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检索条件"主题词=optimization algorithm"

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A Mixed Integer Efficient Global optimization algorithm with Multiple Infill Strategy - Applied to a Wing Topology optimization Problem

A Mixed Integer Efficient Global Optimization Algorithm with...

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AIAA Science and Technology (SciTech) Forum and Exposition

作者： Roy, Satadru Crossley, William A. Stanford, Bret K. Moore, Kenneth T. Gray, Justin S. Purdue Univ Aeronaut & Astronaut 701 W Stadium Ave W Lafayette IN 47907 USA NASA Langley Res Ctr Aeroelastic Branch 218 Dodd Blvd Hampton VA 23681 USA DB Consulting Grp Inc Cleveland OH 44135 USA NASA Glenn Res Ctr Cleveland OH 44135 USA

ISBN: (数字)9781624105784

ISBN: (纸本)9781624105784

With the advancement in high performance computing and numerical optimization techniques, engineering design optimization problems are becoming more complex, larger scale, higher fidelity, and computationally more demanding, requiring longer run times than ever before. There exists methodologies and techniques that can address some of these challenges but very few can address all, and most are limited in the extent that these concerns can be addressed. With the goal of addressing such challenging engineering problems, we developed a new optimization algorithm, named AMIEGO, that combines concepts from surrogate-based optimization approaches, gradient-based numerical methods, Partial Least Squares, evolutionary algorithms, and Branch-and-Bound, providing newer capabilities that were not previously perceived. The effort here builds upon this previously developed optimization algorithm to include multiple infill sampling capability that combines the concept of generalized expected improvement function, unsupervised learning, and multi-objective evolutionary technique. To demonstrate, AMIEGO with the multiple infill capability (called AMIEGO-MIMOS) solves a series of increasingly difficult engineering design optimization problems. The results reveal the performance of the new approach is problem dependent. When applied to a ten-bar truss problem, the newly proposed multiple infill strategy consistently leads to a better design solutions when compared to the existing CPTV method (implemented with the context of the AMIEGO algorithm). On the other hand, when applied to a mixed-integer high fidelity wing topology optimization problem - MIMOS, despite showing a steeper convergence at the start, eventually leads to an inferior solution as compared to CPTV approach. These results also reveal that a small number of starting points, in general, are sufficient to lead to a good overall solution.

关键词： optimization algorithm Numerical optimization Trusses Multi Objective Evolutionary algorithms High Performance Computing Surrogate Model Wingbox Kernel Functions Parallel Computing Aspect Ratio

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Solving The Printed Circuit Board Drilling Problem By Ant Colony optimization algorithm

Solving The Printed Circuit Board Drilling Problem By Ant Co...

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World Congress on Engineering and Computer Science

作者： Eldos, Taisir Kanan, Aws Aljumah, Abdullah Salman Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Engn Al Kharj 11942 Saudi Arabia

ISBN: (纸本)9789881925237

Printed Circuit Board (PCB) manufacturing depends on the holes drilling time, which is a function of the number of holes and the order in which they are drilled. A typical PCB may have hundreds of holes and optimizing the time to complete the drilling plays a role in the production rate. At an early stage of the manufacturing process, a numerically controlled drill has to move its bit over the holes one by one and must complete the job in minimal time. The order by which the holes are visited is of great significance in this case. Solving the TSP leads to minimizing the time to drill the holes on a PCB. Finding an optimal solution to the TSP maybe prohibitively large as the number of possibilities to evaluate in an exact search is (n-1)!/2 for n-hole PCB. There exist too many algorithms to solve the TSP in an engineering sense;semi-optimal solution, with good quality and cost tradeoff. Starting with Greedy algorithm which delivers a fast solution at the risk of being low in quality, to the evolutionary algorithms like Genetic algorithms, Simulated Annealing algorithms, Ant Colony, Swarm Particle optimization, and others which promise better solutions at the price of more search time. We propose an Ant Colony optimization (ACO) algorithm with problem-specific heuristics like making use of the dispersed locales, to guide the search for the next move. Hence, making smarter balance between the exploration and exploitation leading to better quality for the same cost or less cost for the same quality. This will also offer a better way of problem partitioning which leads to better parallelization when more processing power is to be used to deliver the solution even faster.

关键词： Ant Colony optimization algorithm Printed Circuits Board Drilling Traveling Salesman

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Study on Modeling and Scheduling optimization algorithm of Mining-excavation Planning 9

Study on Modeling and Scheduling Optimization Algorithm of M...

