Journal articles on the topic 'Network design problem; Bayesian optimization; Simulation-based optimization'
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1
Yin, Ruyang, Jiping Xing, Pengli Mo, Nan Zheng, and Zhiyuan Liu. "BO-B&B: A hybrid algorithm based on Bayesian optimization and branch-and-bound for discrete network design problems." Electronic Research Archive 30, no. 11 (2022): 3993–4014. http://dx.doi.org/10.3934/era.2022203.
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<abstract> <p>A discrete network design problem (DNDP) is conventionally formulated as an analytical bi-level programming problem to acquire an optimal network design strategy for an existing traffic network. In recent years, multimodal network design problems have benefited from simulation-based models. The nonconvexity and implicity of bi-level DNDPs make it challenging to obtain an optimal solution, especially for simulation-related models. Bayesian optimization (BO) has been proven to be an effective method for optimizing the costly black-box functions of simulation-based continuous network design problems. However, there are only discrete inputs in DNDPs, which cannot be processed using standard BO algorithms. To address this issue, we develop a hybrid method (BO-B&B) that combines Bayesian optimization and a branch-and-bound algorithm to deal with discrete variables. The proposed algorithm exploits the advantages of the cutting-edge machine-learning parameter-tuning technique and the exact mathematical optimization method, thereby balancing efficiency and accuracy. Our experimental results show that the proposed method outperforms benchmarking discrete optimization heuristics for simulation-based DNDPs in terms of total computational time. Thus, BO-B&B can potentially aid decision makers in mapping practical network design schemes for large-scale networks.</p> </abstract>
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Han, Lu, Xianjun Shi, and Yuyao Zhai. "Test optimization selection method based on NSGA-3 and improved Bayesian network model." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 39, no. 2 (April 2021): 414–22. http://dx.doi.org/10.1051/jnwpu/20213920414.
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Most of the solutions to existing test selection problems are based on single-objective optimization algorithms and multi-signal models, which maybe lead to some problems such as rough index calculation and large solution set limitations. To solve these problems, a test optimization selection method based on NSGA-3 algorithm and Bayesian network model is proposed. Firstly, the paper describes the improved Bayesian network model, expounds the method of model establishment, and introduces the model's learning ability and processing ability on uncertain information. According to the constraints and objective functions established by the design requirements, NSGA-3 is used to calculate the test optimization selection scheme based on the improved Bayesian network model. Taking a certain component of the missile airborne radar as an example, the fault detection rate and isolation rate are selected as constraints, and the false alarm rate, misdiagnosis rate, test cost, and test quantity are the optimization goals. The method of this paper is used for test optimization selection. It has been verified that this method can effectively solve the problem of multi-objective test selection, and has guiding significance for testability design.
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Zhang, Xinyong, and Liwei Sun. "Optimization of Optical Machine Structure by Backpropagation Neural Network Based on Particle Swarm Optimization and Bayesian Regularization Algorithms." Materials 14, no. 11 (June 1, 2021): 2998. http://dx.doi.org/10.3390/ma14112998.
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Fit of the highly nonlinear functional relationship between input variables and output response is important and challenging for the optical machine structure optimization design process. The backpropagation neural network method based on particle swarm optimization and Bayesian regularization algorithms (called BMPB) is proposed to solve this problem. A prediction model of the mass and first-order modal frequency of the supporting structure is developed using the supporting structure as an example. The first-order modal frequency is used as the constraint condition to optimize the lightweight design of the supporting structure’s mass. Results show that the prediction model has more than 99% accuracy in predicting the mass and the first-order modal frequency of the supporting structure, and converges quickly in the supporting structure’s mass-optimization process. The supporting structure results demonstrate the advantages of the method proposed in the article in terms of high accuracy and efficiency. The study in this paper provides an effective method for the optimized design of optical machine structures.
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Dong, Qiang, Ruiying Li, and Rui Kang. "System Resilience Evaluation and Optimization Considering Epistemic Uncertainty." Symmetry 14, no. 6 (June 8, 2022): 1182. http://dx.doi.org/10.3390/sym14061182.
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Epistemic uncertainties, caused by data asymmetry and deficiencies, exist in resilience evaluation. Especially in the system design process, it is difficult to obtain enough data for system resilience evaluation and improvement. Mathematics methods, such as evidence theory and Bayesian theory, have been used in the resilience evaluation for systems with epistemic uncertainty. However, these methods are based on subjective information and may lead to an interval expansion problem in the calculation. Therefore, the problem of how to quantify epistemic uncertainty in the resilience evaluation is not well solved. In this paper, we propose a new resilience measure based on uncertainty theory, a new branch of mathematics that is viewed as appropriate for modeling epistemic uncertainty. In our method, resilience is defined as an uncertainty measure that is the belief degree of a system’s behavior after disruptions that can achieve the predetermined goal. Then, a resilience evaluation method is provided based on the operation law in uncertainty theory. To design a resilient system, an uncertain programming model is given, and a genetic algorithm is applied to find an optimal design to develop a resilient system with the minimal cost. Finally, road networks are used as a case study. The results show that our method can effectively reduce cost and ensure network resilience.
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Song, Wenxue. "Building Construction Design Based on Particle Swarm Optimization Algorithm." Journal of Control Science and Engineering 2022 (June 29, 2022): 1–8. http://dx.doi.org/10.1155/2022/7139230.
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In order to take a scientific risk control strategy to reduce the safety risk of construction projects, a construction safety risk decision-making method based on particle swarm optimization algorithm was proposed. Through the analysis of prefabricated building construction safety risk factors, the combination of the Markov Chain and Bayesian networks method was used to estimate the probability of risk factors. The relationship between the various risk factors was described by conditional probability, and a safety risk loss-control investment double objective optimization model was built. The corresponding algorithm was designed and the R language programming was used to solve the problem. The experimental results showed that by taking a high degree of control over the risk factors of the investment strategy, when the constraint cost was RMB 200,000, the global optimal risk loss and the global optimal control cost were RMB 1,400,500 and 19,600, respectively. When the constraint cost was 280,000 yuan, the global optimal risk loss and global optimal control cost were 1.046 million yuan and 278.5 million yuan, respectively. When the constraint cost was 320,000 yuan, the global optimal risk loss and global optimal control cost were 910,100 yuan and 317,300, yuan respectively. It was concluded that, considering the risk correlation optimization model, a reasonable allocation strategy was adopted, combined with the actual situation, which performed a promoting function in improving the assembly building construction safety risk decision-making.
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Ozaki, Yoshihiko, Yuki Tanigaki, Shuhei Watanabe, Masahiro Nomura, and Masaki Onishi. "Multiobjective Tree-Structured Parzen Estimator." Journal of Artificial Intelligence Research 73 (April 8, 2022): 1209–50. http://dx.doi.org/10.1613/jair.1.13188.
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Practitioners often encounter challenging real-world problems that involve a simultaneous optimization of multiple objectives in a complex search space. To address these problems, we propose a practical multiobjective Bayesian optimization algorithm. It is an extension of the widely used Tree-structured Parzen Estimator (TPE) algorithm, called Multiobjective Tree-structured Parzen Estimator (MOTPE). We demonstrate that MOTPE approximates the Pareto fronts of a variety of benchmark problems and a convolutional neural network design problem better than existing methods through the numerical results. We also investigate how the configuration of MOTPE affects the behavior and the performance of the method and the effectiveness of asynchronous parallelization of the method based on the empirical results.
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Yaloveha, Vladyslav, Andrii Podorozhniak, and Heorhii Kuchuk. "Convolutional neural network hyperparameter optimization applied to land cover classification." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 1 (February 23, 2022): 115–28. http://dx.doi.org/10.32620/reks.2022.1.09.
