检索结果-内蒙古大学图书馆

Tradeoff Analysis of Unintentional Interference and Communication Rate of UAV

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第4期74卷 5941-5953页

作者： Lu, Yongguang Wang, Kehao Liu, Pei Chen, Lin Shin, Hyundong Quek, Tony Q. S. Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Kyung Hee Univ Dept Elect Engn Yongin 17104 South Korea Singapore Univ Technol & Design Informat Syst Technol & DesignPillar Singapore 487372 Singapore

With the development of unmanned aerial vehicle (UAV) technology, using UAVs as aerial communication platforms to provide additional communication services is a promising technology. However, due to the scarcity of spectrum resources, UAV will inevitably causes unintentional interference to primary users (PUs) when transmitting data to secondary users (SUs). Aiming at this problem, with the help of the multi-objective optimization problem (MOOP) framework, this paper studies the trade-off between UAV's communication rate and UAV's unintentional interference by optimizing UAV's trajectory, UAV's transmission power, and SU's scheduling. Specifically, we consider a cognitive UAV communication scenario that a single UAV needs to transmit data to multiple SUs in a shared spectrum scenario. During this process, if the existing PUs receive unintentional interference from the UAV does not exceed a given threshold which is also called interference temperature (IT), it will be regarded as a normal communication state. Then, a MOOP is established that includes maximizing the minimum UAV-SU communication rate and maximizing the times that PUs are in normal communication states. Finally, with the help of $\epsilon -constraint$ method and stochastic submodular optimization algorithm, we propose an iterative algorithm to obtain the approximate Pareto front. The tradeoff between UAV's communication rate and UAV's unintentional interference is analyzed through numerical simulation.

关键词： UAV cognitive communication UAV uninten-tional interference trajectory optimization communication design communication design multi-objective optimization problem multi-objective optimization problem multi-objective optimization problem

来源：评论

学校读者我要写书评

暂无评论

optimization of high-dimensional expensive multi-objective problems using multi-mode radial basis functions

引用

COMPLEX & INTELLIGENT SYSTEMS 2025年第2期11卷 1-22页

作者： Shen, Jiangtao Wang, Xinjing He, Ruixuan Tian, Ye Wang, Wenxin Wang, Peng Wen, Zhiwen Northwestern Polytech Univ Sch Marine Sci & Technol Youyi West Rd Xian 710072 Shaanxi Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Minist Educ Key Lab Intelligent Comp & Signal Proc Jiulong Rd Hefei 230601 Anhui Peoples R China Xian Precis Machinery Res Inst Jinye Rd Xian 710072 Shaanxi Peoples R China

Numerous surrogate-assisted evolutionary algorithms are developed for multi-objective expensive problems with low dimensions, but scarce works have paid attention to that with high dimensions, i.e., generally more than 30 decision variables. In this paper, we propose a multi-mode radial basis functions-assisted evolutionary algorithm (MMRAEA) for solving high-dimensional expensive multi-objective optimization problems. To improve the reliability, the proposed algorithm uses radial basis functions based on three modes to cooperate to provide the qualities and uncertainty information of candidate solutions. Meanwhile, bi-population based on competitive swarm optimizer and genetic algorithm are applied for better exploration and exploitation in high-dimensional search space. Accordingly, an infill criterion based on multi-mode of radial basis functions that comprehensively considers the quality and uncertainty of candidate solutions is proposed. Experimental results on widely-used benchmark problems with up to 100 decision variables demonstrate the effectiveness of our proposal. Furthermore, the proposed method is applied to the structure optimization of the blended-wing-body underwater glider (BWBUG) and gets impressive solutions.

