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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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Efficient Electromagnetic Compatibility optimization Design Based on the Stochastic Collocation Method

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APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL 2024年第6期39卷 533-541页

作者： Niu, Xiaobing Liu, Shenglin Qiu, Runze Dalian Maritime Univ Coll Marine Elect Engn Dalian 116026 Peoples R China

- Nowadays, in the field of electromagnetic compatibility (EMC), numerical methods such as finite element analysis are often used for simulation analysis. These numerical methods take a long time to solve some complex simulation problems, which is not conducive to the optimal design of EMC. In particular, the intelligent optimization algorithm that needs continuous iterative calculation will not be realized because of the long optimization time. This paper realizes the innovative application of the uncertainty analysis method (Stochastic Collocation Method) in EMC optimization design. Two typical EMC optimization design problems, namely, the prediction of cable crosstalk and the design of shielding performance of metal boxes, are proposed to verify the effectiveness of the optimization algorithm. Meanwhile, its performance is compared with the classical intelligent optimization algorithms such as genetic algorithms and immune algorithms.

关键词： Efficient optimization design elec- tromagnetic compatibility failure mechanism analysis intelligent optimization algorithms stochastic colloca- tion method

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Hybrid intelligent framework for carbon price prediction using improved variational mode decomposition and optimal extreme learning machine

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CHAOS SOLITONS & FRACTALS 2022年 156卷 111783-111783页

作者： Wang, Jujie Cui, Quan He, Maolin Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Forecast & Evaluat Meteoro Nanjing 210044 Peoples R China

As the climate problem continues to worsen, carbon trading markets for energy conservation and emission reduction have been established in many countries. Accurate forecasting of carbon trading prices is not only a realistic problem, but also brings huge challenges to relevant researches. In this study, a novel predicting model is proposed to predict carbon price. And this model combines the advantages of the improved variational mode decomposition (IVMD) algorithm, multiscale entropy (MSE) algorithm, and the extreme learning machine (ELM) model improved by the intelligent optimization algorithm. Firstly, center frequency (CF) and mutual information (MI) entropy are utilized to jointly determine the number of decomposition layers of the variational mode decomposition (VMD), and avoid the problem of excessive decomposition. Subsequently, the complexity of each intrinsic mode function (IMF) from the improved variational mode decomposition is calculated by multiscale entropy, and intrinsic mode functions are recombined to reduce the complexity of subsequent modeling. At the last, the extreme learning machine optimized by the sparrow search algorithm (SSA) is adopted to model and predict the different sequence combinations. The performance indicators of the proposed model are significantly lower than others. For example, the root mean square error (RMSE) of the proposed model is 0.6653 in Hubei market, 0.9719 in Guangdong market and 1.2819 in Shanghai market. Additionally, the optimized extreme learning machine model is more suitable for the prediction of time series, which also provides an effective forecasting tool for related researchers. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Variational mode decomposition Multiscale entropy Sparrow search algorithm Extreme learning machine intelligent optimization algorithms

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Designing reduced congestion road networks via an elitist adaptive chemical reaction optimization

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年 163卷

作者： Salman, Sinan Alaswad, Suzan Zayed Univ Coll Technol Innovat Abu Dhabi U Arab Emirates Zayed Univ Coll Business Abu Dhabi U Arab Emirates

We propose a new approach for solving a variant of the transportation Network Design Problem (NDP) where network congestion is reduced via traffic pattern redesign. The investigated NDP selects a set of roads to be converted from two-way traffic to one-way traffic. This allows increased traffic flow in the selected roads at the cost of restricting flow in the opposing direction roads. An optimal set of converted roads can result in significant congestion reduction for the network without the high construction costs associated with network capacity expansion. The new approach builds on the recently developed Chemical Reaction optimization (CRO) metaheuristic and leverages Markov chain traffic assignment to predict traffic flow changes in response to network modifications. We propose an elitist and adaptive version of the CRO metaheuristic allowing it to more efficiently search for optimal solutions. We deploy the proposed approach to the cities of Abu Dhabi and Sioux Falls and report on our results. We also compare the Elitist Adaptive CRO (EACRO) results to those of Genetic Algorithm (GA) to demonstrate its performance. Compared to GA, the proposed approach reduces network congestions faster using fewer objective function evaluations. It also produces lower network congestion designs using long optimization runs. However, we find the algorithm to be sensitive to parameter calibration and dependent on the use of elitism for good performance. We explore the internal dynamics of the proposed algorithm and investigate its search effectiveness to understand its superior performance when compared to GA.

