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40th IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Li, Jiahui Kang, Hui Sun, Geng Liang, Shuang Liu, Yanheng Zhang, Ying Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)9780738112817

Unmanned aerial vehicle (UAV) communications and networks are promising technologies in the forthcoming fifth-generation wireless communications. However, they have the challenges for realizing secure communications. In this paper, we consider to construct a virtual antenna array consists UAV elements and use collaborative beamforming (CB) to achieve the UAV secure communications with different base stations (BSs), subject to the known and unknown eavesdroppers on the ground. To achieve a better secure performance, the UAV elements can fly to optimal positions with optimal excitation current weights for performing CB transmissions. However, this leads to extra motion energy consumptions. We formulate a secure communication multi-objective optimization problem (MOP) of UAV networks to simultaneously improve the total secrecy rates, total maximum sidelobe levels (SLLs) and total motion energy consumptions of UAVs by jointly optimizing the positions and excitation current weights of UAVs, and the order of communicating with different BSs. Due to the complexity and NP-hardness of the formulated MOP, we propose an improved multiobjective dragonfly algorithm with chaotic solution initialization and hybrid solution update operators (IMODACH) to solve the problem. Simulation results verify that the proposed IMODACH can effectively solve the formulated MOP and it has better performance than some other benchmark approaches.

关键词： UAV communications collaborative beamforming physical layer security multi-objective optimization problem evolutionary computation

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Optimal design of dampers within seismic structures

Optimal design of dampers within seismic structures

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2nd International Conference on Smart Materials and Nanotechnology in Engineering

作者： Ren, Wenjie Qian, Hui Song, Wali Wang, Liqiang Hebei Univ Technol Sch Civil Engn Tianjin 300401 Peoples R China Zhengzhou Univ Sch Civil Engn Zhengzhou 450001 Peoples R China Tianjin Univ Technol & Educ Dept Elect Engn Tianjin 300222 Peoples R China

ISBN: (纸本)9780819478047

An improved multi-objective genetic algorithm for structural passive control system optimization is proposed. Based on the two-branch tournament genetic algorithm, the selection operator is constructed by evaluating individuals according to their dominance in one run. For a constrained problem, the dominance-based penalty function method is advanced, containing information on an individual's status (feasible or infeasible), position in a search space, and distance from a Pareto optimal set. The proposed approach is used for the optimal designs of a six-storey building with shape memory alloy dampers subjected to earthquake. The number and position of dampers are chosen as the design variables. The number of dampers and peak relative inter-storey drift are considered as the objective functions. Numerical results generate a set of non-dominated solutions.

关键词： multi-objective optimization problem genetic algorithm vibration control dampers selection mechanism penalty function number and position

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Uplink Data Transmission Based on Collaborative Beamforming in UAV-assisted MWSNs

Uplink Data Transmission Based on Collaborative Beamforming ...

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IEEE Global Communications Conference (GLOBECOM)

作者： Wang, Aimin Wang, Yuxin Sun, Geng Li, Jiahui Liang, Shuang Liu, Yanheng Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Sch Math Changchun 130012 Peoples R China

ISBN: (纸本)9781728181042

Unmanned aerial vehicles (UAVs) have attracted growing attention in enhancing the performance of mobile wireless sensor networks (MWSNs) since they can act as the aerial base stations (ABSs) and have the autonomous nature to collect data. In this paper, we consider to construct a virtual antenna array (VAA) consists of mobile sensor nodes (MSNs) and adopt the collaborative beamforming (CB) to achieve the long-distance and efficient uplink data transmissions with the ABSs. First, we formulate a high data transmission rate multi-objective optimization problem (HDTRMOP) of the CB-based UAV-assisted MWSN to simultaneously improve the total transmission rates, suppress the total maximum sidelobe levels (SLLs) and reduce the total motion energy consumptions of MSNs by jointly optimizing the positions and excitation current weights of MSN-enabled VAA, and the order of communicating with different ABSs. Then, we propose an improved non-dominated sorting genetic algorithm-III (INSGA-III) with chaos initialization, average grade mechanism and hybrid-solution generate strategy to solve the problem. Simulation results verify that the proposed algorithm can effectively solve the formulated HDTRMOP and it has better performance than some other benchmark methods.

关键词： Mobile wireless sensor networks unmanned aerial vehicles collaborative beamforming non-dominated sorting genetic algorithm-III multi-objective optimization problem

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Computational Complexity Measures for Many-objective optimization problems

Computational Complexity Measures for Many-objective Optimiz...

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Conference on Conquering Complexity - Challenges and Opportunities

作者： Curry, David M. Dagli, Cihan H. Missouri Univ Sci & Technol Dept Engn Management & Syst Engn Rolla MO 65409 USA

multi-objective optimization problems (MOPs) are commonly encountered in the study and design of complex systems. Pareto dominance is the most common relationship used to compare solutions in MOPs, however as the number of objectives grows beyond three, Pareto dominance alone is no longer satisfactory. These problems are termed "Many-objective optimization problems (MaOPs)". While most MaOP algorithms are modifications of common MOP algorithms, determining the impact on their computational complexity is difficult. This paper defines computational complexity measures for these algorithms and applies these measures to a multi-objective Evolutionary Algorithm (MOEA) and its MaOP counterpart. (C) 2014 The Authors. Published by Elsevier B.V.

