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4th International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2022

作者： Chen, Bowei College of Safety and Ocean Engineering Beijing102249 China

ISBN: (纸本)9781665463997

In order to further optimize the dominant truss structure inside the aerofoil of a certain aircraft type, an optimization model for the structure is proposed. Firstly, a traditional optimization model is established based on the nonlinear programming method, which regards the deflection of the aerofoil's end nodes as the objective function, and the size of the components, the constraint reaction force of the hinge supports, and the stress of the components as the constraints. Secondly, taking the existence of the complex internal geometrical angles and a large quantity of oblique components into consideration, an improved optimization model whose constraint conditions contain the length of a specific oblique component is proposed, and the results are verified by the finite element analysis results of ABAQUS software. The results show that when the length of the dominant truss structure inside the aerofoil is 12m, the height is 3m, the length of a specific component inside the wing is 2.8m, and the cross-sectional area of the component is 11200mm2, the deflection of the aerofoil's end node is the lowest, and the safety of the aerofoil's design scheme satisfies the requirement. The establishment of this model has an important guiding significance for the optimal design of other aerofoil's dominant truss structures. © 2022 IEEE.

关键词： nonlinear programming

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On optimal regression trees to detect critical intervals for multivariate functional data

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COMPUTERS & OPERATIONS RESEARCH 2023年 152卷

作者： Blanquero, Rafael Carrizosa, Emilio Molero-Rio, Cristina Romero Morales, Dolores Univ Seville Inst Matemat Seville Spain Copenhagen Business Sch Frederiskberg Denmark

In this paper, we tailor optimal randomized regression trees to handle multivariate functional data. A compromise between prediction accuracy and sparsity is sought. Whilst fitting the tree model, the detection of a reduced number of intervals that are critical for prediction, as well as the control of their length, is performed. Local and global sparsities can be modeled through the inclusion of LASSO-type regularization terms over the coefficients associated to functional predictor variables. The resulting optimization problem is formulated as a nonlinear continuous and smooth model with linear constraints. The numerical experience reported shows that our approach is competitive against benchmark procedures, being also able to trade off prediction accuracy and sparsity.

关键词： Optimal randomized regression trees Multivariate functional data Critical intervals detection nonlinear programming

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Deterministic and Stochastic Exploration of Long Asteroid Fly-by Sequences Exploiting Tree-graph and Optimal Substructure Properties 73

Deterministic and Stochastic Exploration of Long Asteroid Fl...

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73rd International Astronautical Congress, IAC 2022

作者： Sánchez, J.P. Bellome, A. Carrillo, M. Del Ser, J. Institut Supérieur de l'Aéronautique et de l'Espace Toulouse France Centre of Autonomous and Cyber-Physical Systems Cranfield University United Kingdom Airbus Defense & Space Stevenage United Kingdom Department of Communications Engineering University of the Basque Country Bilbao Spain

In the past, space trajectory design was limited to the optimal design of transfers to single destinations. However, a somewhat more daring approach is today making the space community to consider missions that visit, with one single spacecraft, a multitude of celestial objects;such as asteroid tour mission proposals CASTAway or MANTIS, which both proposed to visit 10 or more asteroids in a quick succession of asteroid fly-bys. The design of these so-called asteroid tours is complicated by the fact that the sequence of asteroids is not known a priori, but is the objective of the optimisation itself. This leads to a complex mixed-integer non-linear programming (MINLP) problem, on which the decision variables assume both continuous and discrete values. Beyond the obvious complexity of such problem formulation, preliminary mission design requires not only to locate the global optimum solution but, also, to map the ensemble of solutions that leads to feasible transfers. This paper analyses the complexity of such search space, which can be efficiently modelled as a tree-graph of interconnected Lambert arc solutions between two consecutive asteroids. This allows to exploit the optimal substructure of the problem and enables complete tree traverse explorations for limited asteroid catalogues. Nevertheless, the search space quickly grows in complexity for larger catalogues, featuring a labyrinthine multi-modal structure and extreme non-linearities. This underlying complexity ultimately renders common stochastic heuristics, such as Ant Colony Optimization, rather inefficient. Mostly, due to the fact that the metaheuristic processes are not able to gather any real understanding, or knowledge, such that it can efficiently guide the search. Instead, an astrodynamics-lead heuristic based on the distance between spacecraft and asteroid at the asteroid's MOID-point crossing epoch, enables an efficient pruning of the asteroid catalogue. Then, deterministic processes based on dynami

