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multi-objective dynamic optimization of path-constrained switched systems

AUTOMATICA 2024年 159卷

作者： Zhang, Chi Fu, Jun Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

A multi-objective dynamic optimization algorithm is proposed for path-constrained switched systems to locate approximated local Pareto solutions with specified tolerances. The algorithm is based on iteratively solving single-objective modified epsilon-constraint dynamic optimization problems to avoid generating weak Pareto solutions. These problems are first reduced to semi-infinite programs embedded with ordinary differential equations by performing the control vector parameterization technique. Then the guaranteed feasibility is attained within a finite number of iterations by enforcing the path constraints at discretized time points with a restriction parameter on the right-hand side. Furthermore, the approximation error of the obtained local Pareto solutions is quantitatively analyzed. The result shows that the Pareto approximation error can be directly controlled via user-specified parameters. Finally, the effectiveness of the algorithm is illustrated via a numerical example. (c) 2023 Elsevier Ltd. All rights reserved.

关键词： Switched systems Semi-infinite programs epsilon-constraint method multi-objective dynamic optimization

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Research on multi-objective Parameter dynamic optimization Design of HEV Considering Mode Transition

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MECHANIKA 2022年第1期28卷 45-57页

作者： Huang, Kang Tang, Zhiyu Qiu, Mingming Wu, Di Zhang, Bingzhan Hefei Univ Technol Sch Mech Engn Hefei 230009 Peoples R China Natl & Local Joint Engn Res Ctr Automot Technol & Hefei 230009 Peoples R China Hefei Univ Technol Sch Automot & Traff Engn Hefei 230009 Peoples R China

The parameter optimization of hybrid electric vehicle is very complex and has always been the focus of research, due to the highly intertwined relationship between powertrain configurations and EMS. In addition, automated powertrain is typical cyber-physics system that contains discrete states, such as modes and gears. Transition between discrete states is a contributing factor that causes discomfort. Therefore, in order to solve the above problems, this paper proposes a multi-objective dynamic optimization algorithm considering mode transition. The algorithm consists of two layers. The first layer optimizes fuel economy and dynamic performance, the second layer optimizes ride comfort and smoothness performance, and the results of the second layer are returned to the first layer for re-optimization in real time. The mode transition frequency is used as the comfort objective function, and the threshold value of the engine high efficiency working area is added to the optimization parameter selection. The results show that the optimization method proposed in this paper realizes the parameter decoupling, and the optimization results achieve the global optimization. Effectively improves the vehicle performance and reduces engine start-stop frequently.

关键词： multi-objective dynamic optimization decouple mode transition ride comfort fuel economy

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Meta-learning-based multi-objective PSO model for dynamic scheduling optimization

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ENERGY REPORTS 2023年 9卷 1227-1236页

作者： Lv, Zheng Liao, Zherun Liu, Ying Zhao, Jun Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equipm Minist Educ Dalian 116024 Peoples R China

The by-product gas is an important secondary energy in the iron and steel industry. It is important to make the by-product gas's utilization efficient and reasonable,which is the key to improve the economic efficiency and the level of energy conservation and emission reduction. Aiming at the problems of complex dynamic changes, difficult to accurately model and difficult to predict real-time traffic in gas energy system, this paper proposes an optimal scheduling method based on meta-learning multi-objective particle swarm optimization algorithm. The gas energy optimization scheduling problem is modeled as a multi-objective dynamic scheduling optimization problem. The difficulty of this problem is to comprehensively consider multiple indicators, continuously adapt to the change of the objective function over time, and track the changing Pareto optimal solution set. One of the promising solutions is to build prediction models with better performance under dynamic changes. The model we propose makes full use of the optimization results and the experience information of the optimization process in the historical optimization task. We propose a dynamic parameter initialization method based on meta-learning by starting multiple historical optimization tasks at the same time. Thus,the comprehensive parameters of different tasks are obtained, and a general and best base parameter is learned. Experiments show that the proposed model has better convergence and accuracy than the conventional algorithm. (c) 2023 Published by Elsevier Ltd. This is an open access article under theCCBY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Energy scheduling Meta-learning multi-objective dynamic optimization Pre-knowledge learning

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Fuzzy portfolio optimization for time-inconsistent investors: a multi-objective dynamic approach

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SOFT COMPUTING 2020年第13期24卷 9927-9941页

作者： Li, You Wang, Bo Fu, Anrui Watada, Junzo Nanjing Univ Finance & Econ Sch Finance Nanjing 210046 Jiangsu Peoples R China Nanjing Univ Sch Management & Engn Nanjing 210093 Jiangsu Peoples R China Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan

In recent years, fuzzy optimization has been widely adopted to handle the nonstatistical uncertainties in portfolio selection. Meanwhile, various risk measurements, including variance, entropy and value at risk, have been introduced in fuzzy environments to evaluate portfolio risks from different perspectives. In this study, we discuss fuzzy multi-objective dynamic portfolio optimization for time-inconsistent investors. When building the model, variance and value at risk as the representatives of different types of risk measurements are employed together with the expected return. And a dynamic investment policy is developed for time-inconsistent investors, which combines the expected return and value at risk into one objective. Then, the model is established to maximize the cumulative combined objective function and minimize the cumulative portfolio variance simultaneously. In addition, a multi-objective dynamic evolutionary algorithm is designed as a possible solution of the proposed model. The effectiveness of this research is demonstrated by using a real market data-based case study. Experimental results demonstrate that the proposed model matches the practical behavior of time-inconsistent investors and the solution algorithm is feasible to solve the complicated nonlinear problem.