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In recent times, machine learning algorithms have shown great performance in solving problems in different fields of study, including the analysis of remote sensing images, computer vision, natural language processing, medical issues, etc. A well-prepared input dataset can have a huge impact on the result metrics. However, a correctly selected hyperparameter combined with neural network architecture could highly increase the final metrics. Therefore, the hyperparameters optimization problem becomes a key issue in a deep learning algorithm. The process of finding a suitable hyperparameter combination could be performed manually or automatically. Manual search is based on previous research and requires enormous human efforts. However, there are many automated hyperparameter optimization methods have been successfully applied in practice. The automated hyperparameter tuning techniques are divided into two groups: black-box optimization techniques (such as Grid Search, Random Search) and multi-fidelity optimization techniques (HyperBand, BOHB). The most recent and promising among all approaches is BOHB which, which combines both Bayesian optimization and bandit-based methods, outperforms classical approaches, and can run asynchronously with given GPU resources and time budget that plays a vital role in the hyperparameter optimization process. The previous study proposed a convolutional deep learning neural network for solving land cover classification problems in the EuroSAT dataset. It was found that adding spectral indexes NDVI, NDWI, and GNDVI with RGB channels increased the result accuracy (from 64.72% to 84.19%) and F1 (from 63.89 % to 84.05%) score. However, the convolutional neural network architecture and hyperparameter combination were selected manually. The research optimizes convolutional neural network architecture and finds suitable hyperparameter combinations applied to land cover classification problems using multispectral images. The obtained results must increase result performance compared with the previous study and given budget constraints.
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Cook, Jared A., Ralph C. Smith, Jason M. Hite, Razvan Stefanescu, and John Mattingly. "Application and Evaluation of Surrogate Models for Radiation Source Search." Algorithms 12, no. 12 (December 12, 2019): 269. http://dx.doi.org/10.3390/a12120269.
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Surrogate models are increasingly required for applications in which first-principles simulation models are prohibitively expensive to employ for uncertainty analysis, design, or control. They can also be used to approximate models whose discontinuous derivatives preclude the use of gradient-based optimization or data assimilation algorithms. We consider the problem of inferring the 2D location and intensity of a radiation source in an urban environment using a ray-tracing model based on Boltzmann transport theory. Whereas the code implementing this model is relatively efficient, extension to 3D Monte Carlo transport simulations precludes subsequent Bayesian inference to infer source locations, which typically requires thousands to millions of simulations. Additionally, the resulting likelihood exhibits discontinuous derivatives due to the presence of buildings. To address these issues, we discuss the construction of surrogate models for optimization, Bayesian inference, and uncertainty propagation. Specifically, we consider surrogate models based on Legendre polynomials, multivariate adaptive regression splines, radial basis functions, Gaussian processes, and neural networks. We detail strategies for computing training points and discuss the merits and deficits of each method.
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Liu, Zhiqiang, Hongzhou Zhang, Shengjin Wang, Weijun Hong, Jianhui Ma, and Yanfeng He. "Reliability Evaluation of Public Security Face Recognition System Based on Continuous Bayesian Network." Mathematical Problems in Engineering 2020 (May 25, 2020): 1–9. http://dx.doi.org/10.1155/2020/6287394.
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For the sake of measuring the reliability of actual face recognition system with continuous variables, after analyzing system structure, common failures, influencing factors of reliability, and maintenance data of a public security face recognition system in use, we propose a reliability evaluation model based on Continuous Bayesian Network. We design a Clique Tree Propagation algorithm to reason and solve the model, which is realized by R programs, and as a result, the reliability coefficient of the actual system is obtained. Subsequently, we verify the Continuous Bayesian Network by comparing its evaluation results with those of traditional Bayesian Network and Ground Truth. According to these evaluation results, we find out some weaknesses of the system and propose some optimization strategies by the way of finding the right remedies and filling in blanks. In this paper, we synthetically apply a variety of methods, such as qualitative analysis, quantitative analysis, theoretical analysis, and empirical analysis, to solve the unascertained causal reasoning problem. The evaluation method is reasonable and valid, the results are consistent with realities and objective, and the proposed strategies are very operable and targeted. This work is of theoretical significance to research on reliability theory. It is also of practical significance to the improvement of the system’s reliability and the ability of public order maintenance.
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Altabey, Wael A., Mohammad Noori, Zhishen Wu, Mohamed A. Al-Moghazy, and Sallam A. Kouritem. "Studying Acoustic Behavior of BFRP Laminated Composite in Dual-Chamber Muffler Application Using Deep Learning Algorithm." Materials 15, no. 22 (November 15, 2022): 8071. http://dx.doi.org/10.3390/ma15228071.
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Over the last two decades, several experimental and numerical studies have been performed in order to investigate the acoustic behavior of different muffler materials. However, there is a problem in which it is necessary to perform large, important, time-consuming calculations particularly if the muffler was made from advanced materials such as composite materials. Therefore, this work focused on developing the concept of the indirect dual-chamber muffler made from a basalt fiber reinforced polymer (BFRP) laminated composite, which is a monitoring system that uses a deep learning algorithm to predict the acoustic behavior of the muffler material in order to save effort and time on muffler design optimization. Two types of deep neural networks (DNNs) architectures are developed in Python. The first DNN is called a recurrent neural network with long short-term memory blocks (RNN-LSTM), where the other is called a convolutional neural network (CNN). First, a dual-chamber laminated composite muffler (DCLCM) model is developed in MATLAB to provide the acoustic behavior datasets of mufflers such as acoustic transmission loss (TL) and the power transmission coefficient (PTC). The model training parameters are optimized by using Bayesian genetic algorithms (BGA) optimization. The acoustic results from the proposed method are compared with available experimental results in literature, thus validating the accuracy and reliability of the proposed technique. The results indicate that the present approach is efficient and significantly reduced the time and effort to select the muffler material and optimal design, where both models CNN and RNN-LSTM achieved accuracy above 90% on the test and validation dataset. This work will reinforce the mufflers’ industrials, and its design may one day be equipped with deep learning based algorithms.
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Coles, Darrell, and Andrew Curtis. "Efficient nonlinear Bayesian survey design using DN optimization." GEOPHYSICS 76, no. 2 (March 2011): Q1—Q8. http://dx.doi.org/10.1190/1.3552645.
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A new method for fully nonlinear, Bayesian survey design renders the optimization of industrial-scale geoscientific surveys as a practical possibility. The method, DN optimization, designs surveys to maximally discriminate between different possible models. It is based on a generalization to nonlinear design problems of the D criterion (which is for linearized design problems). The main practical advantage of DN optimization is that it uses efficient algorithms developed originally for linearized design theory, resulting in lower computing and storage costs than for other nonlinear Bayesian design techniques. In a real example in which we optimized a seafloor microseismic sensor network to monitor a fractured petroleum reservoir, we compared DN optimization with two other networks: one proposed by an industrial contractor and one optimized using a linearized Bayesian design method. Our technique yielded a network with superior expected data quality in terms of reduced uncertainties on hypocenter locations.
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Guo, Yang, and Jinhe Zhou. "Design on routing optimization algorithm based on complex network." MATEC Web of Conferences 232 (2018): 01020. http://dx.doi.org/10.1051/matecconf/201823201020.
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In order to solve the problem of information flow network transmission and congestion control in complex internet networks, A new and complicated routing network algorithm based on network evolution simulation model is proposed, An improved method is proposed for a typical static local routing algorithm in an SRTD network. This method adds a node message queue length as a main parameter. The variable parameters can be used to adjust the node processing capacity and the weight of node packet queue length in the routing policy. And to a certain extent, the efficiency of simulation is improved and the purpose of network optimization is achieved.