关键词： nonlinear programming

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On high-order multilevel optimization strategies

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SIAM Journal on Optimization 2021年第1期31卷 307-330页

作者： Calandra, Henri Gratton, Serge Riccietti, Elisa Vasseur, Xavier TOTAL Centre Scientifique et Technique Jean F\'eger PauF-64000 France INPT-IRIT University of Toulouse and ENSEEIHT Toulouse Cedex 7F-31071 France ISAE-SUPAERO University of Toulouse Toulouse Cedex 4F-31055 France

We propose a new family of multilevel methods for unconstrained minimization. The resulting strategies are multilevel extensions of high-order optimization methods based on qth-order Taylor models (with q \geq 1) that have been recently proposed in the literature. The use of high-order models, while decreasing the worst-case complexity bound, makes these methods computationally more expensive. Hence, to counteract this effect, we propose a multilevel strategy that exploits a hierarchy of problems of decreasing dimension, still approximating the original one, to reduce the global cost of the step computation. A theoretical analysis of the family of methods is proposed. Specifically, local and global convergence results are proved, and a worst-case complexity bound to reach first-order stationary points is also derived. A multilevel version of the well-known adaptive regularization by cubics (corresponding to q = 2 in our setting) has been implemented, as well as a multilevel third-order method (q = 3). Numerical experiments clearly highlight the relevance of the new multilevel approaches leading to considerable computational savings compared to their one-level counterparts. © 2021 Society for Industrial and Applied Mathematics

关键词： nonlinear programming

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Joint Power Control and Resource Allocation for Optimizing the D2D User Performance of Full-Duplex D2D Underlying Cellular Networks

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SENSORS 2023年第23期23卷 9549页

作者： Zhou, Yuetian Cai, Bowen Ding, Xue China Telecom Res Inst Dept Mobile Commun & Terminal Technol Beijing 100032 Peoples R China Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu 610056 Peoples R China

D2D communication is a promising technology for enhancing spectral efficiency (SE) in cellular networks, and full-duplex (FD) has the potential to double SE. Due to D2D's short-distance communication and low transmittance power, it is natural to integrate FD into D2D, creating FD-D2D to underlay a cellular network to further improve SE. However, the residual self-interference (RSI) resulting from FD-D2D and interference arising from spectrum sharing between D2D users (DUs) and cellular users (CUs) can restrict D2D link performance. Therefore, we propose an FD-D2D underlying cellular system in which DUs jointly share uplink and downlink spectral resources with CUs. Moreover, we present two algorithms to enhance the performance experience of DUs while improving the system's SE. For the first algorithm, we tackle an optimization problem aimed at maximizing the sum rate of FD-DUs in the system while adhering to transmittance power constraints. This problem is formulated as a mixed-integer nonlinear programming problem (MINLP), known for its mathematical complexity and NP-hard nature. In order to address this MINLP, our first algorithm decomposes it into two subproblems: power control and spectral resource allocation. The power control aspect is treated as a nonlinear problem, which we solve through one-dimensional searching. Meanwhile, spectral resource allocation is achieved using the Kuhn-Munkres algorithm, determining the pairing of CUs and DUs sharing the same spectrum. As for the second algorithm, our objective is to enhance the individual performance of FD-DUs by maximizing the minimum rate among them, ensuring more uniform rate performance across all FD-DUs. In order to solve this optimization problem, we propose a novel spectral resource allocation algorithm based on bisection searching and the Kuhn-Munkres algorithm, whereas the power control aspect remains the same as in the first algorithm. The numerical results demonstrate that our proposed algorithm effecti

关键词： full-duplex underlaid D2D Kuhn-Munkres algorithm uniform algorithm nonlinear programming

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nonlinear operational optimization of an industrial power-to-heat system with a high temperature heat pump, a thermal energy storage and wind energy