关键词： multi-objective optimization problem High-dimensional Expensive optimization Surrogate ensemble Structure design of BWBUG

来源：评论

学校读者我要写书评

暂无评论

A multi-objective approach for the integrated planning of drone and robot assisted truck operations in last-mile delivery

引用

EXPERT SYSTEMS WITH APPLICATIONS 2025年 269卷

作者： Mokhtari-Moghadam, Ali Salhi, Abdellah Yang, Xinan Nguyen, Trung Thanh Pourhejazy, Pourya Liverpool John Moores Univ Sch Engn Liverpool L3 England Univ Essex Sch Math Stat & Actuarial Sci Colchester CO4 3SQ England UiT Arctic Univ Norway Dept Ind Engn N-8514 Narvik Norway

Supply chains are experiencing a major transition driven by changing customer expectations, environmental concerns, and technological development. Considering the surge in e-commerce since the pandemic, the path forward for affordable, responsive supply chains is autonomous last-mile delivery. Drone and robot technologies complement the last-mile delivery's operational requirements and hence should be incorporated to assist truck deliveries. This study develops a bi-objective optimization framework for the integrated planning of Drone- And-Robot-assisted Truck (DART) delivery operations to minimize total delivery cost and maximize customer satisfaction considering a soft time window. Three models, including DART, drone-, and robot-assisted trucks are compared considering different operational situations. The results show that the DART delivery mode outperforms with an increase in the number of demand points. DART is particularly preferred when there is a moderate combination of high-density and distant demand points in last-mile delivery. Numerical experiments confirmed that the robot-assisted delivery model brings about cost-effectiveness in heavily populated areas. On the other hand, the drone-assisted truck model stands out in situations where there is a small number of demand points with high dispersity.

关键词： multi-objective optimization problem Robot Drone Last mile delivery E-commerce Logistics

来源：评论

学校读者我要写书评

暂无评论

A hybrid evolutionary algorithm with adaptive multi-population strategy for multi-objective optimization problems

引用

SOFT COMPUTING 2017年第20期21卷 5975-5987页

作者： Wang, Hongfeng Fu, Yaping Huang, Min Huang, George Wang, Junwei Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Hong Kong Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Liaoning Peoples R China

In this paper, a new multi-objective evolutionary algorithm (MOEA) named hybrid MOEA with adaptive multi-population strategy (HMOEA-AMP) is proposed for multi-objective optimization problems (MOPs).In the framework of HMOEA-AMP, the particle swarm optimization and differential evolution are hybridized to guide the exploitation of the Pareto optimal solutions and the exploration of the optimal distribution of the achieved solutions, respectively. multiple subpopulations are constructed in an adaptive fashion according to a number of scalar subproblems, which are decomposed from a MOP through a set of predefined weight vectors. Comprehensive experiments using a set of benchmark are conducted to investigate the performance of HMOEA-AMP in comparison with several state-of-the-art MOEAs. The experimental results show the advantage of the proposed algorithm.

关键词： Evolutionary multi-objective optimization Hybrid evolutionary algorithm multi-objective optimization problem Particle swarm optimization Differential evolution multi-population

来源：评论

学校读者我要写书评

暂无评论

Two efficient algorithms for constructing almost even approximations of the Pareto front in multi-objective optimization problems

引用

ENGINEERING optimization 2019年第4期51卷 567-589页

作者： Dolatnezhadsomarin, Azam Khorram, Esmaile Amirkabir Univ Technol Dept Math & Comp Sci Tehran Iran

In this article, two algorithms are proposed for constructing almost even approximations of the Pareto front of multi-objective optimization problems. The first algorithm is a hybrid of the epsilon-constraint and Pascoletti-Serafini scalarization methods for solving bi-objective problems. The second is a modification of the successive Pareto optimization (SPO) algorithm for solving three-objective problems. In these algorithms, the MATLAB fmincon solver is used to solve single-objective optimization problems, which returns a local optimal solution. Some metrics are considered to evaluate the quality of approximations obtained by the suggested algorithms on six test problems, and their results are compared with other algorithms (normal constraint, weighted constraint, SPO, differential evolution, multi-objective evolutionary algorithm/decomposition-differential evolution, non-dominated sorting genetic algorithm-II and S-metric selection evolutionary multi-objective algorithm). Experimental results show that the proposed algorithms provide almost even approximations of the whole Pareto front, and better quality of approximation and CPU time compared with established algorithms.

