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multi-objective game theory optimization for balancing economic, social and ecological benefits in the Three Gorges Reservoir operation

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ENVIRONMENTAL RESEARCH LETTERS 2021年第8期16卷

作者： Yu, Yang Zhao, Rui Zhang, Jiahe Yang, Douqiang Zhou, Tianyu Southwest Jiaotong Univ Fac Geosci & Environm Engn Chengdu 611756 Peoples R China Hohai Univ Dept Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China

Reservoir operation is an important and effective measure for realizing optimal allocation of water resources. It can effectively alleviate regional scarcity of water resources, flood disasters and other social problems, and plays an important role in supporting sustainable strategic development of water resources. Coordinating the stakeholders is key to the smooth operation of a multifunctional reservoir. This research examines the competition among stakeholders of a multi-objective ecological reservoir operation aiming to provide for economic, social and ecological demands. A multi-objective game theory model (MOGM) specified 10-day water discharge to meet the triple water demands (power generation, socio-economic consumption and environment) for multi-purpose reservoir operation. The optimal operation of the Three Gorges Reservoir (TGR), with the ecological objective of providing comprehensive ecological flow demanded for some key ecological problems that may occur in the middle and lower reaches of the Yangtze River, was chosen as a case study. Discharged water calculated by the MOGM and a conventional multi-objective evolutionary algorithm/decomposition with a differential evolution operator was then allocated to different demands. The results illustrate the applicability and efficiency of the MOGM in balancing transboundary water conflicts in multi-objective reservoir operation that can provide guidance for the operation of the TGR.

关键词： ecological reservoir operation game theory multi-objective evolutionary algorithm transboundary water conflicts water allocation

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NON-DOMINATED SORTING METHODS FOR multi-objective OPTIMIZATION: REVIEW AND NUMERICAL COMPARISON

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2021年第2期17卷 1001-1023页

作者： Long, Qiang Wu, Xue Wu, Changzhi Southwest Univ Sci & Technol Sch Sci Mianyang 621010 Sichuan Peoples R China Guangzhou Univ Sch Management Guangzhou 510006 Peoples R China

In multi-objective evolutionary algorithms (MOEAs), non-dominated sorting is one of the critical steps to locate efficient solutions. A large percentage of computational cost of MOEAs is on non-dominated sorting for it involves numerous comparisons. By now, there are more than ten different non-dominated sorting algorithms, but their numerical performance comparing with each other is not clear yet. It is necessary to investigate the advantage and disadvantage of these algorithms and consequently give suggestions to specific users and algorithm designers. Therefore, a comprehensively numerical study of non-dominated sorting algorithms is presented in this paper. Firstly, we design a population generator. This generator can generate populations with specific features, such as population size, number of Pareto fronts and number of points in each Pareto front. Then non-dominated sorting algorithms were tested using populations generated in certain structures, and results were compared with respect to number of comparisons and time consumption. Furthermore, In order to compare the performance of sorting algorithms in MOEAs, we embed them into a specific MOEA, dynamic sorting genetic algorithm (DSGA), and use these variations of DSGA to solve some multi-objective benchmarks. Results show that dominance degree sorting outperforms the other methods, fast non-dominance sorting performs the worst and the other sorting algorithms performs equally.

关键词： multi-objective optimization non-dominated sorting pareto front multi-objective evolutionary algorithm

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Solving dynamic overlapping community detection problem by a multiobjective evolutionary algorithm based on decomposition

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SWARM AND evolutionary COMPUTATION 2020年 54卷 100668-000页

作者： Wan, Xing Zuo, Xingquan Song, Feng Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China Minist Educ Key Lab Trustworthy Distributed Comp & Serv Beijing Peoples R China

Dynamic and overlapping are two common features of community structures for many real world complex networks. Although there are few studies on detecting dynamic overlapping communities, all those studies only consider a single optimization objective. In practice, it is necessary to evaluate the community detection by multiple metrics to reflect different aspects of a community structure and those metrics may conflict with each other. In this paper, we propose a multi-objective approach based on decomposition for the problem of dynamic overlapping community detection, with consideration of three optimization objectives: partition density (D), extended modularity (EQ), and improved mutual information (NMILFK). The dynamic overlapping network can be regarded as a set of network snapshots. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) is used to detect the communities for each snapshot. To improve the search efficiency, the dynamic optimization technique and a dynamic resource allocation strategy are introduced into the approach. Experiments show that our approach can find uniformly distributed Pareto solutions for the problem and outperforms those comparative approaches.

