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Energy and Spectral Efficiency Tradeoff via Rate Splitting and Common Beamforming Coordination in multicell Networks

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IEEE TRANSACTIONS ON COMMUNICATIONS 2020年第12期68卷 7719-7731页

作者： Zhang, Jinghua Zhang, Jia Zhou, Yong Ji, Hui Sun, Jiande Al-Dhahir, Naofal Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China Univ Texas Dallas Elect & Comp Engn Dept Richardson TX 75080 USA ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China

Rate splitting (RS) has the potential to significantly enhance both energy efficiency (EE) and spectral efficiency (SE) of wireless networks. In this paper, we propose joint design of the beamforming and rate allocation to maximize both EE and SE of a downlink multicell multiple-input single-output (MISO) system with rate splitting and common beamforming coordination (RS-CBC). This design problem is formulated as a non-convex quadratically-constrained multi-objective optimization problem (MOOP). By investigating the quasi-concavity relationship between EE and SE, the formulated MOOP is transformed into a single-objective optimization problem (SOOP) to offer a tradeoff between EE and SE by maximizing EE in any achievable SE region. A series of transformations are then applied to make the SOOP tractable, after which an efficient iterative algorithm based on successive convex approximation (SCA) is proposed to solve the problem. Simulation results demonstrate the effectiveness of the proposed algorithm and unveil interesting tradeoffs between EE and SE under different parameter settings.

关键词： Array signal processing Resource management optimization Downlink Interference Wireless communication NOMA Energy efficiency spectral efficiency rate splitting common beamforming coordination multi-objective optimization problem multicell networks

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An Interference Avoidance Method Using Two Dimensional Genetic Algorithm for multicarrier Communication Systems

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JOURNAL OF COMMUNICATIONS AND NETWORKS 2013年第5期15卷 486-495页

作者： Huynh, Chuyen Khoa Lee, Won Cheol Soongsil Univ Sch Elect Seoul South Korea

In this article, we suggest a two-dimensional genetic algorithm (GA) method that applies a cognitive radio (CR) decision engine which determines the optimal transmission parameters for multicarrier communication systems. Because a CR is capable of sensing the previous environmental communication information, CR decision engine plays the role of optimizing the individual transmission parameters. In order to obtain the allowable transmission power of multicarrier based CR system demands interference analysis a priori, for the sake of efficient optimization, a two-dimensional GA structure is proposed in this paper which enhances the computational complexity. Combined with the fitness objective evaluation standard, we focus on two multi-objective optimization methods: The conventional GA applied with the multi-objective fitness approach and the non-dominated sorting GA with Pareto-optimal sorting fronts. After comparing the convergence performance of these algorithms, the transmission power of each subcarrier is proposed as non-interference emission with its optimal values in multicarrier based CR system.

关键词： Cognitive radio decision engine genetic algorithm (GA) interference analysis multi-objective optimization problem non-dominated sorting genetic algorithm (NSGA)

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Development of a framework for the sustainability evaluation of renewable and fossil fuel power plants using integrated LCA-emergy analysis: A case study in Iran

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RENEWABLE ENERGY 2021年 179卷 1548-1564页

作者： Alizadeh, Sadegh Avami, Akram Sharif Univ Technol Dept Energy Engn Energy Syst Engn Grp Azadi AvePOB 14565-114 Tehran Iran

This study presents a comprehensive framework for evaluating renewable and non-renewable power plants' performance using the Life Cycle Analysis (LCA) and emergy analysis. The emergy analysis is used to consider the free ecosystem services in the sustainability of the systems as a supplement to the LCA. The results indicate that the wind and photovoltaic power plants have the best performance in terms of the LCA analysis, while the wind and combined cycle power plants have the highest emergy sustain ability index. The best scenario is chosen under a two-objective optimization problem, including the single score and emergy sustainability as objective functions. Here, the wind power plant is the most interesting option while the combined cycle power plant with CO2 capture is the least interesting alternative. In this study, a novel framework is developed to assess the Cost of Avoided Carbon emissions (CACe) using the integrated LCA-emergy analysis for the combined cycle power plant in which its value is evaluated to be about $151.65/ton of CO2. Eventually, an uncertainty analysis is performed on the solar transformities using Monte Carlo simulation. The framework presented in this paper provides insights to increase power plants' sustainability for managers and authorities. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Emergy sustainability Life cycle analysis multi-objective optimization problem NSGA-II method Pollution control strategies Renewable power plants