关键词： Network Design Problem Markov Chain Traffic Assignment Chemical Reaction optimization intelligent optimization algorithms Operations Research

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Research on Thrust Allocation for Power Positioning System Based on Improved Sparrow Search Algorithm With Multi-Strategy Fusion

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IEEE ACCESS 2024年 12卷 148434-148449页

作者： Wei, Zhenghao He, Zhibin Sun, Bin Su, Yulong Dalian Maritime Univ Marine Engn Coll Dalian 116026 Peoples R China

This paper proposes an improved sparrow search algorithm (ADGSSA) based on multi-strategy fusion, and studies and verifies it in the thrust distribution of the dynamic positioning system of the new research vessel of Dalian Maritime University. First, the sparrow search algorithm is introduced. Aiming at its shortcomings in convergence speed, optimization accuracy and easy to fall into local optimality, a multi-strategy fusion improvement scheme including piecewise nonlinear dynamic random convergence factor strategy, warning position update condition and formula adjustment strategy, and golden sine perturbation strategy is proposed. Subsequently, the improved algorithm is applied to 12 benchmark functions with different characteristics and compared with Coati optimization algorithm (COA), whale optimization algorithm (WOA) and sparrow search algorithm (SSA). Experimental results show that ADGSSA outperforms the other three algorithms in optimization accuracy, convergence performance and stability. In the evaluation of 12 benchmark functions, ADGSSA demonstrates superior performance compared to SSA, WOA, and COA. Specifically, ADGSSA achieves the closest optimal value in 83% of the functions, provides the best average value in 85% of the functions, and has the lowest standard deviation in 75% of the functions. In the actual application of the thrust distribution system, ADGSSA shows significant advantages. Compared with WOA, the average power of the thruster is reduced by about 75%;compared with SSA, it is reduced by about 35%;compared with COA, it is reduced by about 17%. In addition, in 12 sets of thrust tests, the thruster wear of ADGSSA reached the smallest proportion of 82% among the four algorithms, which is significantly better than other algorithms. The test results and convergence curves further show that ADGSSA has significant effectiveness and superiority in improving the thrust distribution performance of the dynamic positioning system, proving the poten

关键词： intelligent and practical dual-purpose vessels ship power positioning thrust distribution thrust distribution spar search algorithm spar search algorithm multi-strategy fusion multi-strategy fusion intelligent optimization algorithms intelligent optimization algorithms intelligent optimization algorithms

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PMDV-hop: An effective range-free 3D localization scheme based on the particle swarm optimization in wireless sensor network

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2018年第1期12卷 61-80页

作者： Wang, Wenjuan Yang, Yuwang Wang, Lei Lu, Wei Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci & Technol Nanjing Jiangsu Peoples R China

Location information of individual nodes is important in the implementation of necessary network functions. While extensive studies focus on localization techniques in 2D space, few approaches have been proposed for 3D positioning, which brings the location closer to the reality with more complex calculation consumptions for high accuracy. In this paper, an effective range-free localization scheme is proposed for 3D space localization, and the sensitivity of parameters is evaluated. Firstly, we present an improved algorithm (MDV-Hop), that the average distance per hop of the anchor nodes is calculated by root-mean-square error (RMSE), and is dynamically corrected in groups with the weighted RMSE based on group hops. For more improvement in accuracy, we expand particle swarm optimization (PSO) of intelligent optimization algorithms to MDV-Hop localization algorithm, called PMDV-hop, in which the parameters (inertia weight and trust coefficient) in PSO are calculated dynamically. Secondly, the effect of various localization parameters affecting the PMDV-hop performance is also present. The simulation results show that PMDV-hop performs better in positioning accuracy with limited energy.