关键词： multi-objective evolutionary algorithm Overlapping community detection Dynamic community detection Dynamic optimization

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multi-objective evolving long-short term memory networks with attention for network intrusion detection

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APPLIED SOFT COMPUTING 2023年 139卷

作者： Wei, Wenhong Chen, Yi Lin, Qiuzhen Ji, Junkai Wong, Ka-Chun Li, Jianqiang Dongguan Univ Technol Sch Comp Sci & Technol Dongguan Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Cyber security has received increasing attention, as people use more Internet applications in their lives and worry about the security of their personal data on the Internet. Intrusion Detection Systems (IDSs) are critical security tools that can detect and respond to intrusions. In recent years, Deep Learning (DL) techniques have gained popularity in IDS design due to their promising performance in terms of detection accuracy. However, the design of DL architectures usually requires professional knowledge and significantly impacts the performance of the DL model. Furthermore, the existence of a small ratio of abnormal traffic in vast network traffic leads to a serious imbalanced data problem, which negatively affects the performance of the DL model in detecting minority attack classes. To alleviate these problems, this paper proposes a multi-objective evolutionary DL model (called EvoBMF) to detect network intrusion behaviors. The model incorporates bidirectional Long-short Term Memory (BiLSTM) for preliminary feature extraction, multi-Head Attention (MHA) for further capturing features and global information of the network traffic, and Full-Connected Layer (FCL) module to perform final classification. To deal with the challenge of manually tuning the parameters of the DL model when tackling different tasks, the parameters of the EvoBMF model are first encoded as the chromosome of the multi-objective evolutionary algorithm (MOEA), which aims to optimize the two conflicting objectives (complexity and classification ability) of the model. A state-of-the-art MOEA (MOEA/D-DRA) is then used to optimize the above two objectives, aiming to obtain the optimal architecture for EvoBMF, which can be easily deployed in cloud computing scenarios to detect and respond to network intrusions. Additionally, to alleviate the severe imbalance in routine network traffic, the synthetic minority over-sampling technique is introduced to generate representative samples of minority classes

关键词： Intrusion detection system Neural architecture search multi-objective evolutionary algorithm

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multi-objective dynamic distribution adaptation with instance reweighting for transfer feature learning

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KNOWLEDGE-BASED SYSTEMS 2023年第1期263卷

作者： Li, Haoran He, Fazhi Pan, Yiteng Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China

In knowledge adaptation, the source and target knowledge are transferred into the same mapping space by simultaneously reducing the difference between the marginal and conditional distributions;however, it is difficult to precisely balance the two distributions at each transformation. To address this problem, a novel multi-objective dynamic distribution adaptation (MODDA) with instance reweighting is proposed to reduce discrepancies between the two distributions. In addition, a customised non-dominated sorting genetic algorithm-II (NSGA2) optimisation method is presented for searching the optimal cumulative weight path, and four genetic operator combinations are compared to determine which one is optimal for MODDA. Moreover, kernel mean matching is proposed for the first time for dynamic compensation based on an individual's relevance in instance reweighting. The experimental results confirm that MODDA outperforms other state-of-the-art algorithms in terms of the classification accuracy for 16 well-known cross-domain tasks.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Domain adaptation multi-objective evolutionary algorithm Instance reweighting

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High-dimensional interactive adaptive RVEA for multi-objective optimization of polyester polymerization process

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INFORMATION SCIENCES 2023年 650卷

