检索结果-内蒙古大学图书馆

Collaborative optimization of Energy Management Strategy and Adaptive Cruise Control Based on Deep Reinforcement Learning

引用

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2023年第1期9卷 34-44页

作者： Peng, Jiankun Fan, Yi Yi, Guodong Jiang, Ruhai Southeast Univ Sch Transportat Nanjing 211189 Peoples R China Southeast Univ Sch Mech Engn Nanjing 211189 Peoples R China CAS Juche Technol Co Ltd Hefei 230091 Peoples R China

Hybrid electric vehicles (HEVs) have great prospects in reducing fossil fuel consumption, and adaptive cruise control (ACC) technology provides safe and convenient travel for drivers. The fusion of the two technologies can theoretically improve the safety, comfort, and fuel economy of vehicles. Hence, the energy management strategy (EMS) of Prius, a typical HEV configuration, is studied under the car-following scenario. This optimization problem involves complex systems, inconsistent objectives, and stringent constraints, which may be challenging to conventional algorithms. Therefore, a novel deep deterministic policy gradient (DDPG)-based ecological driving strategy (DDPG-ECO) is proposed and the weights of multiple objectives are analyzed to optimize the training results. The extensive simulation experiment compares the effects of Ornstein-Uhlenbeck action noise (OUAN) and soft-max action noise (SAN), which act on the acceleration action. Simulations under different driving cycles show that the fuel economy of DDPG-ECO can achieve more than 90% of dynamic programming (DP)-based methods on the conditions of ensuring car-following performances.

关键词： Adaptive cruise control (ACC) deep reinforcement learning (DRL) energy management strategy (EMS) multiobjective optimization problem

来源：评论

学校读者我要写书评

暂无评论

A Modified Marriage in Honey-Bee optimization for multiobjective optimization problems

A Modified Marriage in Honey-Bee Optimization for Multiobjec...

引用

IEEE International Conference Intelligent Systems

作者： Patcharawadee Poolsamran Arit Thammano Computational Intelligence Laboratory Faculty of Information Technology King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9781467322768

This paper proposes a modified marriage in honey-bee optimization for solving multiobjective optimization problems. Unlike the original marriage in honey-bee optimization, the proposed algorithm divides the objective space into several colonies, each of which has its own queen. The fitness of each solution is based on 3 parameters: the size of the colony, the number of dominating solutions, and the number of dominated solutions. The nondominated solutions with highest fitness values are preferentially assigned to be the queens while the rest are assigned to be the drones. Next, all drones are assigned to the colony according to their distances from the queens of the colonies. In order to maximize a genetic variance in the population, the multiple mating is used. The multiple mating requires the queen to mate with drones from the other colonies. The proposed algorithm has been evaluated and compared to two state-of-the-art metaheuristic algorithms: the Pareto archived evolution strategy and the nondominated sorting genetic algorithm. The experimental results on 5 different ZDT benchmark functions illustrate that the proposed algorithm is able to converge to the true Pareto fronts and has better spread of solutions, as compared with the published results of the two state-of-the-art algorithms.

关键词： swarm intelligence marriage in honey-bee optimization multiobjective optimization problem queens drone honey bees colonies Marital Relationship Marriage Solutions evolution strategy Mating BISTABILITY Queens

来源：评论

学校读者我要写书评

暂无评论

Hierarchical arrangement of characteristics in product design optimization

引用

JOURNAL OF MECHANICAL DESIGN 2006年第4期128卷 701-709页

作者： Yoshimura, Masataka Taniguchi, Masahiko Izui, Kazuhiro Nishiwaki, Shinji Kyoto Univ Dept Aeronaut & Astronaut Mech Engn Div Optimum Syst Design Engn LabSakyo Ku Kyoto 6068501 Japan

This paper proposes a machine product design optimization method based on the decomposition of performance characteristics, or alternatively, extraction of simpler characteristics, that is especially responsive to the detailed features or difficulties presented by specific design problems. The optimization problems examined here are expressed using hierarchical constructions of the decomposed and extracted characteristics and the optimizations are sequentially repeated, starting with groups of characteristics having conflicting characteristics at the lowest hierarchical level and proceeding to higher levels. The proposed method not only effectively provides optimum design solutions, but also facilitates deeper insight into the design optimization results, so that ideas for optimum solution breakthroughs are more easily obtained. An applied example is given to demonstrate the effectiveness of the proposed method.

