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multi-objective Synergetic Operation for Cascade Reservoirs in the Upper Yellow River

WATER 2024年第10期16卷 1416页

作者： Hong, Kunhui Zhang, Wei Ma, Aixing Wei, Yucong Cao, Mingxiong Hohai Univ Coll Harbour Coastal & Offshore Engn Nanjing 210098 Peoples R China Nanjing Hydraul Res Inst Nanjing 210029 Peoples R China Minist Commun Key Lab Port Waterway & Sedimentat Engn Nanjing 210029 Peoples R China

The Yellow River, a critical water resource, faces challenges stemming from increasing water demand, which has led to detrimental effects on hydropower generation and ecological balance. This paper will address the complex task of balancing the interests of hydropower generation, water supply, and ecology within the context of cascade reservoirs, specifically Longyangxia and Liujiaxia reservoirs. Employing a systemic coupling coordination approach, we constructed a multi-objective synergetic model of the upper Yellow River in order to explore synergies and competitions among multiple objectives. The results reveal that there is a weak competitive relationship between hydropower generation and water supply, a strong synergy between hydropower generation and ecology, and a strong competitive relationship between water supply and ecology. The Pareto solution set analysis indicates a considerable percentage (59%, 20%, and 8% in wet, normal, and dry years, respectively) exhibiting excellent coordination. The probability of excellent coordination decreases with diminishing inflow. The optimization scheme with the highest coupling coordination demonstrates significant improvements in power generation, water supply, and ecological benefits in the upper Yellow River without compromising other objectives, fostering the sustainable operation of hydropower generation, water supply, and ecology in the upper Yellow River.

关键词： Yellow River Basin multi-objective evolutionary algorithm cascade reservoirs coupling

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A comparative study of pre-screening strategies within a surrogate-assisted multi-objective algorithm framework for computationally expensive problems

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NEURAL COMPUTING & APPLICATIONS 2021年第9期33卷 4387-4416页

作者： Li, Fan Gao, Liang Garg, Akhil Shen, Weiming Huang, Shifeng Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Natl CNC Engn Technol Res Ctr Sch Mech Sci & Engn Wuhan 430074 Peoples R China

The multi-offspring method has been recognized as an efficient approach to enhance the performance of multi-objective evolutionary algorithms. However, some pre-screening strategies should be used when a multi-offspring-assisted multi-objective evolutionary algorithm is used to solve computationally expensive problems. So far, there is no any reported comprehensive study that compares the effects of different pre-screening strategies on the performance of the multi-offspring-assisted multi-objective evolutionary algorithms. In this paper, four pre-screening strategies (convergence-based, maximin distance-based expected improvement matrix (EIM-based), diversity-based and random-based strategies) for the multi-offspring-assisted multi-objective evolutionary algorithm are compared. The convergence-based strategy gives more priority to non-dominated solutions, and it is vital for exploiting the current promising areas. The diversity-based strategy gives more priority to solutions with greater uncertainties, and it is important for exploring the sparse areas. The EIM-based strategy considers the exploration and exploitation simultaneously, and the random-based strategy gives no priority to any solution. A series of benchmark problems whose dimensions vary from 8 to 30 and a reactive power optimization problem are used to test the multi-offspring-assisted multi-objective evolutionary algorithm under the four pre-screening strategies. The experimental results show that the convergence-based strategy performs best on most of the simple problems, while the EIM-based strategy performs best on most of the complex problems. The diversity-based strategy can produce positive effects on some problems, while the random-based strategy cannot improve the performance of its basic algorithm.

关键词： Computationally expensive problem multi-objective evolutionary algorithm Pre-screening strategy Surrogate model multi-offspring method

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Dynamic Resource Scheduling for C2 Organizations Based on multi-objective Optimization

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IEEE ACCESS 2019年 7卷 64614-64626页

作者： Xun Wang Yao, Peiyang Zhang, Jieyong Wan Lujun Jiao, Zhiqiang Air Force Engn Univ Informat & Nav Coll Xian 710077 Shaanxi Peoples R China Air Force Engn Univ Air Traff Control & Nav Coll Xian 710051 Shaanxi Peoples R China

The dynamic resource scheduling problem is a field of intense research in command and control organization mission planning. This paper analyzes the emergencies in the battlefield first and divides them into three categories: the changing of task attributes, reduction of available platforms, and change in the number of tasks. To deal with these emergencies, in this paper, we built a series of multi-objective optimization models that maximizes the task execution quality and minimizes the cost of plan adjustment. To solve the model, we proposed an improved multi-objective evolutionary algorithm. A type of mapping operator and an improved crowding-distance sorting method are designed for the algorithm. Finally, the availability of the model and the solving algorithm were proved through a series of experiments. The Pareto frontier for the multi-objective dynamic resource scheduling problem can be found effectively, and the algorithm proposed in this paper shows better convergence compared with the AMP-NSGA-II algorithm.