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Cong, Yong-Quan, Ting Guan, Ju-Fu Cui, and Xiang-Guo Cheng. "LGBM: An Intrusion Detection Scheme for Resource-Constrained End Devices in Internet of Things." Security and Communication Networks 2022 (October 11, 2022): 1–12. http://dx.doi.org/10.1155/2022/1761655.
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The intrusion detection schemes (IDSs) based on the Gradient Boosting Decision Tree (GBDT) face three problems: unbalanced training data distribution, large dimensionality of data features, and difficulty in model parameter optimization, which lead to weak monitoring capability and high false positive rate. For the problem of unbalanced training data distribution, we make the one-sided gradient oversampling algorithm to ensure the balance between the data of each category. To tackle the problem of the large dimensionality of data features, we develop a hierarchical cross-validation algorithm for binding mutually exclusive features. To address the problem of difficulty in model parameter optimization, we design a Bayesian optimization algorithm to make the model parameter search process more targeted and reduce the model training cost by establishing functional relationships between hyperparameters and target functions. The detailed experimental results show that the scheme can effectively solve the problems of data imbalance, high-dimensional data features, and low parameter finding efficiency, and improve the model’s ability to monitor the attack behavior.
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Sahoo, Laxminarayan, Supriyan Sen, Kalishankar Tiwary, Sovan Samanta, and Tapan Senapati. "Optimization of Data Distributed Network System under Uncertainty." Discrete Dynamics in Nature and Society 2022 (April 8, 2022): 1–12. http://dx.doi.org/10.1155/2022/7806083.
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The major network design or data distributed problems may be described as constrained optimization problems. Constrained optimization problems include restrictions imposed by the system designers. These limitations are basically due to the system design’s physical limitations or functional requirements of the network system. Constrained optimization is a computationally challenging job whenever the constraints/limitations are nonlinear and nonconvex. Furthermore, nonlinear programming methods can easily deal same optimization problem if somehow the constraints are nonlinear and convex. In this paper, we have addressed a distributed network design problem involving uncertainty that transmits data across a parallel router. This distributed network design problem is a Jackson open-type network design problem that has been formulated based on the M/M/1 queueing system. Because our network design problem is a nonlinear, convex optimization problem, we have employed a well-known Kuhn–Tucker (K-T) optimality algorithm to solve the same. Here, we have used triangular fuzzy numbers to express uncertain traffic rates and data processing rates. Then, by applying α -level interval of fuzzy numbers and their corresponding parametric representation of α -level intervals, the associated network design problem has been transformed to its parametric form and later has been solved. To obtain the optimal data stream rate in terms of interval and to illustrate the applicability of the entire approach, a hypothetical numerical example has been exhibited. Finally, the most important results have been reported.
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Di, Ruohai, Ye Li, Kaifang Wan, Zhigang Lyu, and Peng Wang. "Bayesian network parameter learning algorithm based on improved QMAP." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 39, no. 6 (December 2021): 1356–67. http://dx.doi.org/10.1051/jnwpu/20213961356.
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Small data sets make the statistical information in Bayesian network parameter learning inaccurate, which makes it difficult to get accurate Bayesian network parameters based on data. Qualitative maximum a posteriori estimation (QMAP) is the most accurate algorithm for Bayesian network parameter learning under the condition of small data sets. However, when the number of parameter constraints is large or the parameter feasible region is small, the rejection-acceptance sampling process in QMAP algorithm will become extremely time-consuming. In order to improve the learning efficiency of QMAP algorithm and not affect its learning accuracy as much as possible, a new analytical calculation method of the center point of constrained region is designed to replace the original rejection-acceptance sampling calculation method. Firstly, a new objective function is designed, and a constrained objective optimization problem for solving the boundary points of the constrained region is constructed. Secondly, a new optimization engine is used to solve the objective optimization problem, and the boundary points and center points of the constrained region are obtained. Finally, the existing QMAP algorithm is improved by the obtained center points. The simulation results show that the CMAP algorithm proposed in this paper has a slightly worse parameter learning accuracy than the QMAP algorithm, but its computational efficiency is 2-5 times higher than that of the QMAP algorithm.
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Liao, Chuan Zhu, Ming Yan Jiang, and Kun Kun Shang. "Cross Layer Optimization for Lifetime Maximization in Wireless Sensor Network Based on Particle Swarm Optimization." Advanced Materials Research 718-720 (July 2013): 1980–85. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1980.
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Network lifetime is a critical metric in the design of energy-constrained wireless sensor networks. In this paper, we consider the joint cross layer optimization of the physical layer, medium access control layer and routing layer to maximize network lifetime of a multi-sources and single-sink wireless sensor network with energy constraints. We focus on synchronous small-scale sensor network with interference-free link scheduling and practical MPSK link transmission scheme. As the network lifetime maximization problem is a constrained non-convex optimization problem that is difficult to be solved, and the particle swarm optimization algorithm is a good intelligent algorithm, we employ it to solve the problem mentioned above effectively in this paper. The penalty function technique is brought in to work out the constrained optimization problem by converting it to an unconstrained optimization problem. Simulation results show the effectiveness of the proposed algorithm in energy saving and network lifetime maximization, and the particle swarm optimization can solve the network lifetime maximization problem fast and efficiently.
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Wang, Hongchun, Jing Qu, and Wensheng Niu. "Network Topology Optimization Based on Time-Triggered DIMA." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 6 (December 2018): 1224–31. http://dx.doi.org/10.1051/jnwpu/20183661224.
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Distributed Integrated Modular Avionics System (DIMA) has attracted extensive attention in recent years, and the time-triggered Ethernet (TTE) supports the time triggering mechanism of DIMA system, so it has become the main protocol adopted by this system. In order to generate a network topology with lower architecture cost, load balancing and relatively short path, we studied the network topology optimization problem based on TTE. In view of the arbitrariness of TTE network topology, based on the analysis of time-triggered DIMA model, Floyd algorithm and simulated annealing algorithm are applied to realize topology optimization. The simulation results show that the proposed topology design and optimization method can not only effectively reduce network congestion, but also save network cost.
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Sun, Hua, Ziyou Gao, and Fangxia Zhao. "Dynamic Network Design Problem under Demand Uncertainty: An Adjustable Robust Optimization Approach." Discrete Dynamics in Nature and Society 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/739656.
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This paper develops an adjustable robust optimization approach for a network design problem explicitly incorporating traffic dynamics and demand uncertainty. In particular, a cell transmission model based network design problem of linear programming type is considered to describe dynamic traffic flows, and a polyhedral uncertainty set is used to characterize the demand uncertainty. The major contribution of this paper is to formulate such an adjustable robust network design problem as a tractable linear programming model and justify the model which is less conservative by comparing its solution performance with the robust solution from the usual robust model. The numerical results using one network from the literature demonstrate the modeling advantage of the adjustable robust optimization and provided strategic managerial insights for enacting capacity expansion policies under demand uncertainty.
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Cavagnaro, Daniel R., Jay I. Myung, Mark A. Pitt, and Janne V. Kujala. "Adaptive Design Optimization: A Mutual Information-Based Approach to Model Discrimination in Cognitive Science." Neural Computation 22, no. 4 (April 2010): 887–905. http://dx.doi.org/10.1162/neco.2009.02-09-959.
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Discriminating among competing statistical models is a pressing issue for many experimentalists in the field of cognitive science. Resolving this issue begins with designing maximally informative experiments. To this end, the problem to be solved in adaptive design optimization is identifying experimental designs under which one can infer the underlying model in the fewest possible steps. When the models under consideration are nonlinear, as is often the case in cognitive science, this problem can be impossible to solve analytically without simplifying assumptions. However, as we show in this letter, a full solution can be found numerically with the help of a Bayesian computational trick derived from the statistics literature, which recasts the problem as a probability density simulation in which the optimal design is the mode of the density. We use a utility function based on mutual information and give three intuitive interpretations of the utility function in terms of Bayesian posterior estimates. As a proof of concept, we offer a simple example application to an experiment on memory retention.