关键词： multi-objective dynamic optimization Portfolio selection dynamic investment policy Fuzzy set theory Particle swarm optimization

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multi-objective dynamic Reactive Power optimization Based on OLTC and Reactive Power Compensation 4

Multi-objective Dynamic Reactive Power Optimization Based on...

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4th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Cai, Xiang Huang, Qingjun Zhou, Xiudong Zhu, Yuan Sun, Shiyi Zhu, Junwei State Key Lab Disaster Prevent & Reduct Power Gri Changsha Peoples R China

ISBN: (纸本)9781665479141

Reactive power optimization is to minimize the active power losses of the distribution network by coordinating on-load tap-changing(OLTC) transformer and reactive power compensation(RPC) equipment, such as capacitor banks(CB) and static var compensator(SVC), etc.. Aside from power losses, minimizing voltage deviation is also an important aspect of reactive power optimization. Traditional reactive power optimization only considers the optimization effect of a single period, not the overall day's optimization. In view of this, a multi-objective dynamic reactive power optimization model is established in this paper, with the objective of minimizing the active power losses and voltage deviation, and the constraints of OLTC and RPC equipment. The second-order cone relaxation (SOCR) is used to convert the mixed integer nonlinear programming (MINLP) problem into the mixed integer second-order cone programming (MISOCP) problem, and the multi-objective variable-weight method is adapted to achieve the multi-objective dynamic reactive power optimization. Case studies are carried out on the modified IEEE 33-node system to verify the feasibility and effectiveness of the proposed method.

关键词： distribution network multi-objective dynamic optimization second order cone relaxation

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A novel algorithm for solving optimal path planning problems based on parametrization method and fuzzy aggregation

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PHYSICS LETTERS A 2009年第38期373卷 3439-3449页

作者： Zamirian, M. Kamyad, A. V. Farahi, M. H. Ferdowsi Univ Mashhad Dept Math Mashhad 917751159 Iran

In this Letter a new approach for solving optimal path planning problems for a single rigid and free moving object in a two and three dimensional space in the presence of stationary or moving obstacles is presented. In this approach the path planning problems have some incompatible objectives such as the length of path that must be minimized, the distance between the path and obstacles that must be maximized and etc., then a multi-objective dynamic optimization problem (MODOP) is achieved. Considering the imprecise nature of decision maker's (DM) judgment, these multiple objectives are viewed as fuzzy variables. By determining intervals for the values of these fuzzy variables, flexible monotonic decreasing or increasing membership functions are determined as the degrees of satisfaction of these fuzzy variables on their intervals. Then, the optimal path planning policy is searched by maximizing the aggregated fuzzy decision values, resulting in a fuzzy multi-objective dynamic optimization problem (FMODOP). Using a suitable t-norm, the FMODOP is converted into a non-linear dynamic optimization problem (NLDOP). By using parametrization method and some calculations, the NLDOP is converted into the sequence of conventional non-linear programming problems (NLPP). It is proved that the solution of this sequence of the NLPPs tends to a Pareto optimal solution which, among other Pareto optimal solutions, has the best satisfaction of DM for the MODOP. Finally. the above procedure as a novel algorithm integrating parametrization method and fuzzy aggregation to solve the MODOP is proposed. Efficiency of our approach is confirmed by some numerical examples. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Optimal path planning multi-objective dynamic optimization Fuzzy aggregation Parametrization method Non-linear programming Membership function

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Assessing the performance potential of climate adaptive greenhouse shells

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ENERGY 2019年 175卷 534-545页

作者： Lee, Chul-sung Hoes, P. Costola, D. Hensen, J. L. M. Hanbat Natl Univ Dept Architectural Engn Deajeon South Korea Eindhoven Univ Technol Dept Built Environm Eindhoven Netherlands Univ Strathclyde Dept Mech Aerosp Engn Glasgow Lanark Scotland

Agriculture is responsible for 7.2% of the final energy consumption in the Netherlands;most energy is used for heating and lighting in the greenhouse sector. Currently, the greenhouse sector faces major challenges in reducing its energy demand while increasing crop quality and quantity. One route to improve the performance of industrial greenhouses could be based on using climate adaptive shells. These shells are capable of changing their thermal and optical properties on an hourly, daily, or seasonal basis to optimize performance. The climate adaptive shell concept shows considerable potential for performance improvement in the building sector. However, its potential for the greenhouse sector is yet unknown. This paper quantifies this potential by predicting the energy savings and the increase in net profit using a new framework based on numerical simulation and optimization techniques. The simulation results show that climate adaptive greenhouse shells increase net profit between 7% and 20% for tomato producing Dutch greenhouses. Monthly and hourly adaptation resulted in considerable primary energy savings of 23% and 37%, respectively. It is expected that the predicted net profit increase and energy savings will drive the attention of the greenhouse industry towards the development of climate adaptive greenhouse shells. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Climate adaptive greenhouse shells multi-objective dynamic optimization Adaptation period Greenhouse performance simulation