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APPLIED ENERGY 2023年 344卷

作者： Walden, Jasper V. M. Baehr, Martin Glade, Anselm Gollasch, Jens Tran, A. Phong Lorenz, Tom German Aerosp Ctr Inst Low Carbon Ind Proc Simulat & Virtual Design Walther Pauer Str 5 D-03046 Cottbus Brandenburg Germany

The heat demand for industrial processes is often provided in the form of steam generated by various fossil fueled equipment. In order to reduce CO2 emissions, the heat demand has to be covered by renewable energy sources. Electrified steam generation relies on complex energy systems, that can be operated according to energy availability and cost developments. However, such a multi component industrial energy system poses a challenge in modeling and determining the cost-or emission-optimal operation of the system. This study develops a methodology to model a multi component industrial energy system on the basis of a case study. By optimal system operation, either costs or emissions are minimized in response to fluctuating renewable wind energy and electricity prices.A high temperature heat pump (HTHP), a sensible thermal energy storage (TES) and a wind turbine are combined to create an electrified energy system to supply super-heated steam. During periods of low wind speed, additional grid electricity is purchased to ensure a steady heat supply. The HTHP offers a high operational flexibility and thus, enables the charging and discharging of the TES. A model of the closed reverse Brayton cycle HTHP, which is able to simulate part load behavior, is created in a process simulation software and consolidated in nonlinear surrogate models. The component behavior of a TES is represented by a combination of equations based on heat exchanger relations. Finally, the resulting algebraic nonconvex, nonlinear optimization problem based on the proposed system is solved using the local interior point optimizer (IPOPT) solver equipped with a multi-start approach to determine an optimal operation over a reference week with respect to the current wind power generation, grid emissions and electricity *** results of the optimization show, that optimal operating strategies enable a high potential to decarbonize future industries at minimum operational costs or emissions.

关键词： Operational optimization Renewable steam generation Electrification in industry Offline optimization nonlinear programming

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A compromise solution method for the multiobjective minimum risk problem

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OPERATIONAL RESEARCH 2021年第3期21卷 1913-1926页

作者： Bellahcene, Fatima Marthon, Philippe Mouloud Mammeri Univ Fac Sci LAROMAD Lab BP 17 RP Tizi Ouzou 15000 Algeria ENSEEIHT Informat 2 Rue CamichelBP 7122 F-31071 Toulouse 7 France

We develop an approach which enables the decision maker to search for a compromise solution to a multiobjective stochastic linear programming (MOSLP) problem where the objective functions depend on parameters which are continuous random variables with normal multivariate distributions. The minimum-risk criterion is used to transform the MOSLP problem into its corresponding deterministic equivalent which in turn is reduced to a Chebyshev problem. An algorithm based on the combined use of the bisection method and the probabilities of achieving goals is developed to obtain the optimal or epsilon optimal solution of this specific problem. An illustrated example is included in this paper to clarify the developed theory.

关键词： Multiobjective programming Stochastic programming nonlinear programming Minimum-risk criterion

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Growth threshold-effect on renewable energy consumption in major oil-producing countries in sub-Saharan Africa: a dynamic panel threshold regression estimation

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INTERNATIONAL JOURNAL OF ENERGY SECTOR MANAGEMENT 2021年第3期15卷 496-522页

作者： Abdulqadir, Idris Abdullahi Univ Sains Malaysia Sch Social Sci Econ Program George Town Malaysia