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9th International Symposium on Computational Intelligence and Design (ISCID)

作者： Ming, Jian Hu, Nailian Sun, Jinhai Univ Sci & Technol Beijing Minist Educ State Key Lab High Efficient Min & Safety Met Min Beijing Peoples R China

ISBN: (纸本)9781509035588

The mining-excavation plan plays an important part in the operation management and business intelligence system construction of the mining enterprise and has great impact on decision-making of the mining enterprise. This paper takes the decision support system underground metal mine as the research object and studies its model and algorithm. Based on goals and constraints of ore blending operations, the ore blending model is established by using the theory and technology of multiobjective programming and the ore blending plan is optimized, which provides the theoretical basis and data support for the mining-excavation planning. Based on the experience and knowledge of domain experts and constraints of mining, the stope superseding and verification model is established by using the experts system and its inference mode, therefore the stoping sequence of ore blocks is worked out. In view of the cost characteristics of underground mining the mining-excavation planning model is established by using the time Petri net extended with price information, thus the minimum production cost and corresponding mining-excavation plan are worked out by using the above model.

关键词： mining-excavation planning mining process modeling and scheduling optimization algorithm multiobjective optimization

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SIMULATION OF REGIONAL WINTER WHEAT YIELD BY COMBINING EPIC MODEL AND REMOTELY SENSED LAI BASED ON GLOBAL optimization algorithm

SIMULATION OF REGIONAL WINTER WHEAT YIELD BY COMBINING EPIC ...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Ren, Jianqiang Chen, Zhongxin Tang, Huajun Yu, Fushui Huang, Qing Minist Agr Key Lab Resources Remote Sensing & Digital Agr Beijing 10081 Peoples R China

ISBN: (纸本)9781457710056

In recent years, combining spatial and timely remote sensing data and crop growth model is an important way to improve accuracy of crop growth simulation and crop growth monitoring. In this paper, global optimization algorithm SCE-UA (Shuffled Complex Evolution method - University of Arizona) was used to integrate remotely sensed leaf area index (LAI) with EPIC crop growth model to simulate regional winter wheat yield and other field management information such as sowing date, plant density and net nitrogen fertilizer application rate in Huanghuaihai Plain in China. Final results showed that average relative error of estimated winter wheat yield was 1.81% and RMSE was 0.208 t/ha. Compared with the actual observation data, average relative error of simulated plant density and net nitrogen fertilization application rate was -7.95% and -8.88% respectively and absolute error of simulated sowing date was only 1 day. These above accuracy of simulated results could meet requirements of crop monitoring at regional scale. It was proved that integrating remotely sensed LAI with EPIC model based on SCE-UA for simulation of crop growth condition and crop yield was feasible.

关键词： Data assimilation crop growth model yield estimation remote sensing LAI optimization algorithm

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Using Double Well Function as a Benchmark Function for optimization algorithm

Using Double Well Function as a Benchmark Function for Optim...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Wang, Peng Yang, Guosong Southwest Minzu Univ Sch Comp Sci & Engn Chengdu Peoples R China Chinese Acad Sci Chengdu Inst Comp Applicat Chengdu Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781728183923

The double well function (DWF) is an important theoretical model originating from quantum physics and has been used to describe the energy constraint problem in quantum mechanics and structural chemistry. Although its form may vary, the DWF has two different local minima in the one-dimensional case, and the number of local minima increases as its dimension grows. As a multi-stable function, the DWF is assumed to be a potential candidate for testing the performance of the heuristic optimization algorithm, which aims to seek the global minimum. To verify this idea, a typical DWF is employed in this paper, and a mathematical analysis is presented herein, and its properties as a benchmark function are discussed in different cases. In addition, we conducted a set of experiments utilizing a few optimization algorithms, such as the multi-scale quantum harmonic oscillator algorithm and covariance matrix adaptation evolution strategy;thus the analysis has been illustrated by the results of the numerical experiment. Moreover, to guide the design of an ideal DWF used as a benchmark function in experiment, different values of the decisive parameters were tested corresponding to our analysis, and some useful rules were given based on the discussion of the results.

关键词： double well double well function benchmark function optimization algorithm performance

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Adaptive Bayesian optimization algorithm for Unpredictable Business Environments. 24

Adaptive Bayesian Optimization Algorithm for Unpredictable B...

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8th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (ISMSI)

作者： Maitra, Sarit Alliance Univ Alliance Business Sch Bengaluru India

ISBN: (纸本)9798400717291

This paper introduces an adaptive Bayesian optimization (BayesOpt) framework with dynamic conditioning and jitter mechanisms. The new framework enhances the adaptability and effectiveness of optimization in unpredictable business environments. The dynamic scaling in this framework dynamically modifies the mean objective function in each iteration, and adaptive conditioning functions. The adaptive acquisition jitter function enhances adaptability by adjusting the jitter of the acquisition function. The framework is tested using single-objective, multi-objective, and decoupled multiobjective functions. Statistical analyses which include t-statistics, p-values, and effect size measures (Cohen's d and Hedges g) reveal the superiority of the proposed framework over the original Bayes optimization. The primary contribution is developing a novel and effective optimization approach in stochastic environments, especially in the context of supply chain inventory management.