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Ceylan, Huseyin. "Optimal Design of Signal Controlled Road Networks Using Differential Evolution Optimization Algorithm." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/696374.
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This study proposes a traffic congestion minimization model in which the traffic signal setting optimization is performed through a combined simulation-optimization model. In this model, the TRANSYT traffic simulation software is combined with Differential Evolution (DE) optimization algorithm, which is based on the natural selection paradigm. In this context, the EQuilibrium Network Design (EQND) problem is formulated as a bilevel programming problem in which the upper level is the minimization of the total network performance index. In the lower level, the traffic assignment problem, which represents the route choice behavior of the road users, is solved using the Path Flow Estimator (PFE) as a stochastic user equilibrium assessment. The solution of the bilevel EQND problem is carried out by the proposed Differential Evolution and TRANSYT with PFE, the so-called DETRANSPFE model, on a well-known signal controlled test network. Performance of the proposed model is compared to that of two previous works where the EQND problem has been solved by Genetic-Algorithms- (GAs-) and Harmony-Search- (HS-) based models. Results show that the DETRANSPFE model outperforms the GA- and HS-based models in terms of the network performance index and the computational time required.
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Zhang, Xin-Yu, Yu-Bo Tian, and Xie Zheng. "Antenna Optimization Design Based on Deep Gaussian Process Model." International Journal of Antennas and Propagation 2020 (November 12, 2020): 1–10. http://dx.doi.org/10.1155/2020/2154928.
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When using Gaussian process (GP) machine learning as a surrogate model combined with the global optimization method for rapid optimization design of electromagnetic problems, a large number of covariance calculations are required, resulting in a calculation volume which is cube of the number of samples and low efficiency. In order to solve this problem, this study constructs a deep GP (DGP) model by using the structural form of convolutional neural network (CNN) and combining it with GP. In this network, GP is used to replace the fully connected layer of the CNN, the convolutional layer and the pooling layer of the CNN are used to reduce the dimension of the input parameters and GP is used to predict output, while particle swarm optimization (PSO) is used algorithm to optimize network structure parameters. The modeling method proposed in this paper can compress the dimensions of the problem to reduce the demand of training samples and effectively improve the modeling efficiency while ensuring the modeling accuracy. In our study, we used the proposed modeling method to optimize the design of a multiband microstrip antenna (MSA) for mobile terminals and obtained good optimization results. The optimized antenna can work in the frequency range of 0.69–0.96 GHz and 1.7–2.76 GHz, covering the wireless LTE 700, GSM 850, GSM 900, DCS 1800, PCS1900, UMTS 2100, LTE 2300, and LTE 2500 frequency bands. It is shown that the DGP network model proposed in this paper can replace the electromagnetic simulation software in the optimization process, so as to reduce the time required for optimization while ensuring the design accuracy.
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Jones, Michael C., Thomas A. Mazzuchi, and Shahram Sarkani. "A Simulation-based Optimization Approach to Logistic and Supply Chain Network Design." Optimizing Operations 28, no. 97 (July 1, 2021): 284–318. http://dx.doi.org/10.22594/10.22594/dau.20-860.28.03.
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The Department of Defense (DoD) operates a world-wide supply chain, which in 2017 contained nearly 5 million items collectively valued at over $90 billion. Since at least 1990, designing and operating this supply chain, and adapting it to ever-changing military requirements, are highly complex and tightly coupled problems, which the highest levels of DoD recognize as weaknesses. Military supply chains face a wide range of challenges. Decisions made at the operational and tactical levels of logistics can alter the effectiveness of decisions made at the strategic level. Decisions must be made with incomplete information. As a result, practical solutions must simultaneously incorporate decisions made at all levels as well as take into account the uncertainty faced by the logistician. The design of modern military supply chains, particularly for large networks where many values are not known precisely, is recognized as too complex for many techniques found in the academic literature. Much of the literature in supply chain network design makes simplifying assumptions, such as constant per-unit transportation costs regardless of the size of the shipment, the shipping mode selected, the time available for the delivery, or the route taken. This article avoids these assumptions to provide an approach the practitioner can use when designing and adapting supply chain networks. This research proposes a simulation-based optimization approach to find a near-optimal solution to a large supply chain network design problem of the scale faced by a theater commander, while recognizing the complexity and uncertainty that the practicing military logistician must deal with.
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Afshar, M. H., A. Afshar, M. A. Mariño, and A. A. S. Darbandi. "Hydrograph-based storm sewer design optimization by genetic algorithm." Canadian Journal of Civil Engineering 33, no. 3 (March 1, 2006): 319–25. http://dx.doi.org/10.1139/l05-121.
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A model is developed for the optimal design of storm water networks. The model uses a genetic algorithm (GA) as the search engine and the TRANSPORT module of the US Environmental Protection Agency storm water management model version 4.4H (SWMM4.4H) as the hydraulic simulator. Two different schemes are used to formulate the problem with varying degrees of success in reaching a near-optimal solution. In the first scheme, the nodal elevations and pipe diameters are selected as the decision variables of the problem which were determined by the GA to produce the trial solutions. In the second scheme, only nodal elevations are optimized by the GA, and determination of pipe diameters is left to the TRANSPORT SWMM module. Simulation of the trial solutions in both methods is carried out by the TRANSPORT module of SWMM4.4H. The proposed model is applied to some benchmark examples, and the results are presented and compared with the existing results in the literature.Key words: genetic algorithm, optimal design, sewer network, SWMM.
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Sangroula, Uchit, Kuk-Heon Han, Kang-Min Koo, Kapil Gnawali, and Kyung-Taek Yum. "Optimization of Water Distribution Networks Using Genetic Algorithm Based SOP–WDN Program." Water 14, no. 6 (March 9, 2022): 851. http://dx.doi.org/10.3390/w14060851.
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Water distribution networks are vital hydraulic infrastructures, essential for providing consumers with sufficient water of appropriate quality. The cost of construction, operation, and maintenance of such networks is extremely large. The problem of optimization of a water distribution network is governed by the type of water distribution network and the size of pipelines placed in the distribution network. This problem of optimal diameter allocation of pipes in a distribution network has been heavily researched over the past few decades. This study describes the development of an algorithm, ‘Smart Optimization Program for Water Distribution Networks’ (SOP–WDN), which applies genetic algorithm to the problem of the least-cost design of water distribution networks. SOP–WDN demonstrates the application of an evolutionary optimization technique, i.e., genetic algorithm, linked with a hydraulic simulation solver EPANET, for the optimal design of water distribution networks. The developed algorithm was applied to three benchmark water distribution network optimization problems and produced consistently good results. SOP–WDN can be utilized as a tool for guiding engineers during the design and rehabilitation of water distribution pipelines.
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Wang, Ruisong, Gongliang Liu, Wenjing Kang, Bo Li, Ruofei Ma, and Chunsheng Zhu. "Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks." Sensors 18, no. 8 (August 6, 2018): 2568. http://dx.doi.org/10.3390/s18082568.
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Information acquisition in underwater sensor networks is usually limited by energy and bandwidth. Fortunately, the received signal can be represented sparsely on some basis. Therefore, a compressed sensing method can be used to collect the information by selecting a subset of the total sensor nodes. The conventional compressed sensing scheme is to select some sensor nodes randomly. The network lifetime and the correlation of sensor nodes are not considered. Therefore, it is significant to adjust the sensor node selection scheme according to these factors for the superior performance. In this paper, an optimized sensor node selection scheme is given based on Bayesian estimation theory. The advantage of Bayesian estimation is to give the closed-form expression of posterior density function and error covariance matrix. The proposed optimization problem first aims at minimizing the mean square error (MSE) of Bayesian estimation based on a given error covariance matrix. Then, the non-convex optimization problem is transformed as a convex semidefinite programming problem by relaxing the constraints. Finally, the residual energy of each sensor node is taken into account as a constraint in the optimization problem. Simulation results demonstrate that the proposed scheme has better performance than a conventional compressed sensing scheme.
