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A flexible multi-objective task allocation method for major marine emergencies

OCEAN ENGINEERING 2024年 297卷

作者： Guo, Yu Mao, Jiahui Zhang, Haidong Li, Jichao Yang, Qingqing Yang, Kewei Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China

The boom in the maritime economy has led to an increase in the frequency of major marine emergencies, and it is critical for decision-makers to develop a well-planned task-allocation scheme. Compared to ordinary marine emergencies, major marine emergencies (MME) are characterized by multiple tasks, evolving scenarios, and difficulties in terms of adjustment. This paper introduces flexibility theory into marine emergency decisionmaking by proposing a novel framework called the two-phase flexible task-allocation scheme generation (TFTASG). We first developed a multi-objective programming model of the TFTASG in two phases: three objectives of responding to MME within a critical time and the maximization of the subject's enthusiasm and task completion efficiency were designed in the first phase, and different objectives are formulated according to different task types;the objective of flexibility maximization was constructed in the second phase. Then, to solve the combinatorial explosion problem and generate timely solutions, the improved differential evolution algorithm was proposed according to the characteristics of the TFTASG. Finally, a real-world case was carried out to demonstrate the effectiveness of our model. The results show that our proposed flexible task-allocation scheme can achieve fast adjustment at low cost and that the TFTASG can provide decision-makers with scientific decision support.

关键词： Maritime emergencies Task allocation Multi -objective programming Flexibility intelligent optimization algorithms

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Improved Wolf Pack Algorithm with Differential Evolution for Global Numerical optimization 10

Improved Wolf Pack Algorithm with Differential Evolution for...

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10th International Conference on Advanced Computational Intelligence (ICACI)

作者： Chen, Xiayang Tang, Chaojing Zhang, Lei Liu, Yi Natl Univ Def Technol Coll Elect Sci Changsha Hunan Peoples R China

ISBN: (纸本)9781538643624

Wolf Pack Algorithm (WPA) is a new optimal algorithm with outstanding performance, but there is still room for improvement of its convergence Aiming at speeding up the convergence of the WPA, a novel hybrid optimal algorithm based on Wolf Pack Algorithm and Differential Evolution (DE) is proposed, which named WPADE for short. Differential Evolution is introduced into the process of the wolves updating to replace the random updating. Four standard benchmark functions are applied to verify the effects of these improvements. Our experiments shows that the performance of WPADE is better than the basic WPA and other evolution algorithm or swarm intelligence optimal algorithm, such as GA, DE, PSO, and ABS.

关键词： Wolf Pack Algorithm Differential Evolution swarm intelligence intelligent optimization algorithms metaheuristic algorithms

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Optimal Energy Storage Configuration Method For Wave Energy Power Generation System 3

Optimal Energy Storage Configuration Method For Wave Energy ...

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3rd International Conference on Energy and Electrical Power Systems (ICEEPS)

作者： Wang, Lixin Li, Ye Yang, Shuai Chen, Yating He, Wenbin Ma, Xiaoting Inner Mongolia Power Grp Hohhot Peoples R China Inner Mongolia Elect Power Econ & Technol Res Ins Hohhot Peoples R China

ISBN: (纸本)9798350375145;9798350375138

The rapid and random changes in wave characteristics make it difficult to meet the requirements for secure and stable operation of the power grid, resulting in low wave energy utilization. The inherent advantage of energy storage can address the grid connection and improve wave energy consumption, hence proposing an energy storage optimization configuration method for wave energy generation system. The goal is to minimize the construction and operational costs of the wave energy storage system by taking into account constraints such as the state of charge of the energy storage, energy storage power, and the grid operating conditions. An energy storage configuration optimization model is established. The Whale optimization Algorithm and Genetic Algorithm are applied to solve the inner and outer layer models, optimizing the system's daily operation strategy, energy storage access points, and capacity to increase wave energy utilization while minimizing system costs. Simulation results show that in the IEEE 33-node system, the proposed method can address the energy storage layout, and capacity configuration issues in wave energy power generation system(WEPGS), verifying the effectiveness and feasibility of the proposed method.