关键词： multi-objective optimization problem Pareto-optimal solution Pascoletti-Serafini scalarization approach epsilon-constraint method

来源：评论

学校读者我要写书评

暂无评论

An Efficient Marine Predators Algorithm for Solving multi-objective optimization problems: Analysis and Validations

引用

IEEE ACCESS 2021年 9卷 42817-42844页

作者： Abdel-Basset, Mohamed Mohamed, Reda Mirjalili, Seyedali Chakrabortty, Ripon K. Ryan, Michael Zagazig Univ Fac Comp & Informat Zagazig 44519 Egypt Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Fortitude Valley Qld 4006 Australia UNSW Canberra Sch Engn & Informat Technol Capabil Syst Ctr Campbell ACT 2612 Australia

Recently, a new strong optimization algorithm called marine predators algorithm (MPA) has been proposed for tackling the single-objective optimization problems and could dramatically fulfill good outcomes in comparison to the other compared algorithms. Those dramatic outcomes, in addition to our recently-proposed strategies for helping meta-heuristic algorithms in fulfilling better outcomes for the multi-objective optimization problems, motivate us to make a comprehensive study to see the performance of MPA alone and with those strategies for those optimization problems. Specifically, This paper proposes four variants of the marine predators' algorithm (MPA) for solving multi-objective optimization problems. The first version, called the multi-objective marine predators' algorithm (MMPA) is based on the behavior of marine predators in finding their prey. In the second version, a novel strategy called dominance strategy-based exploration-exploitation (DSEE) recently-proposed is effectively incorporated with MMPA to relate the exploration and exploitation phase of MPA to the dominance of the solutions-this version is called M-MMPA. DSEE counts the number of dominated solutions for each solution-the solutions with high dominance undergo an exploitation phase;the others with small dominance undergo the exploration phase. The third version integrates M-MMPA with a novel strategy called Gaussian-based mutation, which uses the Gaussian distribution-based exploration and exploitation strategy to search for the optimal solution. The fourth version uses the Nelder-Mead simplex method with M-MMPA (M-MMPA-NMM) at the start of the optimization process to construct a front of the non-dominated solutions that will help M-MMPA to find more good solutions. The effectiveness of the four versions is validated on a large set of theoretical and practical problems. For all the cases, the proposed algorithm and its variants are shown to be superior to a number of well-known multi-objectiv

关键词： optimization Evolutionary computation Heuristic algorithms Pareto optimization Search problems Convergence Australia multi-objective optimization problem dominance strategy-based exploration-exploitation Gaussian-based mutation Nelder-Mead simplex marine predators algorithm

来源：评论

学校读者我要写书评

暂无评论

A novel approach for the solution of multiobjective optimization problem using hesitant fuzzy aggregation operator

引用

RAIRO-OPERATIONS RESEARCH 2022年第1期56卷 275-292页

作者： Ahmad, Firoz Adhami, Ahmad Yusuf John, Boby Reza, Amit Indian Stat Inst SQC & OR Unit Bangalore Karnataka India Indian Inst Technol Gandhinagar Gandhinagar Gujarat India Aligarh Muslim Univ Dept Stat & Operat Res Aligarh Uttar Pradesh India

Many decision-making problems can solve successfully by traditional optimization methods with a well-defined configuration. The formulation of such optimization problems depends on crisply objective functions and a specific system of constraints. Nevertheless, in reality, in any decision-making process, it is often observed that due to some doubt or hesitation, it is pretty tricky for decision-maker(s) to specify the precise/crisp value of any parameters and compelled to take opinions from different experts which leads towards a set of conflicting values regarding satisfaction level of decision-maker(s). Therefore the real decision-making problem cannot always be deterministic. Various types of uncertainties in parameters make it fuzzy. This paper presents a practical mathematical framework to reflect the reality involved in any decision-making process. The proposed method has taken advantage of the hesitant fuzzy aggregation operator and presents a particular way to emerge in a decision-making process. For this purpose, we have discussed a couple of different hesitant fuzzy aggregation operators and developed linear and hyperbolic membership functions under hesitant fuzziness, which contains the concept of hesitant degrees for different objectives. Finally, an example based on a multiobjective optimization problem is presented to illustrate the validity and applicability of our proposed models.