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Lu, Xing Hua. "Internet of Things System Optimization Design of Logistics Based on Improved LEACH Algorithm." Applied Mechanics and Materials 687-691 (November 2014): 4004–8. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.4004.
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In the logistics system, the Internet of things information transmission problem is researched. The ZigBee, 3G technologies are fused in the radio frequency identification (RFID), a set of logistics networking platform is designed based on 3G-ZigBee, with the functions such as multipoint automatic identification, real-time positioning, wireless network and RFID. The logistics system is designed, the routing algorithm of logistics system is analyzed, the improved LEACH algorithm is proposed, the NS component is used for the simulation analysis. Through the simulation results, the best balance point between energy consumption and network density is obtained, the parameters of the logistics system are optimized. Finally, software design idea and workflow are presented, the purpose of the remote control is achieved.
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Bartoldson, Brian, Rui Wang, Yucheng Fu, David Widemann, Sam Nguyen, Jie Bao, Zhijie Xu, and Brenda Ng. "Latent Space Simulation for Carbon Capture Design Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12447–53. http://dx.doi.org/10.1609/aaai.v36i11.21511.
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The CO2 capture efficiency in solvent-based carbon capture systems (CCSs) critically depends on the gas-solvent interfacial area (IA), making maximization of IA a foundational challenge in CCS design. While the IA associated with a particular CCS design can be estimated via a computational fluid dynamics (CFD) simulation, using CFD to derive the IAs associated with numerous CCS designs is prohibitively costly. Fortunately, previous works such as Deep Fluids (DF) (Kim et al., 2019) show that large simulation speedups are achievable by replacing CFD simulators with neural network (NN) surrogates that faithfully mimic the CFD simulation process. This raises the possibility of a fast, accurate replacement for a CFD simulator and therefore efficient approximation of the IAs required by CCS design optimization. Thus, here, we build on the DF approach to develop surrogates that can successfully be applied to our complex carbon-capture CFD simulations. Our optimized DF-style surrogates produce large speedups (4000x) while obtaining IA relative errors as low as 4% on unseen CCS configurations that lie within the range of training configurations. This hints at the promise of NN surrogates for our CCS design optimization problem. Nonetheless, DF has inherent limitations with respect to CCS design (e.g., limited transferability of trained models to new CCS packings). We conclude with ideas to address these challenges.
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Li, Jia, Yanqiu Liu, Ying Zhang, and Zhongjun Hu. "Robust Optimization of Fourth Party Logistics Network Design under Disruptions." Discrete Dynamics in Nature and Society 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/720628.
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The Fourth Party Logistics (4PL) network faces disruptions of various sorts under the dynamic and complex environment. In order to explore the robustness of the network, the 4PL network design with consideration of random disruptions is studied. The purpose of the research is to construct a 4PL network that can provide satisfactory service to customers at a lower cost when disruptions strike. Based on the definition ofβ-robustness, a robust optimization model of 4PL network design under disruptions is established. Based on the NP-hard characteristic of the problem, the artificial fish swarm algorithm (AFSA) and the genetic algorithm (GA) are developed. The effectiveness of the algorithms is tested and compared by simulation examples. By comparing the optimal solutions of the 4PL network for different robustness level, it is indicated that the robust optimization model can evade the market risks effectively and save the cost in the maximum limit when it is applied to 4PL network design.
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Abdollahzadeh, Asaad, Alan Reynolds, Mike Christie, David Corne, Brian Davies, and Glyn Williams. "Bayesian Optimization Algorithm Applied to Uncertainty Quantification." SPE Journal 17, no. 03 (August 23, 2012): 865–73. http://dx.doi.org/10.2118/143290-pa.
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Summary Prudent decision making in subsurface assets requires reservoir uncertainty quantification. In a typical uncertainty-quantification study, reservoir models must be updated using the observed response from the reservoir by a process known as history matching. This involves solving an inverse problem, finding reservoir models that produce, under simulation, a similar response to that of the real reservoir. However, this requires multiple expensive multiphase-flow simulations. Thus, uncertainty-quantification studies employ optimization techniques to find acceptable models to be used in prediction. Different optimization algorithms and search strategies are presented in the literature, but they are generally unsatisfactory because of slow convergence to the optimal regions of the global search space, and, more importantly, failure in finding multiple acceptable reservoir models. In this context, a new approach is offered by estimation-of-distribution algorithms (EDAs). EDAs are population-based algorithms that use models to estimate the probability distribution of promising solutions and then generate new candidate solutions. This paper explores the application of EDAs, including univariate and multivariate models. We discuss two histogram-based univariate models and one multivariate model, the Bayesian optimization algorithm (BOA), which employs Bayesian networks for modeling. By considering possible interactions between variables and exploiting explicitly stored knowledge of such interactions, EDAs can accelerate the search process while preserving search diversity. Unlike most existing approaches applied to uncertainty quantification, the Bayesian network allows the BOA to build solutions using flexible rules learned from the models obtained, rather than fixed rules, leading to better solutions and improved convergence. The BOA is naturally suited to finding good solutions in complex high-dimensional spaces, such as those typical in reservoir-uncertainty quantification. We demonstrate the effectiveness of EDA by applying the well-known synthetic PUNQ-S3 case with multiple wells. This allows us to verify the methodology in a well-controlled case. Results show better estimation of uncertainty when compared with some other traditional population-based algorithms.
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Shourian, M., S. Jamshid Mousavi, M. B. Menhaj, and E. Jabbari. "Neural-network-based simulation-optimization model for water allocation planning at basin scale." Journal of Hydroinformatics 10, no. 4 (October 1, 2008): 331–43. http://dx.doi.org/10.2166/hydro.2008.057.
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Heuristic search techniques are highly flexible, though they represent computationally intensive optimization methods that may require thousands of evaluations of expensive objective functions. This paper integrates MODSIM, a generalized river basin network flow model, a particle swarm optimization (PSO) algorithm and artificial neural networks into a modeling framework for optimum water allocations at basin scale. MODSIM is called in the PSO model to simulate a river basin system operation and to evaluate the fitness of each set of selected design and operational variables with respect to the model's objective function, which is the minimization of the system's design and operational cost. Since the direct incorporation of MODSIM into a PSO algorithm is computationally prohibitive, an ANN model as a meta-model is trained to approximate the MODSIM modeling tool. The resulting model is used in the problem of optimal design and operation of the upstream Sirvan river basin in Iran as a case study. The computational efficiency of the model makes it possible to analyze the model performance through changing its parameters so that better solutions are obtained compared to those of the original PSO–MODSIM model.
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Ding, Lintao, Chenguang Shi, Wei Qiu, and Jianjiang Zhou. "Joint Dwell Time and Bandwidth Optimization for Multi-Target Tracking in Radar Network Based on Low Probability of Intercept." Sensors 20, no. 5 (February 26, 2020): 1269. http://dx.doi.org/10.3390/s20051269.
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Radar network systems have been demonstrated to offer numerous advantages for target tracking. In this paper, a low probability of intercept (LPI)-based joint dwell time and bandwidth optimization strategy is proposed for multi-target tracking in a radar network. Since the Bayesian Cramer–Rao lower bound (BCRLB) provides a lower bound on parameter estimation, it can be utilized as the accuracy metric for target tracking. In this strategy, in order to improve the LPI performance of the radar network, the total dwell time consumption of the underlying system is minimized, while guaranteeing a predetermined tracking accuracy. There are two adaptable parameters in the optimization problem: one for dwell time, and the other for bandwidth allocation. Since the nonlinear programming-based genetic algorithm (NPGA) can solve the nonlinear problem well, we develop a method based upon NPGA to solve the resulting problem. The simulation results demonstrate that the proposed strategy has superiority over traditional algorithms, and can achieve a better LPI performance of this radar network.
