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18th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI)

作者： Liu, Wei Li, Kaiwen Li, Wenhua Wang, Rui Zhang, Tao Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China HKL MSI2T Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Peoples R China

ISBN: (纸本)9798350329537;9798350329520

The multi-modal orienteering problem (MM-OP) is common in real life and is to plan diverse tours that can visit as many nodes as possible with limited resources. Researchers used to adopt group intelligence algorithms such as evolutionary algorithms to solve MM-OP, however, show difficulty in designing complex operators and dealing with large-scale cases in real-time. This study first proposes a multi-agent deep reinforcement learning (MA-DRL) method for MM-OP. Our model deploys multiple agents, each trained to independently explore and exploit the solution space, thereby discovering diverse optimal paths. This diversity is critical in multi-modal optimization and is achieved through a unique reward-sharing mechanism implemented within the MA-DRL framework. Empirical evaluation of our model against traditional evolutionary algorithms on various test instances demonstrates its superior optimization accuracy and efficiency, particularly in larger problem sizes. Additionally, the robustness across different instance sizes underscores its great generalization ability.

关键词： Orienteering Problem Multi-modal Optimization Multi-agent Deep Reinforcement Learning

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Surrogated-assisted multimodal multi-objective optimization for hybrid renewable energy system

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COMPLEX & intelligent systEMS 2023年第4期9卷 4075-4087页

作者： Zhang, Tao Li, Wenhua Wang, Rui Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interco Changsha 410073 Peoples R China

Hybrid renewable energy system (HRES) is an effective tool to improve the utilization of renewable energy so as to enhance the quality of energy supply. The optimization of HRES includes a simulation process during a long time span, which is time-consuming. So far, introducing a surrogate model to replace the objective evaluation is an effective way to solve such problems. However, existing methods focused few on the diversity of solutions in the decision space. Based on this motivation, we proposed a novel surrogated-assisted multi-objective evolutionary algorithm that focuses on solving multimodal and time-expensive problems, termed SaMMEA. Specifically, we use a Gaussian process model to replace the calculation of the objective values. In addition, a special environmental selection strategy is proposed to enhance the diversity of solutions in the decision space and a model management method is proposed to better train the surrogate model. The proposed algorithm is then compared to several state-of-the-art algorithms on HRES problems, which indicates that the proposed algorithm is competitive.

关键词： Hybrid renewable energy system Surrogated-assisted Multi-objective optimization Evolutionary computation Diversity-preserving mechanisms

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An LSTM-Based Approach For Capacity Estimation on Lithium-ion Battery 33

An LSTM-Based Approach For Capacity Estimation on Lithium-io...

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33rd Chinese Control and Decision Conference (CCDC)

作者： Cao, Mengda Zhang, Yajun Hui, Jianjiang Liu, Yajie Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Hunan Peoples R China Beijing Inst Tracking & Telecommun Technol Beijing 100094 Peoples R China

ISBN: (纸本)9781665440899

With the wide application of lithium-ion batteries, the safety and stability of batteries have attracted much attention in recent years. Accurate capacity estimation can help understand the health status of lithium-ion batteries. Model-based methods can perform well on capacity estimation with enough prior knowledge. However, the internal electrochemical mechanism of lithium-ion battery degradation is so complicated that the cost of prior knowledge is usually expensive. Due to no need for prior knowledge, data-driven methods introduce new solutions to solve this problem. Among these, deep learning methods (DL) show great potential for their advantages of high-dimensional feature mapping. In this paper, an LSTM (Long Short-Term Memory) network model is proposed to estimate the capacity, in which seven time-related health features in the charge stage are used as input of the model. On the one hand, health features extracted from charge time data can decrease the difficulties in data acquisition. On the other hand, the LSTM model has the ability to learn the long-term dependencies between data so that suit for constructing mapping relationship between health features and capacity. The validation experiments are conducted based on two simulation datasets. The results show that the proposed model can accurately track the capacity recovery effect and estimate the RUL of lithium-ion batteries.

关键词： Capacity Estimation Lithium-ion Battery Deep Learning (DL) Long short-term memory (LSTM)

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A Novel Dual-Stage Dual-Population Evolutionary Algorithm for Constrained Multiobjective Optimization

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2022年第5期26卷 1129-1143页

作者： Ming, Mengjun Wang, Rui Ishibuchi, Hisao Zhang, Tao Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Natl Univ Def Technol Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Peoples R China Southern Univ Sci & Technol Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

In addition to the search for feasible solutions, the utilization of informative infeasible solutions is important for solving constrained multiobjective optimization problems (CMOPs). However, most of the existing constrained multiobjective evolutionary algorithms (CMOEAs) cannot effectively explore and exploit those solutions and, therefore, exhibit poor performance when facing problems with large infeasible regions. To address the issue, this article proposes a novel method, called DD-CMOEA, which features dual stages (i.e., exploration and exploitation) and dual populations. Specifically, the two populations, called mainPop and auxPop, first individually evolve with and without considering the constraints, responsible for exploring feasible and infeasible solutions, respectively. Then, in the exploitation stage, mainPop provides information about the location of feasible regions, which facilitates auxPop to find and exploit surrounding infeasible solutions. The promising infeasible solutions obtained by auxPop in turn help mainPop converge better toward the Pareto-optimal front. Extensive experiments on three well-known test suites and a real-world case study fully demonstrate that DD-CMOEA is more competitive than five state-of-the-art CMOEAs.