关键词： Decision-making problem multi-objective optimization problem hesitant fuzzy membership function hesitant fuzzy aggregation operator

来源：评论

学校读者我要写书评

暂无评论

Generation of Pareto optimal solutions for multi-objective optimization problems via a reduced interior-point algorithm

引用

JOURNAL OF TAIBAH UNIVERSITY FOR SCIENCE 2018年第5期12卷 514-519页

作者： El-Sobky, B. Abo-Elnaga, Y. Alexandria Univ Fac Sci Dept Math & Comp Sci Alexandria Egypt Higher Technol Inst Dept Basic Sci Tenth Of Ramadan City Egypt

In this paper, a reduced interior-point (RIP) algorithm is introduced to generate a Pareto optimal front for multi-objective constrained optimization (MOCP) problem. A weighted Tchebychev metric approach is used together with achievement scalarizing function approach to convert MOCP problem to a single-objective constrained optimization (SOCO) problem. An active-set technique is used together with a Coleman-Li scaling matrix and a decrease interior-point method to solve SOCO problem. A Matlab implementation of RIP algorithm was used to solve three cases and application. The results showed that the RIP algorithm is promising when compared with well-known algorithms and the computations may be superior relevant for comprehending real-world application problems.

关键词： multi-objective optimization problem weighted Tchebychev metric approach active set Coleman-Li scaling matrix interior-point method

来源：评论

学校读者我要写书评

暂无评论

Solving general convex quadratic multi-objective optimization problems via a projection neurodynamic model

引用

COGNITIVE NEURODYNAMICS 2024年第4期18卷 2095-2110页

作者： Jahangiri, Mohammadreza Nazemi, Alireza Shahrood Univ Technol Fac Math Sci POB ***-316 Shahrood Iran

A neural network model is constructed to solve convex quadratic multi-objective programming problem (CQMPP). The CQMPP is first converted into an equivalent single-objective convex quadratic programming problem by the mean of the weighted sum method, where the Pareto optimal solution (POS) are given by diversifying values of weights. Then, for given various values weights, multiple projection neural networks are employded to search for Pareto optimal solutions. Based on employing Lyapunov theory, the proposed neural network approach is established to be stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the single-objective problem. The simulation results also show that the presented model is feasible and efficient.

关键词： multi-objective optimization problem Neural networks Convex quadratic programming problem Pareto optimal solution Stability Convergence

来源：评论

学校读者我要写书评

暂无评论

Solving multi-objective optimization problems using Differential Evolution Algorithm with Different Population Initialization Techniques 7

Solving Multi-Objective Optimization Problems using Differen...

引用

7th International Conference on Computing, Communications and Informatics (ICACCI)

作者： Devika, K. Jeyakumar, G. Amrita VishwaVidyapeetham Amrita Sch Engn Dept Comp Sci & Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9781538653142

The researchers of Evolutionary Computing (EC) community proposing new and different algorithmic strategies to tackle the increasing issues in handling optimization problems. As the number of objectives in an optimization problem increases the algorithmic complexity in solving the problem also increases. The way the initial population for an optimization problem generated is greatly affecting the performance of the Evolutionary Algorithms (EAs). This paper investigates the performance of Differential Evolution (DE) in solving Mutli-objective optimization problems (MOOP) with two different population initialization (PI) techniques. The performance of different instances of DE is compared based on the solution accuracy obtained. The results obtained shows that DE shows different performance for different PI techniques.

关键词： multi-objective optimization problem Differential Evolution Population Initialization Evolutionary Algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：