关键词： Command and control organization dynamic resource scheduling multi-objective evolutionary algorithm multi-objective optimization

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A Gradient-Based Search Method for multi-objective Optimization Problems

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INFORMATION SCIENCES 2021年 578卷 129-146页

作者： Gao, Weifeng Wang, Yiming Liu, Lingling Huang, Lingling Xidian Univ Sch Math & Stat Xian 710126 Peoples R China

A gradient-based search method (GBSM) is developed to solve multi-objective optimiza-tion problems. It uses the multi-objective gradient information to construct descent direc-tions, i.e., Pareto descent directions (PDDs), to accelerate the convergence. In addition, a multi-objective evolutionary algorithm based on decomposition is adopted to improve the diversity. The comparisons between GBSM with several selected multi-objective evolu-tionary algorithms and gradient based algorithms on benchmark functions indicate that the proposed method performs competitively and effectively. (c) 2021 Elsevier Inc. All rights reserved.

关键词： multi-objective optimization Descent direction multi-objective evolutionary algorithm

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Study on Optimization Parameters of Solid-Liquid Separator

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IEEE ACCESS 2020年 8卷 85051-85066页

作者： Shi, Mingjiang Huang, Dongmei Zhang, Mengfei Huang, Zhiqiang Guo, Shun Southwest Petr Univ Sch Mechatron Engn Chengdu 610500 Peoples R China

In order to achieve solid-liquid separation and recycling of drilling fluid during petroleum drilling, a new type of solid-liquid separation device was designed and developed to achieve high-efficiency solid-liquid separation and low cost. The working principle of the equipment was introduced, the mechanical model was established, and the sieve flow of drilling fluid and the movement of wet particles in the real situation were analyzed. The relationship between the processing capacity of the solid-liquid separator and the speed of the solid phase in the drilling fluid and the speed of the excitation motor and the rotary drum were given. multi-objective evolutionary algorithm was used to solve the multi-objective optimization model. Two typical evolutionary algorithms NSGA-II and MOEA/D were analyzed. An algorithm that solves this problem was selected and improved. A series of optimal solution sets for this problem were obtained by using the improved multi-objective evolutionary algorithm. Finally, based on the experimental prototype of the solid-liquid separator, a functional experiment was performed on the basis of the calculated optimal solution set. The experimental results show the feasibility of the multi-objective optimization model and algorithm.

关键词： multi-motor multi-objective evolutionary algorithm optimization solid-liquid separation

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Robust optimization of nonlinear impulsive rendezvous with uncertainty

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Science China(Physics,Mechanics & Astronomy) 2014年第4期57卷 731-740页

作者： LUO YaZhong YANG Zhen LI HengNian College of Aerospace Science and Engineering National University of Defense Technology State Key Laboratory of Astronautic Dynamics

The optimal rendezvous trajectory designs in many current research efforts do not incorporate the practical uncertainties into the closed loop of the design.A robust optimization design method for a nonlinear rendezvous trajectory with uncertainty is proposed in this *** performance index related to the variances of the terminal state error is termed the robustness performance index,and a two-objective optimization model(including the minimum characteristic velocity and the minimum robustness performance index)is formulated on the basis of the Lambert algorithm.A multi-objective,non-dominated sorting genetic algorithm is employed to obtain the Pareto optimal solution *** is shown that the proposed approach can be used to quickly obtain several inherent principles of the rendezvous trajectory by taking practical errors into ***,this approach can identify the most preferable design space in which a specific solution for the actual application of the rendezvous control should be chosen.

关键词： rendezvous and docking uncertainty robust optimization multi-objective evolutionary algorithm

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Parallel MOEA Based on Consensus and Membrane Structure for Inferring Phylogenetic Reconstruction

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IEEE ACCESS 2020年 8卷 6177-6189页

作者： Zhang, Qianqian Zhang, Jun Zhong, Yue Ye, Congming Min, Xiaoping Xiamen Univ Dept Comp Sci Xiamen 361005 Peoples R China Heilongjiang Prov Land Reclamat Headquarters Gen Rehabil Dept Harbin 150088 Peoples R China

In recent years, inferring phylogenies has attracted lots of attention in both academic community and various application fields. Phylogenetic inference usually consists of a couple of evolutionary relationships, which can be represented as a phylogenetic tree. The phylogenetic reconstruction problem can be defined as an optimization problem, targeting at finding the most eligible tree among all possible topologies according to a selected criterion. Since the combinatorial number of possible topologies exceeds tolerance, various heuristic and metaheuristic methods have been proposed to find approximate solutions according to the selected criterion. However, different criterions are based on different principle and conflict with each other basically. In this line, scholars has proposed multi-objective evolutionary algorithm (MOEA) based on diverse criteria. Nevertheless, MOEA has suffered unbearable time consumption due to its inherent drawbacks of computational complexity and convergence. By studying the independence between the sub-populations in each time-consuming step of MOEA, the steps without global information can be designed to be executed in parallel, which can fundamentally address computational problems. Effective parallel algorithms designed with the characteristics of modern multicore clusters can solve such problems. In this sense, we propose a parallelized multi-objective evolutionary algorithm (MOEA-MC) by deploying on Spark, which added consensus into evolutionary algorithm to improve the quality of convergence and used membrane structure to keep equal solutions under different weights. In order to assess the performance achieved by the proposal, we have performed comparison among different methods on three real-world datasets separately. The results have certified that the solutions derived from MOEA-MC are superior to traditional methods in all studied datasets. And parallelized MOEA-MC can get dominant position and optimal Pareto-frontier simulta