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Xiong, Ao, Yuanzheng Tong, Shaoyong Guo, Yanru Wang, Sujie Shao, and Lin Mei. "An Optimal Allocation Method of Power Multimodal Network Resources Based on NSGA-II." Wireless Communications and Mobile Computing 2021 (October 26, 2021): 1–10. http://dx.doi.org/10.1155/2021/9632277.
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Basic services for power business were provided by the power multimodel network providers. However, because the power multimodal network is usually complex and changeable, the service of power business is often unstable. This problem can be solved by a suitable network resource optimization method. Therefore, how to design a network resource optimization method that seeks a compromise between multiple performance indicators that achieve the normal operation of power multimode networks is still extremely challenging. An optimal allocation method of power multimodal network resources based on NSGA-II was proposed by this paper. Firstly, the power multimodal network-resource model is established, and the problems existing in the resource optimization process are analyzed. Secondly, preprocessing technology and indirect coding technology are applied to NSGA-II, which solves the coding problem and convergence problem of the application of genetic algorithm to the optimization of network resource allocation. Finally, the simulation results show that, compared with the control algorithm, this method has further optimized the various indicators of the resource allocation of the power multimodal network, and the performance has been improved by more than 6%.
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Li, Hong, and Xue Ding. "Application Research on Improved Fusion Algorithm Based on BP Neural Network and POS." Applied Mechanics and Materials 733 (February 2015): 898–901. http://dx.doi.org/10.4028/www.scientific.net/amm.733.898.
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Optimization problem is the problem which can be often encountered mostly in industrial design, and the key of optimization is to find the global optimum and higher constriction speed. This paper proposes a PSO algorithm based on BP neural network by neural network trains and selects individual extreme best randomly, to make the particle follow the optimal particle in the solution space search, and obtain the optimum extreme best in the whole situation. Through the application of the simulation experiment on image segmentation showed that the algorithm is suitable in dealing with multiple types function and constraint, with fast convergence speed, and easy combination with traditional optimization methods, thus improving its own limitations, and solving problems more efficiently.
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Lebedeva, Ol'ga. "DESIGNING NETWORK OF CITY PUBLIC TRANSPORT ON THE BASIS OF ADDITIVE METHOD." Scientific Papers Collection of the Angarsk State Technical University 2020, no. 1 (June 23, 2020): 158–62. http://dx.doi.org/10.36629/2686-7788-2020-1-158-162.
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The article describes the main features of the optimization model, which can be used to design a network of urban public transport. Design tasks can be solved using a model that allows you to redesign a part of the network or the entire network as a whole. The model consists of an additive procedure in which the decision to include a route in the network or increase the interval of movement is based on an economic criterion - an estimate of the Lagrange multiplier for the optimization problem. The main advantage of the model is that the design problem is solved only through the optimization process. The optimization process remains understandable, and the model does not require the use of special software. The simulation results are given in the article.
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Pachelski, Wojciech, and Paweł Postek. "Optimization of observation plan based on the stochastic characteristics of the geodetic network." Reports on Geodesy and Geoinformatics 101, no. 1 (June 1, 2016): 16–26. http://dx.doi.org/10.1515/rgg-2016-0018.
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Abstract Optimal design of geodetic network is a basic subject of many engineering projects. An observation plan is a concluding part of the process. Any particular observation within the network has through adjustment a different contribution and impact on values and accuracy characteristics of unknowns. The problem of optimal design can be solved by means of computer simulation. This paper presents a new method of simulation based on sequential estimation of individual observations in a step-by-step manner, by means of the so-called filtering equations. The algorithm aims at satisfying different criteria of accuracy according to various interpretations of the covariance matrix. Apart of them, the optimization criterion is also amount of effort, defined as the minimum number of observations required. A numerical example of a 2-D network is illustrated to view the effectiveness of presented method. The results show decrease of the number of observations by 66% with respect to the not optimized observation plan, which still satisfy the assumed accuracy.
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Emmerich, Michael, Monika Grötzner, and Martin Schütz. "Design of Graph-Based Evolutionary Algorithms: A Case Study for Chemical Process Networks." Evolutionary Computation 9, no. 3 (September 2001): 329–54. http://dx.doi.org/10.1162/106365601750406028.
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This paper describes the adaptation of evolutionary algorithms (EAs) to the structural optimization of chemical engineering plants, using rigorous process simulation combined with realistic costing procedures to calculate target function values. To represent chemical engineering plants, a network representation with typed vertices and variable structure will be introduced. For this representation, we introduce a technique on how to create problem specific search operators and apply them in stochastic optimization procedures. The applicability of the approach is demonstrated by a reference example. The design of the algorithms will be oriented at the systematic framework of metricbased evolutionary algorithms (MBEAs). MBEAs are a special class of evolutionary algorithms, fulfilling certain guidelines for the design of search operators, whose benefits have been proven in theory and practice. MBEAs rely upon a suitable definition of a metric on the search space. The definition of a metric for the graph representation will be one of the main issues discussed in this paper. Although this article deals with the problem domain of chemical plant optimization, the algorithmic design can be easily transferred to similar network optimization problems. A useful distance measure for variable dimensionality search spaces is suggested.
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Jiang, Jingen, and Yongchun Xia. "Optimization and Simulation of Literature Aided Reading System Based on Wireless Sensor Network." Journal of Sensors 2021 (October 6, 2021): 1–11. http://dx.doi.org/10.1155/2021/3467411.
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This paper designs and implements an intelligent online reading platform based on wireless sensor network. The platform adopts SSM framework based on Spring architecture as the cornerstone and is oriented to ordinary users and teachers and students. Ordinary users can carry out normal online reading on the platform, and teachers and students can carry out auxiliary reading teaching on the platform. The online reading platform is an interactive reading platform system integrating online reading, EPub resource generation and management, and user reading data statistics. The whole platform is composed of registration and login module, online reading module, EPub e-book generation module, and reading data report module. Spring, Spring MVC, and Mybatis framework are used to achieve hierarchical solution and improve development efficiency. For the problems that may occur in the process of EPub generation, an intelligent EPub generation mechanism is designed and implemented to achieve intelligent error correction and improve the stability of EPub generation. Platform design and implementation of a reading data report generation system can be in the background of the report generation and download. In addition, the random extraction problem in platform business is also analyzed, the problem model is established, and the database random extraction scheme commonly used in the industry is studied. The application of wireless sensor for reading aid is less and mostly stays in theory. Based on the traditional intelligent clustering system, the system is designed to improve the system scalability and provide a feasible literature assisted reading scheme while ensuring the accuracy and efficiency of the system.
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Cao, Lu, An Zhang, and Feng Juan Guo. "Cooperative Target Allocation for UCAV Team Air-to-Ground Attack Based on Decision Graph Bayesian Optimization Algorithm." Advanced Materials Research 457-458 (January 2012): 655–62. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.655.
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In order to control and optimize cooperative air-to-ground attack decision-making of the unmanned combat aerial vehicle (UCAV) team, the principle of income maximum and loss minimum of UCAV team is built firstly. Accordingly, the mathematical model of cooperative target allocation is built based on the decision variables and constraints. Then Bayesian optimization algorithm (BOA) is introduced which is one kind of the evolution algorithm. For improving the ability of the BOA, decision graph is introduced to enhance the represent and learn of Bayesian network and compress the parameter saving. Finally decision graph Bayesian optimization algorithm (DBOA) is utilized to optimize and analyze the model. The simulation results verify that the mathematical model of cooperative target allocation can reflect the importance of cooperative decision-making, the DBOA can converge quickly to the global optimal solution and can effectively solve the cooperative target allocation problem of UCAV team air-to-ground attack.