关键词： Statistics Sociology Optimization Search problems Evolutionary computation Convergence Switches Coevolution constrained multiobjective optimization problems (CMOPs) exploitation exploration

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Collaborative optimization of multi-energy multi-microgrid system: A hierarchical trust-region multi-agent reinforcement learning approach

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APPLIED ENERGY 2024年 375卷

作者： Xu, Xuesong Xu, Kai Zeng, Ziyang Tang, Jiale He, Yuanxing Shi, Guangze Zhang, Tao Hunan Univ Technol & Business Frontier Crossing Acad Changsha 410205 Hunan Peoples R China Xiangjiang Lab Changsha 410205 Hunan Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interco Changsha 410073 Peoples R China

In the context of the expanding diversity of energy demands, an increasing number of heterogeneous Multi- energy Microgrids (MEMGs) are engaging in the collaborative framework of the Multi-energy Multi-microgrid system (MEMMG). However, following this trend, the existing centralized Integrated Energy Management system (IEMS) control strategy is unreliable for future energy systems, characterized by more complex optimization control and a flexible system structure. This paper introduces a hierarchical Multi-agent Deep Reinforcement Learning (HMADRL) approach for distributed IEMS in MEMMG. Firstly, by employing a hierarchical approach, this method simplifies control complexity by segmenting the overarching control challenge into tasks of collaborative planning and action control, which are distributed across varied multi- agent scenes. Considering both macro and microeconomic factors, alongside carbon emissions, the optimal operation of MEMMG is realized through a bottom-up edge multi-agent control approach, in contrast to traditional top-down centralized methods. Secondly, in the phase of the inter-MEMG collaborative strategy, the Centralized Training Decentralized Execution (CTDE) framework is adopted to overcome the problems of unstable training environments and large-scale agent training, and each heterogeneous microgrid can develop local strategies independently with the assurance that their internal data will not be overly exposed. Thirdly, within each MEMG, the Trust-Region (TR) model is introduced for multi-agent action control, adeptly addressing the effects of mutual exclusion in decision-making time series. Simultaneously, an initialized Hot Experience Pool (HEP) is implemented, efficiently reducing exploration in complex, high-dimensional spaces. Finally, the off-time agent model is integrated into the HMADRL environment and undergoes secondary training based on real interactions, thereby deriving the optimal energy management policy. The proposed method m

关键词： Multi-microgrid system Integrated multi-energy network Collaboration optimization Flexible retraining mechanism Hierarchical multi-agent reinforcement learning

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A tri-level optimization model for power grid defense with the consideration of post-allocated DGs against coordinated cyber-physical attacks

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY systEMS 2021年 130卷 106903-106903页

作者： He, Huihui Huang, Shengjun Liu, Yajie Zhang, Tao Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Hunan Peoples R China

Due to the extensive integration of communication infrastructures, the power grid is vulnerable to a range of cyber-physical coordinated attacks. To increase the reliability of the power grid against coordinated attacks, a defensive strategy considering post-allocated Distributed Generators (DGs) is developed in this paper. The problem is formulated as a tri-level optimization model: the upper-level problem represents the action of the planner to determine the optimal plan for the defensive line and the pre-allocated DGs before attacks;the middlelevel problem formulates the behaviour of the attacker in identifying the targets;the lower-level problem simulates the reaction of the defender to optimize power flow and the placement of post-allocated DGs on the remaining microgrid system. Minimizing the system load shed is the objective of this tri-level optimization framework. Furthermore, the traditional Load Redistributed (LR) attack model is reformulated in a discrete form to facilitate the utilization of duality-based method in the tri-level model. The solution process is developed based on the Nested Column-and-Constraint Generation (NCCG) algorithm with the duality-based method. Case studies are conducted on the IEEE 14-bus system and the IEEE RTS79 system and they indicate that the tri-level model and algorithm are productive and promising. In addition, a sensitivity analysis is implemented on the parameters and settings.

关键词： Tri-level optimization model Power grid Post-allocated DGs Cyber-physical coordinated attacks Nested column-and-constraint generation algorithm

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Multiobjective routing optimization of mobile charging vehicles for UAV power supply guarantees

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 162卷 107714-107714页

作者： Qin, Weijian Shi, Zhichao Li, Wenhua Li, Kaiwen Zhang, Tao Wang, Rui Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Hunan Peoples R China

A novel power supply guarantee model based on mobile charging vehicles (MCVs) is developed for solving the battery capacity constraint problem of unmanned aerial vehicles (UAVs) in performing long-term and large-scale missions. In this model, the charging vehicles travel to charging points located under the routes of the UAVs to be charged wirelessly, maximizing the assurance of completing reconnaissance missions. This paper focuses on the scheduling and routing of charging vehicles and achieves multisystem cooperation by constructing two submodels, namely, a UAV reconnaissance routing model and an MCV charging routing model. Furthermore, an improved coevolutionary framework for constrained multiobjective optimization problems with a generalized opposition-based learning strategy (OL-CCMO) is proposed to optimize the two objectives of power supply guarantee investment cost and reconnaissance mission time window deviation. Three scenarios of UAV reconnaissance missions with different levels of complexity are built in the case studies, in which the charging vehicle routes are optimized with multiple algorithms, demonstrating the superiority and applicability of the proposed algorithm by comparison.