关键词： Energy storage system Wave energy Optimal configuration intelligent optimization algorithms

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Review of Power Network Planning Research Status

Review of Power Network Planning Research Status

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IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA)

作者： Xue Lei Wang Li-bin Li Chunhui Li Qiang Shanxi Elect power Corp Econ & Tech Inst State Gr Taiyuan Peoples R China Taiyuan Univ Technol Taiyuan Peoples R China

ISBN: (纸本)9781479969890

Primary mission and basic principle of power network planning is introduced in detail. Mathematical model of power network planning and related constraints are summarized. Several methods based on mathematical optimization, heuristic algorithm, as well as the intelligent optimization algorithms, such as genetic algorithm are illustrated separately, the characteristics and problems of algorithms are analyzed. The defects, which appear while the above heuristic algorithms are applied to nonlinear combination optimization problem, e.g., power network planning, are analyzed. Finally, the problems to be deeply studied are pointed out.

关键词： Power Network Planning Smart grid Heuristic algorithm intelligent optimization algorithms

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Construction Method of Overlay Network for Cyber-Physical System 23

Construction Method of Overlay Network for Cyber-Physical Sy...

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23rd Asia-Pacific Network Operations and Management Symposium (APNOMS)

作者： Deng, Liangliang Chen, Yubing Zhang, Lichen Guangdong Univ Technol Fac Comp Sci & Technol Guangzhou Peoples R China Guangdong Univ Technol Sch Electromech Engn Guangzhou Peoples R China

ISBN: (数字)9784885523397

ISBN: (纸本)9784885523397

The real-time requirements of network communication in cyber-physical systems are high, and the network topology is complex, which makes the communication efficiency in the system low in real-time. However, the traditional routing algorithm has been unable to adapt to the increasingly complex intelligent communication network in today's society. In order to improve the network communication efficiency of the cyber-physical system, a cyber-physical fusion system overlay network construction method based on an improved variable neighborhood search algorithm is proposed. Reduce the communication time overhead of cyber-physical systems. In the network node selection method of the cyber physical system, the neighborhood structure and the dithering method of the variable neighborhood search algorithm are optimized. The objective function is used to further optimize the selection strategy of the covering node set. Finally, the network time cost, communication delay and algorithm stability of the proposed method are analyzed on a large number of random discrete node sets. The experimental results show that the optimized variable domain search algorithm can effectively reduce the network delay and time overhead, and improve the communication efficiency of the cyber-physical system.

关键词： Cyber-physical systems overlay networks realtime communication intelligent optimization algorithms

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Soft Computing Techniques Applied to Distribution Network Reconfiguration: A Survey of the State-of-the-Art 8

Soft Computing Techniques Applied to Distribution Network Re...

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8th International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Srivastava, Ishan Bhat, S. S. VNIT Elect Engn Dept Nagpur Maharashtra India

ISBN: (纸本)9781509011445

Network reconfiguration is the process of altering the topological structure of distribution feeders to achieve more reliable and economic operation of distribution network. Distribution Network Reconfiguration (DNR) is a multi-objective, combinatorial, non-differentiable constrained optimization problem. This strategy nowadays is very frequently used by distribution engineers to meet various objectives like loss minimization and power quality enhancement. This paper describes the various possible objectives and constraints of radial network reconfiguration. This paper also presents a comprehensive review of various strategies of network reconfiguration which are mainly based on heuristic methods and intelligent optimization algorithm.

关键词： Feeder Reconfiguration Combinatorial Heuristic Methods intelligent optimization algorithms Soft computing techniques

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Enhanced Seagull optimization Algorithm for Photovoltaic Cell Parameter Estimating 41

Enhanced Seagull Optimization Algorithm for Photovoltaic Cel...

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第41届中国控制会议

作者： Hongyin Wei Kailin Xu Jianming Zhang Institute of Cyber-Systems and Control State Key Laboratory of Industrial Control TechnologyZhejiang University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,an enhanced seagull optimization algorithm based on a double group evolutionary mechanism(DSOA) is proposed for photovoltaic(PV) cell parameter *** the DSOA,a double group search strategy and time-based mutation strategy are added to the population renewal mechanism of the conventional *** performance of DSOA on standard benchmark functions is surprising;it was good at accuracy and exploitation,also convergence *** with SOA and the other two classical intelligent optimization algorithms,the performance of DSOA is comprehensively *** is adopted to estimate PV cell *** simulation results demonstrate that estimated currents of circuit model based on DSOA have high similarity to the measured value of the photovoltaic cell.