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An efficient top-k ranking method for service selection based on ε-ADMOPSO algorithm

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NEURAL COMPUTING & APPLICATIONS 2019年第1-Sup期31卷 77-92页

作者： Yu, Wei Li, Shijun Tang, Xiaoyue Wang, Kai Wuhan Univ Comp Sch Wuhan 430072 Hubei Peoples R China Wuhan Polytech Univ Sch Math & Comp Sci Wuhan 430023 Hubei Peoples R China SUNY Binghamton Dept Comp Sci Binghamton NY 13902 USA

One of the main concerns in rank aggregation tasks for metasearch service is how to retrieve and aggregate the large-scale candidate search results efficiently. Much work has been done to implement metasearch service engines with different rank aggregation algorithms. However, the performance of these metasearch engines can hardly be improved. In this paper, we transform the top-k ranking task into a multi-objective programming problem when user preferences are considered along with user queries. We build an improved discrete multi-objective programming model to make the aggregate rankings satisfy user queries and user preferences both, and then propose a user preferences-based rank aggregation algorithm accordingly. Based on discrete particle swarm optimization algorithm, we improve the encoding scheme, the initialization methods, the position and velocity definition, the integrating updating process, the turbulence operator, and the external archive updating and leader selection strategy to make sure the candidate results that fit the user's preferences can be located quickly and accurately in a large-scale discrete solution space. We have our proposed algorithm tested on three different benchmark datasets: a public dataset, the real-world datasets and the synthetic simulation datasets. The experimental results demonstrate the efficacy and convergence efficiency of the proposed algorithm over the baseline rank aggregation methods especially when dealing with large amount of candidate results. And when the set of candidate results is of normal size, the proposed algorithm is proved to perform not worse than the baseline methods.

关键词： Top-k ranking IOT metasearch User preference multi-objective optimization problem Particle swarm optimization

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QoS and channel state aware load balancing in 3GPP LTE multi-cell networks

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Science China(Information Sciences) 2013年第4期56卷 91-102页

作者： LI ZhiHang WANG Hao PAN ZhiWen LIU Nan YOU XiaoHu National Mobile Communications Research Laboratory School of Information Science and EngineeringSoutheast University

In this paper,we investigate the load balancing problem with the consideration of different qualityof-service(QoS) and channel state in 3GPP long term evolution(LTE) multi-cell *** imbalance among nearby cells often causes inefficient utilization of the system *** in overloaded cells,users cannot get satisfied services for short resources,while in idle cells,there is plenty of unoccupied ***,switching users only for load balancing with no consideration of channel state often results in aggressive handover of users,which may occupy too many resources in their target cells and deteriorate the network ***,how to efficiently perform load balancing with the consideration of channel state becomes an important ***,LTE network aims to serve users with different QoS requirements,which should be taken into account when performing load *** we propose a multi-objective optimization problem,whose objectives are load balancing index and network average load for users with QoS requirements and a unified utility function for users without QoS *** constraints are physical resource limits and users’ QoS *** we analyze its complexity and propose a practical algorithm,load balancing,with the consideration of different QoS requirements and channel state services(LBQC).Extensive simulations are *** results show that the proposed algorithm can approximate the optimal algorithm efficiently,and leads to significantly better performances than load balancing with no consideration of channel state,e.g.a lower new call blocking rate with a fewer system resources occupation for users with QoS requirements,a higher cell edge throughput and total throughput for users without QoS requirements.

