi Univ Teknol Malaysia Fac Comp Artificial Intelligence & Bioinformat Res Grp Skudai 81310 Johor Malaysia Osaka Inst Technol Dept Elect Informat & Commun Engn Osaka 5358585 Japan Osaka Prefecture Univ Fac Engn Naka Ku Sakai Osaka 5998531 Japan

Reconstructions of genome-scale metabolic networks from different organisms have become popular in recent years. Metabolic engineering can simulate the reconstruction process to obtain desirable phenotypes. In previous studies, optimization algorithms have been implemented to identify the near-optimal sets of knockout genes for improving metabolite production. However, previous works contained premature convergence and the stop criteria were not clear for each case. Therefore, this study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux balance analysis (ACOFBA) to predict near optimal sets of gene knockouts in an effort to maximize growth rates and the production of certain metabolites. Here, we present a case study that uses Baker's yeast, also known as Saccharomyces cerevisiae, as the model organism and target the rate of vanillin production for optimization. The results of this study are the growth rate of the model organism after gene deletion and a list of knockout genes. The ACOFBA algorithm was found to improve the yield of vanillin in terms of growth rate and production compared with the previous algorithms. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Ant colony optimization algorithm Flux balance analysis Metabolic engineering optimization algorithm Gene knockout strategy Microbial strains

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On a novel multi-swarm fruit fly optimization algorithm and its application

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APPLIED MATHEMATICS AND COMPUTATION 2014年 233卷 260-271页

作者： Yuan, Xiaofang Dai, Xiangshan Zhao, Jingyi He, Qian Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China

Swarm intelligence is a research field that models the collective behavior in swarms of insects or animals. Recently, a kind of Drosophila (fruit fly) inspired optimization algorithm, called fruit fly optimization algorithm (FOA), has been developed. This paper presents a variation on original FOA technique, named multi-swarm fruit fly optimization algorithm (MFOA), employing multi-swarm behavior to significantly improve the performance. In the MFOA approach, several sub-swarms moving independently in the search space with the aim of simultaneously exploring global optimal at the same time, and local behavior between sub-swarms are also considered. In addition, several other improvements for original FOA technique is also considered, such as: shrunk exploring radius using osphresis, and a new distance function. Application of the proposed MFOA approach on several benchmark functions and parameter identification of synchronous generator shows an effective improvement in its performance over original FOA technique. (C) 2014 Elsevier Inc. All rights reserved.

关键词： optimization algorithm Fruit fly optimization algorithm (FOA) Multi-swarm Swarm behavior Cooperative swarms

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A modeling method for photovoltaic cells using explicit equations and optimization algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 59卷 23-28页

作者： Xu, Yonghai Kong, Xiangyu Zeng, Yawen Tao, Shun Xiao, Xiangning North China Power Elect Univ Dept Elect & Elect Engn State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China

A numerical method for determining the five-parameter model of photovoltaic cells is presented in the paper. Explicit equations are applied to analyze the relations between parameters which are solved by an optimization algorithm. Lambert W function is implemented to convert the I-V characteristic implicit equation to an explicit one, so the output current and voltage of photovoltaic cells can be obtained by substituting the five parameters into the explicit I-V equation. Several cells are used to verify the accuracy of the proposed method from different aspects. It is found that the proposed method gives precise results and can be applicable to various types of photovoltaic cells. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Photovoltaic cells Five-parameter model Explicit equation optimization algorithm Lambert W function

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