作者： Zhu, Xiuli Jiang, Chunli Hao, Kuangrong Wang, Rui Univ Shanghai Sci & Technol Coll Opt Elect & Comp Engn Shanghai 200093 Peoples R China Chongqing Univ Posts & Telecommun Coll Software Engn Chongqing 400065 Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai Peoples R China

The optimization of operating conditions in the polyester polymerization process is crucial for enhancing the quality of the resulting polyester. A novel multi-objective optimization algorithm, named the Interaction Adaptive RVEA (IARVEA), is presented and designed specifically to address the four-objective optimization problem in the polymerization esterification process in this paper. Three strategies are employed to guide the search for optimal solutions and approach the unknown Pareto fronts associated with this problem. Firstly, the maximum mean discrepancy distance is utilized to compare the distribution of the upper and lower solutions, subsequently determining whether an update to the reference vector is necessary. Secondly, the interaction adaptive strategy generates a reference vector based on interactive information from the current and the preceding iteration. Lastly, the projector distance is incorporated into the environment selection operator of IARVEA, which aids in achieving superior convergence properties and greater solution diversity when compared to the conventional RVEA method. Additionally, a comparative analysis is conducted between IARVEA and four other algorithms using seven benchmark test problems and the four-objective optimization problem in the polyester polymerization esterification process. The experimental results demonstrate the superiority of IARVEA over other algorithms in terms of both IGD and HV metrics. Meanwhile, the research findings have important implications for the polyester industry and related fields, offering valuable insights and guidelines for optimizing the operating conditions of the polymerization esterification process.

关键词： multi-objective evolutionary algorithm Polyester Polymerization esterification process RVEA

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PyBrOpS: a Python package for breeding program simulation and optimization for multi-objective breeding

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G3-GENES GENOMES GENETICS 2024年第10期14卷 jkae199页

作者： Shrote, Robert Z. Thompson, Addie M. Michigan State Univ Dept Plant Soil & Microbial Sci 1066 Bogue St E Lansing MI 48824 USA Michigan State Univ Plant Resilience Inst E Lansing MI 48824 USA

Plant breeding is a complex endeavor that is almost always multi-objective in nature. In recent years, stochastic breeding simulations have been used by breeders to assess the merits of alternative breeding strategies and assist in decision-making. In addition to simulations, visualization of a Pareto frontier for multiple competing breeding objectives can assist breeders in decision-making. This paper introduces Python Breeding Optimizer and Simulator (PyBrOpS), a Python package capable of performing multi-objective optimization of breeding objectives and stochastic simulations of breeding pipelines. PyBrOpS is unique among other simulation platforms in that it can perform multi-objective optimizations and incorporate these results into breeding simulations. PyBrOpS is built to be highly modular and has a script-based philosophy, making it highly extensible and customizable. In this paper, we describe some of the main features of PyBrOpS and demonstrate its ability to map Pareto frontiers for breeding possibilities and perform multi-objective selection in a simulated breeding pipeline.

关键词： stochastic simulation Python breeding programs multi-objective optimization Pareto frontier multi-objective evolutionary algorithm

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Optimal capacity allocation of wind-light-water multi-energy complementary capacity based on improved multi-objective optimization algorithm

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FRONTIERS IN ENERGY RESEARCH 2023年 10卷

作者： Wang, Ying Liu, Jiajun Xian Univ Technol Sch Elect Engn Xian Peoples R China Yinchuan Univ Energy Sch Elect Power Yinchuan Peoples R China

As a basic industry for national economic development, the power industry is closely related to the overall economic and environmental development of China. At present, China is still dominated by thermal power generation. In order to reduce carbon emissions, promote the realization of the "double carbon " goal, and improve the level of clean energy utilization and the operating efficiency of the power system, a wind-light-water storage complementary power generation system is built, and a mathematical model of multi energy complementation is established. The minimum economic cost and the minimum battery capacity are proposed as the objective functions of system capacity configuration. Then a multi-objective evolutionary algorithm based on Pareto optimal space of the NDWA-GA and the PCA is proposed for optimal capacity allocation of multi energy complementary systems in this paper. Compared with the traditional multi-objective optimization algorithm, the correctness and effectiveness of the proposed method are verified. In addition, according to the actual research object, the optimal capacity configuration of the multi energy complementary system is given, which can guide the production and has an important promotion significance for energy saving and emission reduction.