关键词： UAV power supply guarantee Mobile charging Routing Multiobjective optimization

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Multi-supply multi-capacitated p-median location optimization via a hybrid bi-level intelligent algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 160卷 107584-107584页

作者： Yang, Kang Wang, Rui He, Huihui Yang, Xu Zhang, Guo Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Hunan Peoples R China

The Capacitated P-Median Problem (CPMP) is a classic discrete facility location problem in which p capacitated facilities (medians) are selected from an economic point of view to serve a set of demand vertexes so that the total demand assigned to each of the facilities (medians) does not exceed its capacity. Since the capacity used by each facility is fixed and does not depend on the vertex demand, the traditional capacitated p median problem's total capacity is underutilized. To make full use of the total capacity, a more generalized model, namely, the Multi-supply Multi-Capacitated Location Problem (MMCLP), is considered in this paper, which assumes not only that each facility is allowed to open with different capacities while meeting the constraint of total capacity but also that each demand vertex can accept supplies from different facilities. To solve the MMCLP effectively, we propose a hybrid bi-level optimization framework based on decomposition. First, the original MMCLP is decomposed into numerous mixed-integer programming (MIP) sub-problems, i.e., inner optimization is performed, and for outer optimization, a Genetic Algorithm based on Co-Evolution of Population and Neighborhood (GACEPN) is proposed to find better sub-problems near the good sub-problems among all the candidate sub-problems. The inner optimization problem is solved by a commercial MIP solver. The results obtained are returned to the outer layer so that the neighborhood of the outer layer optimization can be adjusted appropriately to generate better sub-problems, thereby approaching the optimal solution of the original problem. The experiments show that for a large-scale MMCLP, compared to the Gurobi solver, the proposed bi-level algorithm (a hybrid of the GACEPN and Gurobi) can find a better solution in less time.

关键词： Facility location Decomposition Bi-level optimization Co-evolution Gurobi

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Wireless Energy Transmission Link Optimization considering Microwave Energy Relay

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COMPLEXITY 2020年第1期2020卷

作者： Song, Yuanming Liu, Yajie Yang, Xu Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Hunan Peoples R China

With the widespread application of intelligent unmanned units and the development of wireless energy transmission technology, it has become an urgent requirement in the field of using wireless power transmission to extend the continuous working time of unmanned units. To improve the wireless transmission energy efficiency in practical application as much as possible based on the current development level of wireless transmission technology, this paper first enumerates and analyzes existing wireless energy supply methods and shows that microwave wireless energy transmission technology is most suitable in this study. Then, the necessity of power transmission relay in long-distance microwave wireless power transmission is discussed. Based on the idea of microwave energy relay transmission, the paper constructs a microwave wireless transmission link planning model which considers power relay and uses the evolutionary algorithm to solve the wireless transmission link planning model with two-layer optimization. Finally, the results show that, when the wireless transmission distance exceeds a certain threshold, adding relay nodes to the link can improve the energy transmission efficiency from the power supply node to the power receiving node.

关键词： Inductive power transmission

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An ensemble learning prognostic method for capacity estimation of lithium-ion batteries based on the V-IOWGA operator

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ENERGY 2022年 257卷

作者： Cao, Mengda Zhang, Tao Liu, Yajie Zhang, Yajun Wang, Yu Li, Kaiwen Natl Univ Def Technol Coll Syst Engn Sanyi Rd Changsha 410073 Hunan Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Hunan Peoples R China

Capacity estimation is crucial for assessing the health statuses of lithium-ion batteries to develop better battery usage and maintenance strategies. However, it is difficult to satisfy multiple application situations in which each individual prognostic method has its own particular preconditions and application limitations. Thus, in this paper, an ensemble prognostic framework is proposed to integrate several individual prognostic methods to achieve better capacity estimation accuracy. In the proposed framework, a measurement-and calculation-based combined feature extraction method is first applied to the battery charging phase to better capture the extracted features that are related to the health statuses of lithium-ion batteries. Then, a novel validation dataset-based induced ordered weighted geometric averaging (V-IOWGA) operator is proposed to realize the time-varying weight allocation of each individual prognostic method to solve issue by which the performances of different prognostic algorithms vary in different phases. The advantages of the proposed ensemble model are verified on lithium-ion battery datasets from NASA PCoE and the University of Maryland CALCE laboratory, and its prediction accuracy is better than that of other individual prognostic models. In addition, comparison experiments involving three types of ensemble models validate the superiority of the proposed model. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Ensemble learning Lithium-ion battery Capacity estimation Incremental capacity analysis (ICA) Induced ordered weighted geometric averaging (IOWGA) operator

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