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Zhang, Zhizhuo, and Change Zheng. "Simulation of Robotic Arm Grasping Control Based on Proximal Policy Optimization Algorithm." Journal of Physics: Conference Series 2203, no. 1 (February 1, 2022): 012065. http://dx.doi.org/10.1088/1742-6596/2203/1/012065.
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Abstract There are many kinds of inverse kinematics solutions for robots. Deep reinforcement learning can make the robot spend a short time to find the optimal inverse kinematics solution. Aiming at the problem of sparse rewards in the process of deep reinforcement learning, this paper proposes an improved PPO algorithm. Firstly, built a simulation environment for the operation of the robotic arm. Secondly, use a convolutional neural network to process the data read by the camera of the robotic arm, obtaining a network about Actor and Critic. Thirdly, based on the principle of inverse kinematics of the robotic arm and the reward mechanism in deep reinforcement learning, design a hierarchical reward function containing motion accuracy to promote the convergence of the PPO algorithm. Finally, compare the improved PPO algorithm with the traditional PPO algorithm. The results show that the improved PPO algorithm has improved both the convergence speed and the operating accuracy.
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Snoeck, André, and Matthias Winkenbach. "A Discrete Simulation-Based Optimization Algorithm for the Design of Highly Responsive Last-Mile Distribution Networks." Transportation Science 56, no. 1 (January 2022): 201–22. http://dx.doi.org/10.1287/trsc.2021.1105.
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Online and omnichannel retailers are proposing increasingly tight delivery deadlines, moving toward instant on-demand delivery. To operate last-mile distribution systems with such tight delivery deadlines efficiently, defining the right strategic distribution network design is of paramount importance. However, this problem exceeds the complexity of the strategic design of traditional last-mile distribution networks for two main reasons: (1) the reduced time available for order handling and delivery and (2) the absence of a delivery cut-off time that clearly separates order collection and delivery periods. This renders state-of-the-art last-mile distribution network design models inappropriate, as they assume periodic order fulfillment based on a delivery cutoff. In this study, we propose a metamodel simulation-based optimization (SO) approach to strategically design last-mile distribution networks with tight delivery deadlines. Our methodology integrates an in-depth simulator with traditional optimization techniques by extending a traditional black-box SO algorithm with an analytical model that captures the underlying structure of the decision problem. Based on a numerical study inspired by the efforts of a global fashion company to introduce on-demand distribution with tight delivery deadlines in Manhattan, we show that our approach outperforms contemporary SO approaches as well as deterministic and stochastic programming methods. In particular, our method systematically yields network designs with superior expected cost performance. Furthermore, it converges to good solutions with a lower computational budget and is more consistent in finding high-quality solutions. We show how congestion effects in the processing of orders at facilities negatively impact the network performance through late delivery of orders and reduced potential for consolidation. In addition, we show that the sensitivity of the optimal network design to congestion effects in order processing at the facilities increases as delivery deadlines become increasingly tight.
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Jia, Wenxian, Menghan Liu, and Jie Zhou. "Adaptive Chaotic Ant Colony Optimization for Energy Optimization in Smart Sensor Networks." Journal of Sensors 2021 (July 1, 2021): 1–13. http://dx.doi.org/10.1155/2021/5051863.
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Smart sensor network has the characteristics of low cost, low power consumption, real time, strong adaptability, etc., and it has a wide range of application prospects in the agricultural field. However, the smart sensor node is limited by its own energy; it also faces many bottlenecks in agricultural applications. Therefore, balancing the energy consumption of nodes and extending the life of the network are important considerations in the design of efficient routing for smart sensor networks. Aiming at the problem of energy constraints, this paper proposes an intelligent sensor network clustering algorithm based on adaptive chaotic ant colony optimization (ACACO). ACACO introduces logical chaotic mapping to interfere with the pheromone on the initial path and uses the adaptive strategy to improve the transition probability formula. After selecting the best next hop node, the advancing ants are released to update the local pheromone, and the current pheromone content is adjusted by the chaos factor. When the ants determine the path, they release subsequent ants to update the global pheromone. The simulation results show that ACACO has obvious advantages over genetic algorithm (GA) and particle swarm optimization (PSO).
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Medbøen, Carl Axel Benjamin, Magnus Bolstad Holm, Mohamed Kais Msakni, Kjetil Fagerholt, and Peter Schütz. "Combining Optimization and Simulation for Designing a Robust Short-Sea Feeder Network." Algorithms 13, no. 11 (November 20, 2020): 304. http://dx.doi.org/10.3390/a13110304.
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Here we study a short-sea feeder network design problem based on mother and daughter vessels. The main feature of the studied system is performing transshipment of cargo between mother and daughter vessels at appropriate locations at sea. This operation requires synchronization between both types of vessels as they have to meet at the same location at the same time. This paper studies the problem of designing a synchronized feeder network, explicitly accounting for the effect of uncertain travel times caused by harsh weather conditions. We propose an optimization-simulation framework to find robust solutions for the transportation system. The optimization model finds optimal routes that are then evaluated by a discrete-even simulation model to measure their robustness under uncertain weather conditions. This process of optimization simulation is repeated until a satisfactory condition is reached. To find even better solutions, we include different performance-improving strategies by adding robustness during route generation or exploiting flexibility in sailing speed to recover from delays. We apply the solution method to a case based on realistic data from a Norwegian shipping company. The results show that the method finds near-optimal solutions that offer robustness against schedule perturbations due to harsh weather. They also highlight the importance of considering uncertainty when designing a short-sea feeder network with transshipment at sea.
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Chen, Yang, Xiaolin Wang, and Chang Zhang. "Wavelet Transform-Based 3D Landscape Design and Optimization for Digital Cities." International Journal of Antennas and Propagation 2022 (September 7, 2022): 1–10. http://dx.doi.org/10.1155/2022/1184198.
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As a hot concept, the digital city has developed rapidly in recent years. The digital city uses information technology to realize all the contents of the past, present, and future of the city on the network, and can build a three-dimensional visual landscape. However, the traditional information fusion model has the problems of noise susceptibility and low efficiency in landscape design. In order to solve the above problems, this article proposes a wavelet transform-based 3D landscape design and optimization method for digital cities, which removes the noise influenced by wavelet change and builds an information fusion model based on neural network to complete the design optimization of the three-dimensional landscape of the digital city. Firstly, for the wavelet change denoising problem, an effective denoising algorithm for natural noise and abnormal noise is proposed by combining convolutional neural network and wavelet transform. The algorithm extracts mixed feature information of local long path and local short path based on the information retention module, and decomposes the information by combining wavelet transform, inputs the different components obtained from the decomposition into the network for training, and removes the noise by subsequent feature screening of the network structure. Then, aiming at the optimization of 3D landscape design, an information fusion model based on long-short time memory network and radial basis backpropagation network is proposed for fusing multiple sources of information in the digital city to evaluate the landscape. The method collects digital city feature information at the information layer and preprocesses the feature information by a rate detection algorithm. Then, LSTM and RBF-BP neural networks in deep learning are used in the feature layer for adaptive learning of multiple feature signals, and finally, fuzzy logic is used to control the system decision output to improve the efficiency of 3D landscape design. Finally, the simulation experimental results show that the proposed denoising method in this article can better retain the texture details in the images and the denoised images have better visual effects; the proposed information fusion model has higher accuracy compared with the traditional methods. Combining this method to design and optimize 3D landscapes in digital cities can improve the efficiency of landscape design.