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A study of multi-objective dynamic water resources allocation modeling of Huai River

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WATER SCIENCE AND TECHNOLOGY-WATER SUPPLY 2015年第4期15卷 817-824页

作者： Peng, Jing Yuan, Ximin Qi, Lan Li, Qiliang Civil Aviat Univ China Airport Coll Tianjin 300300 Peoples R China Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin 300072 Peoples R China

Water resources supply and demand has become a serious problem. Water resources allocation is usually a multi-objective problem, and has been of concern for many researchers. In the north of China, the lack of water resources in the Huai River Basin has handicapped the development of the economy, especially badly in the low-flow period. So it is necessary to study water resources allocation in this area. In this paper, a multi-objective dynamic water resources allocation model has been developed. The developed model took the overall satisfaction of water users in a time interval as the objective function, applied an improved simplex method to solve the calculation, considered the overall users' satisfaction variation with time, and followed the principle that the variation of the system satisfaction within adjacent periods of time must be minimal. The established model was then applied to the Huai River, for the present situation (2010), short-term (2020) and long-term (2030) planning timeframes. From the calculation results, the overall satisfaction in late May and mid September in 2030 was 0.65 and 0.70. After using the model allocation optimization, the overall satisfaction was improved, increasing to 0.78 and 0.79, respectively, thus achieving the dynamic balance optimization of water resources allocation in time and space. This model can provide useful decision support in water resources allocation, when it is used to alleviate water shortages occurring in the low-flow period.

关键词： Huai River modified simplex method multi-objective dynamic optimization water resources allocation

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An Integrated Generation-Compensation optimization Strategy for Enhanced Short-Term Voltage Security of Large-Scale Power Systems Using multi-objective Reinforcement Learning Method

An Integrated Generation-Compensation Optimization Strategy ...

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International Conference on Power System Technology (POWERCON)

作者： Deng, Zhuoming Liu, Mingbo South China Univ Technol Sch Elect Power Engn Guangzhou 510640 Guangdong Peoples R China

ISBN: (纸本)9781538664612

High penetrations of industrial loads have placed significant pressures on short-term voltage security. This paper proposes an integrated generation-compensation optimization strategy, which coordinates the generators and the switchable capacitor banks to enhance short-term voltage security as a multi-objective dynamic optimization (MODO) model. This model is established containing dynamics, power flow balances, and security constraints to minimize the voltage deviation and the cost of control strategy. The differential-algebra equations are converted into algebra equations using the Radau collocation method. Furthermore, a novel multi-objective reinforcement learning (MORL) method is utilized to mitigate the computational burdens and to obtain Pareto optimal solutions by filtering the dominated solutions. Compared with conventional MORL methods, the proposed MORL method divides the full feasible region into several small independent regions to reduce and eliminate the searching for Pareto optimal solutions. Meanwhile, the state functions of MORL are redefined, and the state sensitivities are introduced to judge whether the trial and learning accumulate sufficient knowledge. Moreover, the Pareto optimal solutions are further improved by introducing several possible solutions. Finally, the tradeoff solution is obtained based on Fuzzy decision-making strategy. The effectiveness and efficiency of the MORL method are verified by numerical simulations on a provincial 748-bus power system.

关键词： multi-objective dynamic optimization multi-objective reinforcement learning Pareto optimal solution Radau collocation method short-term voltage security

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Design and Development on a Reconfigurable Modular Joint of the Robot Arm

Design and Development on a Reconfigurable Modular Joint of ...

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3rd IEEE/IFToMM/ASME International Conference on Reconfigurable Mechanisms and Robots (ReMAR)

作者： Deng, Jiaming Wang, Pengcheng Meng, Qingmei Shen, Huiping Chen, Jian Luo, Minzhou Changzhou Univ Sch Mech Engn 1 Gehu Rd Changzhou City Jiangsu Peoples R China Changzhou Inst Adv Mfg Technol Changzhou City Jiangsu Peoples R China

ISBN: (纸本)9783319233277;9783319233260

According to the mathematical model of the mechatronics integration of the flexible robot joint, a modular joint structure is proposed. The factors, which influence the stability of the whole modular mechanical and electrical system bandwidth, are analyzed by force/position hybrid control strategy. Furthermore, these analyses provide the theory basis for reducing the vibrating and motion error. Based on the results of the analysis, multi-objective dynamic optimization algorithm is used to obtain the set of optimal mechanical and controller parameters for the modular joint system, and the key parts are the preferred selection. Finally, the experiment about the precision of modular joints showed that the modular joint can achieve the desired accuracy and provided theoretical basis and design reference for the design of modular joints to performance better.

关键词： Modular joint multi-objective dynamic optimization Resonance frequency Motion error

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