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Integration of Train Regulation and Speed Profile Optimization Based on Feature Learning and hybrid search algorithm

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第2期11卷 2535-2544页

作者： Zhou, Min Hou, Zhuopu Wu, Xingtang Dong, Hairong Wang, Fei-Yue Beijing Jiaotong Univ State Key Lab Adv Rail Autonomous Operat Beijing 100044 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

The independent hierarchy of train dispatching command and train operation control in the existing urban rail transit systems restricts the improvement of operational efficiency and emergency handling capability. This article focuses on integrating train regulation and speed profile optimization by utilizing a feature learning and hybrid search algorithm. Specifically, a genetic algorithm (GA) is used to optimize the train speed profile for a fixed interval running time, and then, the generated labeled sample data are used to train a convolutional neural network (CNN) to learn and extract the features of the optimal speed profile. The nonlinear mapping relationship between input and output variables in trajectory optimization is characterized by a well-trained CNN to reduce the computation time of the optimal speed profile during train regulation. The input variables comprise line conditions and interval running times, while the output variables include the corresponding energy consumption and operating condition switching points of the optimal speed profile. An integrated model of train regulation and operation control is developed with the objective of minimizing total train delay time and energy consumption. To ensure convergence and global search capability, we design a hybrid search algorithm-based train regulation algorithm. Simulation experiments are conducted using data from the Beijing Yizhuang line to validate the effectiveness of the proposed model and algorithms. The experimental results demonstrate that the proposed method can provide an optimal scheme for train regulation and speed profiles.

关键词： Convolutional neural network (CNN) hybrid search algorithm integration speed profile train control train regulation

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Formation Deployment of hybrid Control search algorithm for Multi-robot in Two-Dimensional Space 38

Formation Deployment of Hybrid Control Search Algorithm for ...

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38th Chinese Control Conference (CCC)

作者： Zhu, Dezheng Zhu, Weicao Huan, Guoyang China Elect Technol Grp Corp Res Inst 28 Nanjing 210007 Peoples R China

ISBN: (纸本)9789881563972

In order to deploy multi-robot into a 2-D desired formation during the seeking process, this paper proposes a hybrid seeking algorithm with the formation deployment. Firstly, this paper expands the 1-D deployment algorithm to 2-D, and then combines with the extreme seeking algorithm to obtain the hybrid seeking algorithm. With the algorithm, multi-robot can deploy into a desired 2-D formation during seeking process. Simulation results are provided to illustrate the effectiveness of the algorithm.

关键词： multi-robot extreme seeking algorithm formation deployment hybrid search algorithm

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Simulated annealing-chaotic search algorithm based optimization of reverse osmosis hybrid desalination system driven by wind and solar energies

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SOLAR ENERGY 2018年 173卷 964-975页

作者： Zhang, Guozhao Wu, Baojia Maleki, Akbar Zhang, Weiping Yanbian Univ Coll Sci Dept Phys Yanji 133000 Jilin Peoples R China Univ Tehran Fac New Sci & Technol Dept Renewable Energies Tehran Iran Nanchang Univ Dept Elect Informat Engn Nanchang Jiangxi Peoples R China

Small reverse osmosis (RO) based desalination plants driven by wind and solar energies are attractive options to meet the requirements of autonomous areas. The complexity of these systems requires proper optimization of the solar and wind energy generation with storage needs. The main contribution of this study is a design optimization method of a hybrid reverse osmosis desalination plant powered by solar and wind energy. There are many investigations based on hybrid energy systems but the investigation of a useful model and effective methods are rarely found. Here, the possibility of three autonomous hybrid arrangements, namely, solar-wind-battery-RO desalination, solar-battery-RO desalination, and wind-battery-RO desalination, are investigated. Integer and continuous variables in the optimization model of the hybrid schemes for an autonomous region of Iran are considered. The main optimization function of minimizing the life cycle cost is used to evaluate different types of renewable energy systems for powering reverse osmosis desalination. Also, the probability of power interruption is used to measure the reliability of the hybrid schemes. For this aim, a new hybrid search algorithm is developed for simulating annealing and chaotic system. The results are compared with those from original chaotic search and simulated annealing algorithms. The simulation results show that the relative error in the hybrid scheme between the best performances of the hybrid search algorithm and the simulated annealing algorithm is 47.75%, and 4.08% for the hybrid search algorithm and chaotic search algorithm. In overall terms, the results demonstrate that the hybrid search algorithm provided the best performance between the original simulated annealing and the chaotic search algorithm. Additionally, the hybrid renewable energy system decreases system cost and increases system reliability for increasing fresh water availability and meeting the electricity load demands.