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Lin, Hongzhi, and Yongping Zhang. "Transportation-oriented spatial allocation of land use development: a simulation-based optimization method." SIMULATION 96, no. 7 (May 27, 2020): 583–91. http://dx.doi.org/10.1177/0037549720920374.
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Urban development usually deteriorates the transportation system. For sustainable urban development, policymakers often face the challenging problem of how to optimally allocate overall land use quotas across a number of residential locations according to the performance of the transportation system. This is a kind of Stackelberg competition, where policymakers make land use decisions and travelers make behavioral responses. A novel bi-level model is formulated to solve this problem. The upper-level model minimizes the total system travel time by land use allocation, while at the lower level are sequential models with feedback for transportation system equilibrium. The Dirichlet allocation algorithm, a simulation-based heuristic algorithm, is designed to solve this bi-level model. A simulation experiment using the Nguyen–Dupuis network is then used to verify the proposed model and algorithm. The results from the simulation experiment demonstrate that not only are the model and algorithm operational but that they also provide an effective tool for policymakers to plan for land use development.
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Zhang, Lulu, Zhi-Qin John Xu, and Yaoyu Zhang. "Data-informed deep optimization." PLOS ONE 17, no. 6 (June 23, 2022): e0270191. http://dx.doi.org/10.1371/journal.pone.0270191.
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Motivated by the impressive success of deep learning in a wide range of scientific and industrial applications, we explore in this work the application of deep learning into a specific class of optimization problems lacking explicit formulas for both objective function and constraints. Such optimization problems exist in many design problems, e.g., rotor profile design, in which objective and constraint values are available only through experiment or simulation. They are especially challenging when design parameters are high-dimensional due to the curse of dimensionality. In this work, we propose a data-informed deep optimization (DiDo) approach emphasizing on the adaptive fitting of the the feasible region as follows. First, we propose a deep neural network (DNN) based adaptive fitting approach to learn an accurate DNN classifier of the feasible region. Second, we use the DNN classifier to efficiently sample feasible points and train a DNN surrogate of the objective function. Finally, we find optimal points of the DNN surrogate optimization problem by gradient descent. To demonstrate the effectiveness of our DiDo approach, we consider a practical design case in industry, in which our approach yields good solutions using limited size of training data. We further use a 100-dimension toy example to show the effectiveness of our approach for higher dimensional problems. Our results indicate that, by properly dealing with the difficulty in fitting the feasible region, a DNN-based method like our DiDo approach is flexible and promising for solving high-dimensional design problems with implicit objective and constraints.
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Alswaitti, Mohammed, Kamran Siddique, Shulei Jiang, Waleed Alomoush, and Ayat Alrosan. "Dimensionality Reduction, Modelling, and Optimization of Multivariate Problems Based on Machine Learning." Symmetry 14, no. 7 (June 21, 2022): 1282. http://dx.doi.org/10.3390/sym14071282.
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Simulation-based optimization design is becoming increasingly important in engineering. However, carrying out multi-point, multi-variable, and multi-objective optimization work is faced with the “Curse of Dimensionality”, which is highly time-consuming and often limited by computational burdens as in aerodynamic optimization problems. In this paper, an active subspace dimensionality reduction method and the adaptive surrogate model were proposed to reduce such computational costs while keeping a high precision. In this method, the active subspace dimensionality reduction technique, three-layer radial basis neural network approach, and polynomial fitting process were presented. For the model evaluation, a NASA standard test function problem and RAE2822 airfoil drag reduction optimization were investigated in the experimental design problem. The efficacy of the method was proved by both the experimental examples in which the adaptive surrogate model in a dominant one-dimensional active subspace is given and the optimization efficiency was improved by two orders. Furthermore, the results show that the constructed surrogate model reduced dimensionality and alleviated the complexity of conventional multivariate surrogate modeling with high precision.
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Ni, Lei, Xinyu Da, Hang Hu, and Miao Zhang. "Energy Efficiency Design for Secure MISO Cognitive Radio Network Based on a Nonlinear EH Model." Mathematical Problems in Engineering 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/9149258.
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In this work, we investigate the secrecy energy efficiency (SEE) optimization problem for a multiple-input single-output (MISO) cognitive radio (CR) network based on a practical nonlinear energy-harvesting (EH) model. In particular, the energy receiver (ER) is assumed to be a potential eavesdropper due to the open architecture of a CR network with simultaneous wireless information and power transfer (SWIPT), such that the confidential message is prone to be intercepted in wireless communications. The aim of this work is to provide a secure transmit beamforming design while satisfying the minimum secrecy rate target, the minimum EH requirement, and the maximum interference leakage power to primary user (PU). In addition, we consider that all the channel state information (CSI) is perfectly known at the secondary transmitter (ST). We formulate this beamforming design as a SEE maximization problem; however, the original optimization problem is not convex due to the nonlinear fractional objective function. To solve it, a novel iterative algorithm is proposed to obtain the globally optimal solution of the primal problem by using the nonlinear fractional programming and sequential programming. Finally, numerical simulation results are presented to validate the performance of the proposed scheme.
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JI, XIAOYU, XIANDE ZHAO, and DEMING ZHOU. "A FUZZY PROGRAMMING APPROACH FOR SUPPLY CHAIN NETWORK DESIGN." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, supp02 (April 2007): 75–87. http://dx.doi.org/10.1142/s0218488507004625.
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This paper presents a fuzzy programming method to design supply chain network, in which the customer demands and transportation costs are assumed to be fuzzy parameters. Existing researches on supply chain network design problem are either restricted on deterministic environment or only address stochastic parameters. In this paper, we consider this problem in fuzzy environment. Under different criteria, we format three types of models for the decision makers: expected cost optimization model, chance-constrained model and chance maximization model. A genetic algorithm based on fuzzy simulation is developed to solve the proposed fuzzy models. Moreover, some numerical examples are presented to illustrate the effectiveness of models and solution algorithm.
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Ma, Yinpu, and Kai Liu. "Intelligent Transportation Design Based on Iterative Learning." Journal of Mathematics 2022 (February 8, 2022): 1–7. http://dx.doi.org/10.1155/2022/5027412.
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Most of the existing traffic optimization control methods are based on accurate mathematical models. As an uncertain and complex system, the urban traffic system faces difficulty in accurately calibrating the model parameters. Therefore, the existing methods become very difficult in the actual application process. Based on the massive data contained in the urban traffic system and the repetitive characteristics of traffic flow, this paper proposes a hierarchical traffic signal control method for urban road network based on iterative learning control. The simulation results show that the algorithm can achieve better control effect and can solve the problem of urban traffic congestion more effectively than traditional traffic control methods.
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Jiang, Jiehui, Dezhi Zhang, Shuangyan Li, and Yajie Liu. "Multimodal Green Logistics Network Design of Urban Agglomeration with Stochastic Demand." Journal of Advanced Transportation 2019 (August 7, 2019): 1–19. http://dx.doi.org/10.1155/2019/4165942.
Full textAbstract:
This study investigates a multimodal green logistics network design problem of urban agglomeration with stochastic demand, in which different logistics authorities among the different cities jointly optimize the logistics node configurations and uniform carbon taxes over logistics transport modes to maximize the total social welfare of urban agglomeration and consider logistics users’ choice behaviors. The users’ choice behaviors are captured by a logit-based stochastic equilibrium model. To describe the game behaviors of logistics authorities in urban agglomeration, the problem is formulated as two nonlinear bilevel programming models, namely, independent and centralized decision models. Next, a quantum-behaved particle swarm optimization (QPSO) embedded with a Method of Successive Averages (MSA) is presented to solve the proposed models. Simulation results show that to achieve the overall optimization layout of the green logistics network in urban agglomeration the logistics authorities should adopt centralized decisions, construct a multimode logistics network, and make a reasonable carbon tax.