Purpose This study aims to explore the relationship between the growth threshold effect on renewable energy consumption (REC) in the major oil-producing countries in sub-Saharan Africa (SSA) over the period 1990-2018. Design/methodology/approach This article used a dynamic panel threshold regression model introduced by Hansen (1996, 1999 and 2000) threshold (TR) models. The procedure is achieved using 5,000 bootstrapping replications and the grid search to obtain the asymptotic distribution and p-values. For the long-run relationship among our variables, the author followed the process in Pesaran et al. (1999) pooled mean group (PMG) for heterogeneous panels. Furthermore, for the robustness of our empirical results due to the sensitivity of the results to outliers, the author used the approach by Cook (1979) distance measure. The author applied quantile (QR) regression to explore the distribution of dependent variables following Bassett and Koenker (1982) and Koenker and Bassett (1978) approaches. Findings The results from the threshold effect test and threshold regression revealed a significant single threshold effect of growth level on REC. Furthermore, the result from the PMG estimation showed the growth of the variable, energy intensity, consumer prices and CO2 emissions play a significant role in REC in major oil-producing countries in SSA. The growth threshold estimation results indicated one significant threshold value of 1.013% at one period lagged of real growth. The outlier's sensitivity detention greatly influenced our empirical results. Originality/value The article filled the literature gap by applying a combined measure that is robustness to detect outliers in the data, which none of the studies in the literature addresses hitherto. Further, the article extends the quantile regression to growth - REC literature.

关键词： Co-integration Economic growth Correlation analysis nonlinear programming Quantile regression Sub-Saharan Africa Simulation Threshold regression Dynamic heterogeneous panels Renewable energy consumption

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Staging Optimization of Satellite Launch Vehicles: Application to a Microsatellite Launcher

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JOURNAL OF AEROSPACE TECHNOLOGY AND MANAGEMENT 2024年 16卷

作者： da Silveira, Guilherme Fernandes, Sandro da Silva Inst Aeronaut & Espaco Div Aerodinam Controle & Estruturas Dept Ciencia & Tecnol Aerosp Sao Jose Dos Campos SP Brazil Inst Tecnol Aeronaut Dept Ciencia & Tecnol Aerosp Dept Matemat Sao Jose Dos Campos SP Brazil

With modern technology, only a small percentage of the overall mass of a launch vehicle, usually less than 5%, can be used as payload mass. To achieve any acceptable performance, a key aspect that is exhaustively analyzed during vehicle design is the definition of the number and sizes of its stages. The problem of finding the best vehicle configuration for a given mission is called staging optimization. This work presents the formulation and a resolution method for the staging optimization of a launch vehicle. The problem is formulated as an optimal control problem (OCP) and, for its solution, a direct approach, employing a gradient-based algorithm as solver, is proposed. An application is performed for the Brazilian microsatellite launcher VLM-1. This vehicle has a notable characteristic that, in its nominal configuration, the first and second stages are very similar to each other (same solid rocket motor with the same quantity of propellant). Different vehicle configurations are investigated which result in an improvement in its performance. The obtained results show good agreement with those from a commercial optimization tool.

关键词： Staging Launch vehicles Optimal control nonlinear programming

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Multi-Server Assisted Task Offloading and Resource Allocation for Latency Minimization in Thermal-Aware MEC Networks

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IEEE Transactions on Consumer Electronics 2024年

作者： Zhang, Rongqian Yin, Li Hao, Yuyu Gao, Hui Zhao, Mingxiong Yunnan University School of Information Science and Engineering Kunming650091 China Engineering Research Center of Cyberspace China Yunnan University School of Software Kunming650091 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China

As the 6G network advances,the integration of sophisticated data mining techniques within the Consumer Internet of Things (CIoT) intensifies challenges in CPU temperature management and power efficiency. The growing use of consumer electronics and applications is also expected to drastically increase data traffic. Consequently,the substantial computational demands of these technologies risk causing CPU overheating,particularly given the limited capacities of Mobile Edge Computing (MEC) servers and IoT devices. Addressing these issues requires innovative resource allocation strategies that effectively manage CPU temperatures while maintaining robust system performance. This paper introduces the Thermal-aware Offloading and Resource Allocation Strategy (TORAS),specifically designed to minimize total execution latency for users across multi-server MEC networks by tackling a complex mixed-integer nonlinear programming problem. We streamline this problem into manageable subproblems: offloading ratio selection and resource allocation optimization,which are alternately optimized. Our approach includes deriving closed-form solutions for offloading ratio selection and transmission power allocation,and employing the primal-dual method to determine the optimal resource allocation strategies. This methodology not only addresses the thermal and operational challenges in MEC environments but also enhances latency performance,achieving improvements of 5.2% and 9.7% compared to other schemes,respectively. © 1975-2011 IEEE.

关键词： nonlinear programming

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