关键词： hybrid renewable energy system Solar/wind/desalination Optimization hybrid search algorithm Simulated annealing Chaotic search

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hybrid Peer-to-Peer search algorithm in All IP Broadband Mobile Networks

Hybrid Peer-to-Peer Search Algorithm in All IP Broadband Mob...

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2nd IEEE International Conference on Advanced Computer Control

作者： Zhao Yu Tang Hong-bo Wang Xiao-lei Natl Digital Switching Syst Engn & Technol R&D Ct Zhengzhou Peoples R China

ISBN: (纸本)9781424458479

To improve the quality of search algorithm in all IP broadband mobile networks, a hybrid Peer-to-Peer (P2P) search algorithm (HPSA) is proposed which is based on the features of P2P technology and all IP broadband mobile networks. Firstly, this algorithm defines parameters to decide the threshold by means of grey relational analysis (GRA);then we put forward an adaptive threshold method to adapt the dynamic environment;finally, it also presents the optimal threshold. The simulations show that HPSA makes a good performance working in all IP broadband mobile networks, the new algorithm improves search quality and mobile users' experience efficiently.

关键词： All IP broadband mobile networks Peer-to-Peer hybrid search algorithm adapvtive threshold

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Efficient hybrid search for visual reconstruction problems

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IMAGE AND VISION COMPUTING 1999年第1期17卷 37-49页

作者： Lai, SH Vemuri, BC Siemens Corp Res Princeton NJ 08540 USA Univ Florida Dept Comp & Informat Sci Gainesville FL USA

Visual reconstruction refers to extracting stable descriptions from visual data ([1];A. Blake and A. Zisserman, Visual Reconstruction, MIT Press, Cambridge, MA, 1987). Visual reconstruction problems are commonly formulated in an optimization framework and normally require the optimization of nonconvex functions especially when discontinuity preserving image/shape recovery is the goal. Example problems include, image restoration, surface reconstruction, shape from shading etc. Most existing deterministic methods fail to reach the global optimum and lack the generality to incorporate reasonably complex interactions between boolean valued line process variables used for representing the presence (absence) of discontinuities. Stochastic methods for solving such problems e.g. the simulated annealing algorithm or variants thereof do achieve a global optimum but are plagued by slow convergence rates. In this paper, we present a new hybrid search algorithm as an efficient solution for achieving the global optimum of the nonconvex function derived from a Markov random field formulation which allows for incorporation of complex interactions between the line process variables to better constraint the line processes. In the hybrid search, for the stochastic part, we develop an informed genetic algorithm (GA) while employing an incomplete Cholesky preconditioned conjugate gradient algorithm ([23];S.H. Lai and B.C. Vemuri, Robust and efficient algorithms for optical flow computation, in: Proceedings of the International Symposium on Computer Vision, Coral Gables, FL, 1995, pp. 455-460) for the deterministic part. Our informed GA consists of a reproduction operator and an informed mutation operator. The informed mutation operator exploits specific domain knowledge in the search and is accomplished by the Gibbs sampler. Our hybrid search algorithm is highly parallelizable and leads to a globally optimal solution. The performance of our algorithm is demonstrated via experimental res

关键词： visual reconstruction hybrid search algorithm non-convex optimization informed genetic algorithm

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Local search with constraint propagation and conflict-based heuristics

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ARTIFICIAL INTELLIGENCE 2002年第1期139卷 21-45页

作者： Jussien, N Lhomme, O Ecole Mines F-44307 Nantes 3 France ILOG F-06560 Valbonne France

search algorithms for solving CSP (Constraint Satisfaction Problems) usually fall into one of two main families: local search algorithms and systematic algorithms. Both families have their advantages. Designing hybrid approaches seems promising since those advantages may be combined into a single approach. In this paper, we present a new hybrid technique. It performs a local search over partial assignments instead of complete assignments, and uses filtering techniques and conflict-based techniques to efficiently guide the search. This new technique benefits from both classical approaches: a priori pruning of the search space from filtering-based search and possible repair of early mistakes from local search. We focus on a specific version of this technique: tabu decision-repair. Experiments done on open-shop scheduling problems show that our approach competes well with the best highly specialized algorithms. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： constraint satisfaction conflict-based search local search hybrid search algorithm repair methods

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Bayesian selection of best subsets via hybrid search

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COMPUTATIONAL STATISTICS 2021年第3期36卷 1991-2007页

作者： Jin, Shiqiang Goh, Gyuhyeong Kansas State Univ Dept Stat Manhattan KS 66506 USA

Over the past decades, variable selection for high-dimensional data has drawn increasing attention. With a large number of predictors, there rises a big challenge for model fitting and prediction. In this paper, we develop a new Bayesian method of best subset selection using a hybrid search algorithm that combines a deterministic local search and a stochastic global search. To reduce the computational cost of evaluating multiple candidate subsets for each update, we propose a novel strategy that enables us to calculate exact marginal likelihoods of all neighbor models simultaneously in a single computation. In addition, we establish model selection consistency for the proposed method in the high-dimensional setting in which the number of possible predictors can increase faster than the sample size. Simulation study and real data analysis are conducted to investigate the performance of the proposed method.