关键词： multi-objective optimization problem MOP many-objective optimization problem MaOP computational complexity multi-objective evolutionary algorithm MOEA

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Tradeoff Analysis of Energy Efficiency and Spectral Efficiency for Downlink NOMA Systems 6

Tradeoff Analysis of Energy Efficiency and Spectral Efficien...

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6th International Conference on Information Communication and Signal Processing, ICICSP 2023

作者： Su, Binbin Xue, Jie Liu, Hongbo Liu, Yongcai Naval University of Engineering National Key Laboratory of Electromagnetic Energy Wuhan China

ISBN: (纸本)9798350339994

Non-orthogonal multiple access (NOMA) is regarded as a promising technique for the fifth generation communication due to both its outstanding spectral efficiency (SE) and energy efficiency (EE). In this letter, we investigate the joint optimization of EE and SE problem for a downlink single-input single-output (SISO) NOMA system under the condition of minimum user rate requirements and total power constraints, which leads to a multi-objective optimization problem (MOP). We firstly transformed it into a single-objective optimization problem (SOP) by applying the weighted sum method. To solve the SOP, it is then converted into a pseudo-concave problem and finally solved by Dinkelbach's method. Simulation results show that the proposed method can reach proper tradeoff between the optimal EE and SE. © 2023 IEEE.

关键词： Dinkelbach's method energy efficiency(EE) multi-objective optimization problem Non-orthogonal multiple access weighted sum method

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multi-objective Energy optimization Of Locomotions For Underwater Snake Robot

Multi-objective Energy Optimization Of Locomotions For Under...

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作者： Kenny Duy Luong Norwegian University of Science and Technology

学位级别：硕士

Biologically inspired Autonomous Underwater Vehicles (AUVs) have been developed in the recent decades. Tis thesis focuses on the AUVs that are biologically inspired by snakes, called Underwater Snake Robots (USRs). A well-known issue of the USRs, or any AUVs, is the long- term autonomy. To achieve this, energy efcient approaches are required. Many studies have considered single-objective optimization problems regarding the energy efciency of the USR, but almost none with multi-objective optimization problems (MOPs). Tis thesis presents MOPs of diferent locomotions of the USR. Te presented MOPs consider the energy efcient optimiza- tion of maximizing the forward velocity, while minimizing the power consumption of the USR. For computing the efcient motion paterns, two multi-objective Evolutionary Algorithms (MOEAs) called Non-dominated Sort Genetic Algorithm II (NSGA-II), and Hypervolume Estimation Algo- rithm for multi-objective optimization (HypE) are applied. A challenging topic of the USR, is their adaptability of diferent locomotions. Diferent locomotions of the USR give rise to diferent search spaces for optimization. We present simulation studies of the two most common snake locomotions: (i) lateral undulation and (ii) eel-like motion. Furthermore, we also present and in- vestigate three altered motion patern of the USR. Te aim of the altered locomotions is to let the MOEAs generate efcient locomotions through evolutionary, which we do not know the gait of. From the simulation results, it turns out that one of the altered motion patern approximates a motion similar to the lateral undulation. Tis motion patern is generated based on Fourier se- ries. Te obtained simulation results are based on optimization with optimal Genetic Algorithm (GA) parameters, found by numerous presimulations of the MOPs. Since this is multi-objective optimization problems, the end results will be in the form of Pareto fronts. Tese Pareto fronts can be used as trade-ofs for selecting th

关键词： Underwater snake robot Energy efciency multi-objective Evolutionary Algorithm Non-dominated Sort Genetic Algorithm II Hypervolume Estimation Algorithm for multi-objective optimization Principal Component Analysis Partial Least Square Regression multi-objective optimization problem Pareto fronts Patern generators

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Locate multiple Pareto Optima Using a Species-Based multi-objective Genetic Algorithm

Locate Multiple Pareto Optima Using a Species-Based Multi-ob...

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9th International Conference on Bio-Inspired Computing - Theories and Applications (BIC-TA)

作者： Fu, Yaping Wang, Hongfeng Huang, Min Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9783662450482

In many real-world multi-objective optimization problems (MOOPs), the decision maker may only concern partial, rather than all, Pareto optima. This requires the solution algorithm to search and locate multiple Pareto optimal solutions simultaneously with higher accuracy and faster speed. To address this requirement, a species-based multi-objective GA (speMOGA) is designed in this paper, where multiple sub-populations would be constructed to search for multiple nondomiated solutions in parallel via decomposing a MOOP into a set of subproblems using the Tchebycheff approach. Based on a series of benchmark test problems, experiments are carried out to investigate the performance of the proposed algorithm in comparison with two classical multi-objective GAs: MOEA/D and NSGA-II. The experimental results show the validity of the proposed algorithm on locating multiple Pareto optima.