关键词： design optimization product design hierarchical optimization decomposition multiobjective optimization problem pareto optimum solutions deeper insight

来源：评论

学校读者我要写书评

暂无评论

Federated Learning Via Nonorthogonal Multiple Access for UAV-Assisted Internet of Things

引用

IEEE INTERNET OF THINGS JOURNAL 2024年第17期11卷 27994-28006页

作者： Ren, Pengfei Wang, Jingjing Tong, Ziheng Chen, Jianrui Pan, Peng Jiang, Chunxiao Beihang Univ Sch Cyber Sci & Technol Beijing 100191 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China

Federated learning (FL), utilizing data from the edge devices (EDs) while protecting user privacy has gained much attention. Its efficacy is substantially influenced by both the quantity of connected devices and the quality of wireless communications. Network congestion, resulting from multiple access and signal attenuation caused by physical obstacles may severely impact the convergence of the FL model. To address these issues, this article employs nonorthogonal multiple access (NOMA) for uplink transmission and designs a two-tier FL framework consisting of ground devices and unmanned aerial vehicles (UAVs) to ensure the construction of Line of Sight (LoS) channels from EDs to the base station. Moreover, we construct a multiobjective joint optimization problem to minimize the FL convergence time considering constraints, such as the NOMA uplink latency, ED selection strategy, local training latency, and energy consumption. We also deduce the theoretical upper bound of the convergence time and transform the proposed multiobjective problem into a solvable form by eliminating the discrete variables determined by the ED selection. In turn, we utilize the proximal policy optimization (PPO) algorithm to solve this optimization problem. Finally, the extensive experimental results demonstrate the advantages of our proposed algorithm in terms of latency and energy consumption, while yielding a high robustness and scalability.

关键词： NOMA optimization Internet of Things Convergence Training Resource management Relays Federated learning (FL) multiobjective optimization problem nonorthogonal multiple access (NOMA) resource allocation

来源：评论

学校读者我要写书评

暂无评论

A multiobjective GRASP-VND algorithm to solve the waste collection problem

引用

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2018年第2期25卷 545-567页

作者： Lopez-Sanchez, A. D. Hernandez-Diaz, A. G. Gortazar, F. Hinojosa, M. A. Pablo de Olavide Univ Econ Quantitat Methods & Econ Hist Ctra UtreraKm 1 Seville 41013 Spain Rey Juan Carlos Univ Tulipan S-N Motoles Madrid 28933 Spain

In this paper, the waste collection problem (WCP) of a city in the south of Spain is addressed as a multiobjective routing problem that considers three objectives. From the company's perspective, the minimization of the travel cost is desired as well as that of the total number of vehicles. Additionally, from the employee's point of view, a set of balanced routes is also sought. Four variants of a multiobjective hybrid algorithm are proposed. Specifically, a GRASP (greedy randomized adaptive search procedure) with a VND (variable neighborhood descent) is combined. The best GRASP-VND algorithm found is applied in order to solve the real-world WCP of a city in the south of Spain.

关键词： routing problems hybrid algorithms greedy randomized search procedure variable neighborhood descent multiobjective optimization problem waste collection problem

来源：评论

学校读者我要写书评

暂无评论

On a Sufficient Condition for Weak Sharp Efficiency in multiobjective optimization

引用

JOURNAL OF optimization THEORY AND APPLICATIONS 2018年第1期178卷 78-93页

作者： Bianchi, Monica Kassay, Gabor Pini, Rita Univ Cattolica Sacro Cuore Milan Italy Babes Bolyai Univ Cluj Napoca Romania Univ Milano Bicocca Milan Italy

In this paper, we provide sufficient conditions entailing the existence of weak sharp efficient points of a multiobjective optimization problem. The approach uses variational analysis techniques, like regularity and subregularity of the diagonal subdifferential map related to a suitable scalar equilibrium problem naturally associated to the multiobjective optimization problem.

关键词： Weak sharp efficient point multiobjective optimization problem Metricregularity Metric subregularity Equilibrium problem Diagonal subdifferential operator

来源：评论

学校读者我要写书评

暂无评论

Iteration-Complexity and Asymptotic Analysis of Steepest Descent Method for multiobjective optimization on Riemannian Manifolds

引用

JOURNAL OF optimization THEORY AND APPLICATIONS 2020年第2期184卷 507-533页

作者： Ferreira, Orizon P. Louzeiro, Mauricio S. Prudente, Leandro F. Univ Fed Goias IME BR-74001970 Goiania Go Brazil TU Chemnitz Fak Math D-09107 Chemnitz Germany

The steepest descent method formultiobjective optimization onRiemannian manifolds with lower bounded sectional curvature is analyzed. The aim of this study is twofold. First, an asymptotic analysis of the method is presented with three different finite procedures for determining the stepsize: Lipschitz, adaptive, and Armijo-type step-sizes. Second, by assuming theLipschitz continuity of a Jacobian, iteration-complexity bounds for the method with these three stepsize strategies are presented. In addition, some examples that satisfy the hypotheses of themain theoretical results are provided. Finally, the aforementioned examples are presented through numerical experiments.