关键词： Adaptive conditioning Dynamic scaling Effect size optimization algorithm Probabilistic modeling Stochastic environments

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Hybrid Artificial Glowworm Swarm optimization algorithm for Solving Multi-dimensional Knapsack Problem

Hybrid Artificial Glowworm Swarm Optimization Algorithm for ...

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International Conference on Advanced in Control Engineering and Information Science (CEIS)

作者： Gong, Qiaoqiao Zhou, Yongquan Luo, Qifang Guangxi Univ Nationalities Coll Math & Comp Sci Nanning 530006 Guangxi Peoples R China

In this paper, a hybrid artificially glowworm swarm optimization algorithm to solve multidimensional 0-1 knapsack problem is proposed. The algorithm utilizes two important strategies, how to select the item based on its unit volume value and the binary glowworm swarm optimization algorithm, 37 multidimensional 0-1 knapsack test instances are tested by the produced algorithm, The integrated performance of the produced algorithm is rather satisfied, and the hybrid glowworm swarm optimization algorithm is rather efficient for solving multidimensional 0-1 knapsack problem. (c) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011]

关键词： glowworm swarm optimization algorithm knapsack problem greedy algorithms binary code

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Lifetime optimization algorithm with Multiple Mobile Sink Nodes for Wireless Sensor Networks 8th

Lifetime Optimization Algorithm with Multiple Mobile Sink No...

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8th China Wireless Sensor Network Conference (CWSN)

作者： Chen, Yourong Chen, Di Liu, Yaolin Wang, Zhangquan Ren, Tiaojuan Zhejiang Shuren Univ Coll Informat Sci & Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9783662469811;9783662469804

In order to overcome the energy hole problem and long data gathering latency problem in some wireless sensor networks (WSNs), lifetime optimization algorithm with multiple mobile sink nodes for wireless sensor networks (LOA_MMSN) is proposed. LOA_MMSN analyzes the constraints, establishes network optimization model, and decomposes the model into movement path selection model and lifetime optimization model with known movement paths. Finally, the two models are solved. Simulation results show that LOA_MMSN can extend the network lifetime, balance node energy consumption and reduce data gathering latency. Under certain conditions, it outper forms Ratio_w, TPGF and lifetime optimization algorithm with single mobile sink node for WSNs.

关键词： Network lifetime Mobile sink nodes optimization algorithm Sub-gradient method

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Research on the optimization algorithm of Big Data Computing System 17

Research on the Optimization Algorithm of Big Data Computing...

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17th IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC)

作者： Wu, Mengxuan Jiang, Jingjing Wang, Lijuan Sch China Agr Univ Beijing Peoples R China Dalian Univ Sci & Technol Sch Digital Technol Dalian Peoples R China

ISBN: (纸本)9781728186160

With the social progress and development, the scale of data continues to expand, in order to realize the processing and analysis of large-scale data, graph computing system came into being. At present, with the continuous maturity of graph computing system, graph computing has been widely used in various fields, such as social field, Internet of things field and neural network field. In recent years, different graph computing models have emerged, and some typical distributed graph computing models show good expansibility in the formulation of graph data for big data processing. However, in order to further expand the expansibility, many graph calculation models are studied by algorithms. At present, the SFA algorithm is mostly used in the graph calculation system. However, with the continuous development of graph calculation, many inadaptability of the SFA algorithm appear which restricts the further development of graph calculation. Therefore, it is an urgent problem to optimize the algorithm of graph computing system. On the basis of scholars' research, this paper firstly gives a simple overview of graph calculation and graph calculation model. On this basis, it analyzes the specific formula and significance of SFA algorithm, puts forward the specific scheme of algorithm optimization, and carries out experimental detection of optimization algorithm.

关键词： big data graph calculation system optimization algorithm SFA algorithm

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Efficient Microwave Wireless Power Transmission using optimization algorithm 16

Efficient Microwave Wireless Power Transmission using Optimi...

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16th European Conference on Antennas and Propagation (EuCAP)

作者： Kim, Ho Yeol Nam, Sangwook Seoul Natl Univ Sch Elect & Informat Engn Seoul South Korea

ISBN: (纸本)9788831299046

In this study, we developed an efficient microwave wireless power transmission (MPT) system for multiple receivers using an optimization (OPT) technique. The optimization algorithm finds the optimal transmission signal for transferring the desired power to multiple receivers with maximum power transfer efficiency (PTE). We designed a 5 x 5 rectangular patch array antenna and patch element antenna operating at 10 GHz as the transmitter and receiver, respectively. The operating process of the MPT system using the OPT technique is analyzed. Additionally, we compared the received power of each receiver and the PTE of the OPT technique with that of the multi-receiver time-reversal (MR-TR) technique considering various scenarios. The OPT algorithm generates a multibeam to charge multiple receiver simultaneously. We validated that the OPT technique can deliver power to receivers precisely at desired ratios with greater PTE than that of the MR-TR technique in an MPT system.

关键词： microwave wireless power transmission array antenna optimization algorithm

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