关键词： Bayesian variable selection Best subset selection High-dimensional regression analysis hybrid search algorithm

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The Implementation search Deployment Behavior algorithm for Multi-Agents 41

The Implementation Search Deployment Behavior Algorithm for ...

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第41届中国控制会议

作者： Dezheng Zhu Ao Chen Xiaohong Guo Guangjia Fang Nanjing Research Institute of Electronic Engineering

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In order to deploy multi-agents into a 2-D desired formation during the seeking process,this paper proposes a hybrid search algorithm with the formation ***,this paper expands the 1-D deployment algorithm to 2-D,and then combines with the extreme seeking algorithm to obtain the hybrid search *** the algorithm,multi-agents can deploy into a desired 2-D formation during seeking *** results are provided to illustrate the effectiveness of the algorithm.

关键词： multi-agents 2-D formation control hybrid search algorithm

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hybrid search Multi-discipline Feasible Design Optimization of a Typical Space Launch Vehicle 7

Hybrid Search Multi-discipline Feasible Design Optimization ...

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7th International Conference on Recent Advances in Space Technologies (RAST)

作者： Bataleblu, Ali Asghar Roshanian, Jafar Ebrahimi, Masoud KN Toosi Univ Technol Dept Aerosp Engn Tehran Iran Tarbiat Modares Univ Technol Dept Mech Engn Tehran Iran

ISBN: (纸本)9781479976973

This paper presents design of a typical Space Launch Vehicle (SLV) using multidisciplinary design optimization (MDO) Methodology. A one level MDO structure, Multi-Discipline Feasible (MDF) has been used. A hybrid search algorithm (HSA), which associates Simulated Annealing (SA) as a global optimizer with Simplex as a local optimizer is employed. The main objective of this multi-disciplinary design is minimizing the gross lift off mass (GLOM) of the vehicle that is an impetus driver on vehicle performance and cost. The main disciplines that are considered for this design include propulsion and weight, aerodynamic and trajectory simulation. The SLV design variables and the orbital insertion trajectory profile variables despite mission and flight trajectory constraints are optimized simultaneously, whereas the mass characteristics of the vehicle were assigned. The optimized SLV is capable of delivering a small satellite of 250 kg to a circular orbit of 900 km altitude. Simulation results demonstrate the superior performance of the HSA over challenging MDO problems. Optimal design shows the GLOM reduction about 7.0%, which is quite significant at conceptual design level.

关键词： Multidisciplinary Design Optimization Space Launch Vehicle hybrid search algorithm Simulated Annealing algorithm Simplex algorithm

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Bayesian Solutions to High-Dimensional Data Challenges Using hybrid search

Bayesian Solutions to High-Dimensional Data Challenges Using...

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作者： Jin, Shiqiang Kansas State University

学位级别：Ph.D.

In the era of Big Data, variable selection with high-dimensional data has drawn increasing attention. With a large number of predictors, there rises a big challenge for model fitting and prediction. In this dissertation, we propose three different yet interconnected methodologies, which include theory, computation, and real applications for various scenarios of regression analysis. The primary goal in this dissertation is to develop powerful Bayesian solutions to high-dimensional data challenges using a new variable selection strategy, called hybrid search. To effectively reduce computation costs in high-dimensional data analysis, we propose novel computational strategies that can quickly evaluate a large number of marginal likelihoods simultaneously within a single computation. In Chapter 1, we discuss background and current challenges in high-dimensional variable selection. The motivation of our study is also justified. In Chapter 2, we introduce a new Bayesian method of best subset selection in the context of linear regression. The proposed method rapidly finds the best subset via a hybrid search algorithm that combines deterministic local search and stochastic global search. In Chapter 3, on the basis of the approach in Chapter 2, we extend it to a framework of multivariate linear regression model, which analyzes the relationship between multiple response variables and a common set of predictors. In Chapter 4, we propose a general Bayesian method to perform high-dimensional variable selection for various data types, such as binary, count, continuous and time-to-event(survival) data. Using Bayesian approximation techniques, we develop a general computing strategy that enables us to assess the marginal likelihoods of many candidate models within a single computation. In addition, to accelerate the convergence, we employ a hybrid search algorithm that can quickly explore the model spaces and accurately obtain the global maximum of marginal posterior probabilities.

关键词： Bayesian variable selection High-dimensional regression analysis Best subset selection hybrid search algorithm Multivariate regression analysis Laplace approximation

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