关键词： Steepest descent method multiobjective optimization problem Riemannian manifold Lower bounded curvature Iteration-complexity bound

来源：评论

学校读者我要写书评

暂无评论

Necessary and sufficient conditions for multiobjective optimization problems

引用

optimization 1996年第2期36卷 97-104页

作者： Taa, A. Dept. de Mathématiques Fac. des Sci. et Techniques Gueliz-Marrakech B.P. 618 Morocco

We consider the following multiobjective optimization problem (P) Minimize F(χ) subject to χ ∈ X where F is a set-valued map between two Banach X and Y. When the set-valued map F is upper semidifferentiable and compact Luc [2] gives necessary and sufficient optimality conditions for (P) in terms of contingent derivatives. In this paper we study the same problem under weaker notion of set-valued derivatives and without neither compactness nor upper semidifferentiability assumptions.

关键词： Compact base Contingent derivative multiobjective optimization problem Pareto minimal point S-derivative Set-valued map

来源：评论

学校读者我要写书评

暂无评论

MOEA/D with NBI-style Tchebycheff approach for Portfolio Management

MOEA/D with NBI-style Tchebycheff approach for Portfolio Man...

引用

2010 IEEE World Congress on Computational Intelligence

作者： Zhang, Qingfu Li, Hui Maringer, Dietmar Tsang, Edward Univ Essex Sch Comp Sci & Elect Engn Wivenhoe Pk Colchester CO4 3SQ Essex England Univ Nottingham Dept Comp Sci Nottingham NG81BB England Univ Basel Fac Econ & Management WWZ CH-4003 Basel Switzerland

ISBN: (纸本)9781424481262

MOEA/D is a generic multiobjective evolutionary optimization algorithm. MOEA/D needs a approach to decompose a multiobjective optimization problem into a number of single objective optimization problems. The commonlyused weighted sum approach and the Tchebycheff approach may not be able to handle disparately scaled objectives. This paper suggests a new decomposition approach, called NBI-style Tchebycheff approach, for MOEA/D to deal with such objectives. A portfolio management MOP has been used as an example to test the effectiveness of MOEA/D with NBI-style Tchebycheff approach.

关键词： MOEA/D NBI-style Tchebycheff approach evolutionary computation generic multiobjective evolutionary optimization algorithm investment multiobjective optimization problem optimisation portfolio management single objective optimization problem weighted sum approach

来源：评论

学校读者我要写书评

暂无评论

Multi-objective Mobile Agent-based Sensor Network Routing using MOEA/D

Multi-objective Mobile Agent-based Sensor Network Routing us...

引用

2010 IEEE World Congress on Computational Intelligence

作者： Konstantinidis, Andreas Charalambous, Christoforos Zhou, Aimin Zhang, Qingfu Frederick Univ Dept Comp Sci & Engn Nicosia Cyprus East China Normal Univ Dept Comp Sci & Technol Shanghai Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester Essex CO4 3SQ England

ISBN: (纸本)9781424481262

Mobile agents are often used in wireless sensor networks for distributed target detection with the goal of minimizing the transmission of non-critical data that negatively affects the performance of the network. A challenge is to find optimal mobile agent routes for minimizing the data path loss and the sensors energy consumption as well as maximizing the data accuracy. Existing approaches deal with the objectives individually, or by optimizing one and constraining the others or by combining them into a single objective. This often results in missing "good" tradeoff solutions. Only few approaches have tackled the Mobile Agent-based Distributed Sensor Network Routing problem as a multiobjective optimization problem (MOP) using conventional Multi-Objective Evolutionary Algorithms (MOEAs). It is well known that the incorporation of problem specific knowledge in MOEAs is a difficult task. In this paper, we propose a problem-specific MOEA based on Decomposition (MOEA/D) for optimizing the three objectives. Experimental studies have shown that the proposed problem-specific approach performs better than two conventional MOEAs in several WSN test instances.

关键词： MOEA/D WSN data communication data path loss minimization decomposition based MOEA distributed target detection energy consumption evolutionary computation mobile agent based sensor network mobile agents multiobjective evolutionary algorithm multiobjective optimization problem network routing network routing object detection wireless sensor network wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：