关键词： Range-free localization sensitivity of parameters improved algorithm intelligent optimization algorithms

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Rational random walk-based optimal placement of phasor measurement units to enhance the initializing and guiding the optimization processes

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2017年第7期27卷 n/a-N.PAG页

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

Phasor measurement unit (PMU) is one of the state estimation devices that can synchronously measure both the voltage and the current phasor. Different methods have been presented for detecting the optimal number of PMUs. Some disadvantages of these methods are high computation burden, dependence on tuning parameters, complex calculation, and lack of accountability in large-scale power networks. This paper presents the novel strategy called rational random walk (RRW) to tackle these disadvantages. The first advantage of the proposed approach is the simple structure than conventional approaches. The second advantage is the independence of tuning parameters. The third advantage is the capability of implementation on the large-scale power networks. The fourth advantage is the use of 2-stage strategy for reducing the search space. The fifth advantage is capability of RRW to superimpose on the conventional optimization algorithm to enhance the convergence speed. The sixth advantage is capability of RRW to extract the optimal PMU placement under N-1 contingency. Superiority of the RRW in comparison with other methods is verified by implementing on the standard Institute of Electrical and Electronics Engineers (IEEE) test systems. Moreover, the proposed method has been used to specify and test the minimum number of PMUs in 2 real life systems in Iran power network. One of them is 400kV, and the other one is 230kV, and also a large-scale test system polish 2383-bus power system.

关键词： distributed power generation evolutionary algorithm intelligent optimization algorithms optimal PMU placement (OPP)

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Research Status and Progress on Anti-ship Missile Path Planning

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Acta Automatica Sinica 2013年第4期39卷 347-359页

作者： Gang LIU Song-Yang LAO Dong-Feng TAN Zhi-Chao ZHOU Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha 410073 China Unit 95039 of PLA Shantou 515049 China Naval Marine Institute Guangzhou 510430 China

Path planning is one of the multi-objective non-linear optimization problems with multiple constraints. The problem of anti-ship missile path planning (ASMPP) and its approaches are reviewed in this work. Firstly, the research status of ASMPP problem domains is analyzed. It includes unification and specification of correlative nomenclature about ASMPP, the problem boundary and definition, and the characteristics of ASMPP. Secondly, the research progress of problem model is analyzed. Thirdly, the research progress of ASMPP methods based on geometry principle and intelligent optimization algorithms is analyzed. Fourthly, the research progress of cooperative path planning methods of anti-ship missiles launched from multiple platforms is analyzed and summarized. Finally, a brief conclusion of the key problems and the development trend of ASMPP are elaborated.

关键词： Anti-ship missile path planning geometry intelligent optimization algorithms multiple platforms

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An advanced teaching-learning-based algorithm to solve unconstrained optimization problems

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intelligent SYSTEMS WITH APPLICATIONS 2023年 17卷

作者： Fatehi, Mohammad Toloei, Alireza Niaki, S. T. A. Zio, Enrico Shahid Beheshti Univ Dept Aerosp Engn Tehran Iran Sharif Univ Technol Dept Ind Engn Tehran Iran MINES Paris PSL Univ Paris France Politecn Milan Energy Dept Milan Italy

The Teaching-Learning-Based optimization (TLBO) algorithm is being extended to a broader range of applied optimization problems in the literature, mimicking the teaching-learning process. This paper proposes an Advanced Teaching-Learning-Based optimization (Ad-TLBO) algorithm to enhance the efficiency and performance of the original version of TLBO in terms of accuracy, convergence rate, and reliability characteristics. The advancement is obtained by modifying the initialization, search approach, and structure of the two main phases of this algorithm in four steps to improve exploration and exploitation capability. Efficiency comparisons are shown in four challenges with various benchmark functions with multimodal, separable, differentiable, and continuity characteristics. The results are compared with several intelligent optimization algorithms. It is also deduced that this algorithm outperforms all investigated optimization algorithms in terms of accuracy, convergence speed, and success to reach acceptable solutions for various benchmark functions.

关键词： intelligent optimization algorithms Teaching-learning-based optimization (TLBO) Advanced teaching-learning-based optimization (Ad-TLBO)

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