关键词： Consensus multi-objective evolutionary algorithm membrane structure phylogenetic reconstruction parallel algorithm

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MOEA based design of decentralized controllers for LFC of interconnected power systems with nonlinearities, AC-DC parallel tie-lines and SMES units

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ENERGY CONVERSION AND MANAGEMENT 2010年第5期51卷 873-880页

作者： Ganapathy, S. Velusami, S. Annamalai Univ Dept Elect Engn Annamalainagar 608002 Tamil Nadu India

A new design of multi-objective evolutionary algorithm based decentralized controllers for load-frequency control of interconnected power systems with Governor Dead Band and Generation Rate Con straint nonlinearities, AC-DC parallel tie-lines and Superconducting Magnetic Energy Storage (SMES) units, is proposed in this paper. The HVDC link is used as system interconnection in parallel with AC tie-line to effectively damp the frequency oscillations of AC system while the SMES unit provides bulk energy storage and release, thereby achieving combined benefits. The proposed controller satisfies two main objectives, namely, minimum Integral Squared Error of the system output and maximum closed-loop stability of the system. Simulation studies are conducted on a two area interconnected power system with nonlinearities, AC-DC tie-lines and SMES units. Results indicate that the proposed controller improves the transient responses and guarantees the closed-loop stability of the overall system even in the presence of system nonlinearities and with parameter changes. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Load-frequency control multi-objective evolutionary algorithm AC-DC tie-lines Governor Dead Band Generation Rate Constraint Superconducting Magnetic Energy Storage

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A comprehensive multi-objective approach of service selection for service processes with twofold restrictions

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 92卷 119-140页

作者： Liang, Helan Du, Yanhua Jiang, Ting Li, Fanzhang Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Peoples R China Univ Sci & Technol Beijing Sch Mech Engn Beijing 700083 Peoples R China

To build complex business processes with QoS constraints by services with inter-service correlations is a challenge. Meeting this challenge involves the QoS-aware service selection problem for service processes with twofold restrictions (QoS constraints and inter-service correlations). Recently, QoS-aware service selection has been addressed from a multi-objective perspective. However, existing research fails to comprehensively consider both QoS constraints and inter-service correlations that widely exist in a real business world. Besides, none of them comprehensively considers both service selection at build time and run time, which may lead to high economic compensation caused by breach penalties at run time. In this paper, we present a comprehensive multi-objective optimization model for service selection with twofold restrictions. Furthermore, an improved multi-objective evolutionary algorithm framework for this model (MOEAQI) is proposed, which can provide a systematic solution for service selection at build time and run time respectively. In this framework, a constrained-domination principle is designed for the specific objectives and constraints in different phases, and several improved strategies are proposed to increase the searching effectiveness and efficiency. Finally, three sets of experiments with different problem sizes are constructed, and our approach is tested in comparison to the state-of-the-art algorithms for service selection problems including GDE3, E(3)MOGA and DASC. The performance of each algorithm is evaluated in the aspects of set coverage, spacing and computation time. Experimental results demonstrate that our approach is more effective than existing methods for service selection with twofold restrictions. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Service selection Service process QoS constraint Inter-service correlation multi-objective evolutionary algorithm

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Two-lbests based multi-objective particle swarm optimizer

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ENGINEERING OPTIMIZATION 2011年第1期43卷 1-17页

作者： Zhao, S. -Z. Suganthan, P. N. Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

The global best (gbest) or local best (lbest) of every particle in state-of-the-art multi-objective particle swarm optimization (MOPSO) implementations is selected from the non-dominated solutions in the external archive. This approach emphasizes the elitism at the expense of diversity when the size of the current set of non-dominated solutions in the external archive is small. This article proposes that the gbests or lbests should be chosen from the top fronts in a non-domination sorted external archive of reasonably large size. In addition, a novel two local bests (lbest) based MOPSO (2LB-MOPSO) version is proposed to focus the search around small regions in the parameter space in the vicinity of the best existing fronts unlike the current MOPSO variants in which the pbest and gbest (or lbest) of a particle can be located far apart in the parameter space thereby potentially resulting in a chaotic search behaviour. Comparative evaluation using 19 multi-objectives test problems and 11 state-of-the-art multi-objective evolutionary algorithms ranks overall the 2LB-MOPSO as the best while two state-of-the-art MOPSO algorithms are ranked the worst with respect to other multi-objective evolutionary algorithms.

关键词： multi-objective evolutionary algorithm multi-objective particle swarm optimization two lbests multi-objective particle swarm optimization (2LB-MOPSO) external archive

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