关键词： Photovoltaic(PV) parameter estimating seagull optimization algorithm intelligent optimization algorithms

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A Study of Multi-Constraint Planning Problem for Time Series Data with Correlation 4

A Study of Multi-Constraint Planning Problem for Time Series...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Yu, Fangda Yu, Xiaotong Northeast Forestry University Mechanical and Electrical Engineering Heilongjiang Harbin China

ISBN: (纸本)9798350352931

This paper studies the multi-constraint optimization problem under the background of time series data correlation, which is particularly important in the fields of finance, energy and meteorology. Aiming at the multiple constraints that need to be considered at the same time in practical applications, such as time, resources and risks, this paper proposes an optimization algorithm that combines time series correlation analysis. Through theoretical analysis and model design, the algorithm has been verified to be effective in multiple application scenarios, especially in improving financial forecasting and decision-making efficiency. Then, we use the financial market time series data into the algorithm of this paper for optimization experiments. The experimental results show that our new algorithm can not only improve the accuracy of similar financial market prediction models, but also significantly improve the decision-making efficiency. Finally, this paper looks forward to the improvement of the method in complex constraints and the direction of more efficient algorithm, which provides ideas for further research. © 2024 IEEE.

关键词： dynamic dependency analysis intelligent optimization algorithms multi-constraint optimization multi-objective optimization time series correlation

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Cost-Minimized Partial Computation Offloading in Cloud-Assisted Mobile Edge Computing Systems

Cost-Minimized Partial Computation Offloading in Cloud-Assis...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Nowadays, smart mobile devices (SMDs) support various computation-intensive and delay-sensitive applications, e.g., online games, and figure compression. However, SMDs have limited computing resources and battery energy and cannot execute all tasks of the above applications in a real-time manner. Cloud computing provides enormous computing resources and energy that can easily execute tasks offloaded from SMDs. However, could data centers (CDCs) are often located in remote sites, which leads to long transmission time. Small base stations (SBSs) offer high-bandwidth and low-latency services for SMDs, which solves the problem of cloud computing. However, it becomes a challenge to achieve the lowest cost in such a heterogeneous architecture including multiple SMDs, SBSs, and the CDC while meeting delay requirements of tasks. This work proposes a cost-minimized computation offloading strategy to minimize the total cost of the system. A constrained optimization problem is first formulated based on the hybrid architecture. Afterward, a two-stage optimization algorithm called a Lévy flights and Simulated Annealing-based Grey wolf optimizer (LSAG) is developed to optimize the total cost of the system. In the first stage, the optimal edge selection policy is determined given multiple available SBSs. In the second stage, task offloading and resource allocation among SMDs, SBSs, and the cloud are determined. Experiments with real-life tasks prove that LSAG significantly achieves lower cost with faster convergence speed than state-of-the-art peers. © 2023 IEEE.

关键词： cloud computing computation offloading intelligent optimization algorithms Lévy flights Mobile edge computing

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A fast fault location method based on detecting the minimum number of phasor measurement units using a novel adaptive binary differential evolution optimization algorithm

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2015年第11期25卷 2933-2947页

作者： Ghaffarzadeh, Navid Parpaei, Mohammad Tayar, Mirza Zuhaib Imam Khomeini Int Univ Tech & Engn Fac Qazvin Iran

This paper proposes a novel adaptive binary differential evolution optimization algorithm based on optimal placement of phasor measurement units (PMUs) and an intelligent strategy for fault location in power systems. The novelties of the proposed adaptive binary differential evolution optimization algorithm are that the proposed approach presents two new factors, namely, "weight factor" and "reduction factor", in order to improve convergence speed and enhance the capability of the novel modified binary differential evolution optimization algorithm for detecting optimal placement of PMUs in large-scale problems. To investigate the capability of the proposed approach, it is applied to several IEEE test systems (IEEE 14 bus, IEEE 24 bus, IEEE 30 bus, IEEE 39 bus, and IEEE 57 bus test systems). In addition, a comparison with conventional optimization algorithms has been carried out. Then, according to the PMUs data, an intelligent fault location strategy will be proposed. The main advantages of the proposed intelligent fault location strategy are higher accuracy and speed than conventional fault location approaches. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： binary differential evolution optimal PMU placement fault location intelligent optimization algorithms

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