关键词： multi-objective optimization problem Genetic algorithm Species Tchebycheff approach

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To Invest or Not to Invest: The Exploration of Preferred Investment Strategies in Corporate Renewable Power Purchase Agreements

To Invest or Not to Invest: The Exploration of Preferred Inv...

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作者： Li, Daiwei PennState University Libraries

学位级别：Master of Science

The modern society has seen a continuously growing electricity consumption and its associated environmental consequences. With recent technology advancements, renewable energy has been considered by many as a source of electricity that is both economically feasible and environmentally friendly. The investment of renewable energy projects can be intriguing, however. This research first developed a theoretical model using multi-objective optimization problem to determine the preferred investment strategies that considers both the economic and environmental benefit of a special kind of investment in renewable energy projects – Corporate Renewable Power Purchase Agreement (PPA). The proposed methods were implemented on the case study of The Pennsylvania State University in central Pennsylvania, United States. The general version of the multi-objective optimization problem required making significant assumptions that reduced the computation complexity. The study explored the uncertainty in future Wholesale Electricity Prices, which was assumed to be the source of electricity for the investors of these renewable energy projects had there been no investments made. The use of Binomial Lattice Pricing Model, Monte Carlo Simulation, and Unit Commitment produced the feasible solutions of the multi-objective optimization problem in which the corresponded Pareto Set was identified. The simplified version of the proposed multi-objective optimization problem was reduced into several Single-objective optimization problems of the economic benefits of PPA investments, in which they also represent some Real Option Valuation problems under specific conditions. While making other assumptions to maintain the tractability of these problems, the optimal solutions of the Single-objective optimization problem and the Value of Options were identified. One of these Single-objective optimization problem monetized the environmental benefits of PPA investments using Social Cost of Carbon publishe

关键词： multi-objective optimization problem Single-objective optimization problem Real Option Valuation Power Purchase Agreement Greenhouse Gas Emission Wholesale Electricity Price Binomial Lattice Pricing Method Unit Commitment Monte Carlo Simulation Single-objective optimization problem Real Option Valuation Power Purchase Agreement Greenhouse Gas Emission Wholesale Electricity Price Binomial Lattice Pricing Method Unit Commitment Monte Carlo Simulation

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Predator-Prey Evolution Strategy for Solving multi-objective optimization problems

Predator-Prey Evolution Strategy for Solving Multi-Objective...

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2012电子信息与电气工程国际学术会议(EIEE2012)

作者： André A. Keller Laboratoire d'Informatique Fondamentale de Lille/SMAC Université de Lille Nord de France Villeneuve d'Ascq

ISBN: (纸本)9789078677505

Thispaper introduces to a particular algorithm inspired from Nature for solving multi-objective optimization problems. The predator-prey strategy is used for finding Paretooptimal fronts. This is the first essential step in the decision-making process, in which the decision makers are confronted to multiple objectives. The computations have been carried out by using software packages, such as Wolfram Mathematica and the GA-based optimization software packages GENOCOP Ⅲ and NSGA Ⅱ 1 .

关键词： multi-objective optimization problem Predator-prey strategy Pareto-optimal front

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multi-objective uplink data transmission optimization for edge computing in UAV-assistant mobile wireless sensor networks

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JOURNAL OF SYSTEMS ARCHITECTURE 2022年 132卷

作者： Li, Jiahui Sun, Geng Liang, Shuang Wang, Yuxin Wang, Aimin Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat Knowledge Engn Minist Educ Changchun 130012 Peoples R China

Unmanned aerial vehicles (UAVs) have attracted growing attention in enhancing the performance of mobile wireless sensor networks (MWSNs) since they can act as aerial servers (ASs) and have the autonomous nature to collect data for edge computing. In this paper, we consider to construct a virtual antenna array (VAA) consists of mobile sensor nodes (MSNs) and adopt collaborative beamforming (CB) to achieve long-distance and efficient uplink data transmissions with the ASs. First, we formulate a low interference and high-performance uplink transmission multi-objective optimization problem (UTMOP) of the CB-based UAV-assisted MWSN to simultaneously improve the total transmission rates, suppress the total maximum sidelobe levels (SLLs) and reduce the total propulsion energy consumptions of MSNs by jointly optimizing the positions and excitation current weights of MSN-enabled VAA and the order of communicating with different ASs. Then, we propose an improved non-dominated sorting genetic algorithm-III (INSGA-III) with chaos initialization, average grade mechanism and hybrid-solution generate strategy to solve the problem. Simulation results verify that the proposed algorithm can effectively solve the formulated UTMOP, and it has better performance than some other benchmark methods and peer algorithms.

关键词： Mobile wireless sensor networks Unmanned aerial vehicles Collaborative beamforming Non-dominated sorting genetic algorithm-III multi-objective optimization problem

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