关键词： multi energy complementary model multi-objective evolutionary algorithm capacity configuration conserve energy reduce emissions production guidance

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Space division and adaptive selection strategy based differential evolution algorithm for multi-objective satellite range scheduling problem

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SWARM AND evolutionary COMPUTATION 2023年 83卷

作者： Wang, Tianyu Luo, Qizhang Zhou, Ling Wu, Guohua Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China

Satellite range scheduling always plays a crucial role in tracking, telemetry, and control of the spacecraft. With the significant increase in the number and type of satellites in orbit, the demands of users for satellite range scheduling are becoming more and more diverse. However, existing studies for the satellite range scheduling problem (SRSP) rarely consider multiple optimization objectives, which is quite significant in practice. Hence, this paper investigates a multi-objective satellite range scheduling problem (MO-SRSP) that optimizes three objectives simultaneously: the overall profits of tasks, load-balance of antennas, and completion timeliness of tasks. To address MO-SRSP, this paper establishes a mathematical model on the basis of analysis of MO-SRSP and proposes a multi-objective evolutionary algorithm, which is called multi-objective differential evolution algorithm based on space division and adaptive selection strategy (MODE-SDAS). The space division strategy will uniformly divide the objective space into a set of subspaces and preserve a set of non-dominated solutions in each subspace during the environmental selection even if some of these solutions are dominated by other solutions in other subspaces, so as to maintain the diversity of the algorithm. The adaptive selection strategy will adaptively allocate computational resources to different subspaces to improve the convergence of the population. Besides, problem-specific designs such as coding and encoding methods, the discrete differential evolution operator, as well as objective-specific individual variation operators are incorporated into the algorithm to enhance the search capability. Finally, extensive experiments based on simulation test cases are carried out to verify the effectiveness and efficiency of MODE-SDAS. The comparison results show that MODE-SDAS significantly outperforms its competitors in terms of three metrics. Meanwhile, knee point analysis, sensitivity analysis, and applic

关键词： Satellite range scheduling problem multi-objective evolutionary algorithm Differential evolution Space division strategy Adaptive strategy

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Digital twin-driven dynamic scheduling for the assembly workshop of complex products with workers allocation

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2024年 89卷

作者： Gao, Qinglin Liu, Jianhua Li, Huiting Zhuang, Cunbo Liu, Ziwen Beijing Inst Technol Sch Mech Engn Lab Digital Mfg Beijing 100081 Peoples R China Beijing Inst Technol Tangshan Res Inst Tangshan 063015 Peoples R China Beijing Design Inst Electromech Engn Beijing 100039 Peoples R China

Assembly processes for complex products primarily involve manual assembly and often encounter various disruptive events, such as the insertion of new orders, order cancellations, task adjustments, workers absences, and job rotations. The dynamic scheduling problem for complex product assembly workshops requires consideration of trigger events and time nodes for rescheduling, as well as the allocations of multi-skilled and multilevel workers. The application of digital twin technology in smart manufacturing enables managers to more effectively monitor and control disruptive events and production factors on the production site. Therefore, a dynamic scheduling strategy based on digital twin technology is proposed to enable real-time monitoring of dynamic events in the assembly workshop, triggering rescheduling when necessary, adjusting task processing sequences and team composition accordingly, and establishing a corresponding dynamic scheduling integer programming model. Additionally, based on NSGA-II, an improved multi-objective evolutionary algorithm (IMOEA) is proposed, which utilizes the maximum completion time as the production efficiency indicator and the time deviation before and after rescheduling as the production stability indicator. Three new population initialization rules are designed, and the optimal parameter combination for these rules is determined. Finally, the effectiveness of the scheduling strategy is verified through the construction of a workshop digital twin system.

关键词： Dynamic scheduling Digital twin multi-objective evolutionary algorithm Hybrid flow shop Assembly workshop of complex products

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