关键词： load balancing channel state quality of service(QoS) multi-objective optimization problem total utility

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Tradeoff Analysis and Joint optimization of Link-Layer Energy Efficiency and Effective Capacity Toward Green Communications

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2016年第5期15卷 3339-3353页

作者： Yu, Wenjuan Musavian, Leila Ni, Qiang Univ Lancaster Sch Comp & Commun InfoLab21 Lancaster LA1 4WA England

A joint optimization problem of link-layer energy efficiency (EE) and effective capacity (EC) in a Nakagami-m fading channel under a delay-outage probability constraint and an average transmit power constraint is considered and investigated in this paper. First, a normalized multi-objective optimization problem (MOP) is formulated and transformed into a single-objective optimization problem (SOP), by applying the weighted sum method. The formulated SOP is then proved to be continuously differentiable and strictly quasiconvex in the optimum average input power, which turns out to be a cup shape curve. Furthermore, the weighted quasiconvex tradeoff problem is solved by first using Charnes-Cooper transformation and then applying Karush-Kuhn-Tucker (KKT) conditions. The proposed optimal power allocation, which includes the optimal strategy for the link-layer EE-maximization problem and the EC-maximization problem as extreme cases, is proved to be sufficient for the Pareto optimal set of the original EE-EC MOP. Moreover, we prove that the optimum average power level monotonically decreases with the importance weight, but strictly increases with the normalization factor, the circuit power and the power amplifier efficiency. Simulation results confirm the analytical derivations and further show the effects of fading severeness and transmission power limit on the tradeoff performance.

关键词： Quality-of-service delay-outage probability constraint effective capacity energy efficiency multi-objective optimization problem weighted sum method

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Welding robot path planning problem based on discrete MOEA/D with hybrid environment selection

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NEURAL COMPUTING & APPLICATIONS 2021年第19期33卷 12881-12903页

作者： Zhou, Xin Wang, Xuewu Gu, Xingsheng East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China

Welding robot path planning gradually has increasingly widespread attention in automatic production on account of improving the production efficiency in the actual production process. It is a combinational optimization problem to find an optimal welding path for the robot manipulator by arranging the sequence and directions of welding seams. To solve the problem with two objectives, path length and energy consumption, this paper proposed an improved discrete MOEA/D based on a hybrid environment selection (DMOEA/D-HES) with a parallel scheme to search the optimal sequence and directions simultaneously for welding seams. The discretized reproduction and adaptive neighborhood provide a larger search range in solution space to overcome difficulties in duplication and uneven distribution of solutions. Adaptive decomposition method and improved hybrid environment selection promote solutions converge to the optimal direction and further balance convergence and diversity. Eight TSPLIB problems were tested with the proposed algorithm and the other four algorithms. Besides, the algorithm is compared with four multi-objective evolutionary algorithms (MOEAs) on the multi-objective welding robot path planning on the balance beam. The test results indicate DMOEA/D-HES outperforms other algorithms on convergence with a competitive diversity, which is effective to be applied in the actual welding process.

关键词： MOEA D Welding robot Path planning multi-objective optimization problem Adaptive decomposition method Discrete reproduction

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A comparative study of novel object-based geostatistical algorithm and direct sampling method on fracture network modeling

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2023年第2期37卷 777-793页

作者： Shakiba, Sima Ardejani, Faramarz Doulati Univ Tehran Coll Engn Sch Min Tehran Iran Univ Tehran Mine Environm & Hydrogeol Res Lab MEHR Lab Tehran Iran

Accurate discrete fracture network modeling is a significant requirement for fluid flow simulation in various applications such as managing groundwater resources, simulating oil and gas reservoirs, and modeling geothermal energy resources. The existing fracture network modeling approaches are often unsuccessful in regenerating spatial variability and can only characterize the fracture geometries by statistical probability distributions. In addition, the alternative geostatistical methods to address these limitations suffer from a smoothing effect and reproducing fracture patterns due to the use of the two-point statistics technique. In this paper, a comparative study between the new object-based iterative fracture network modeling algorithm and the geostatistical direct sampling (DS) method is performed. The presented algorithm starts with an initial configuration to directly model the statistical geometry of the fracture network and uses particle swarm optimization algorithm to impose four different constraints and include its spatial variability. Each constraint is defined in the form of the difference between spatial properties of the reference configuration and of the generated model using L2-norm criterion characterized by common specific filtering functions in the image processing. Both employed methods are applied on a real 2-Dimentional fracture network image from an exposed wall and their performance is assessed by four different criteria including classification correctness rate (CCR), indicator variogram (gamma), degree of consistency (r), and average relative error (ARE). Results show the superiority of the presented algorithm over the DS method in regenerating the real fracture network configuration with CCR = 0.99, r = 0.994, ARE = 2.63, and the same indicator variogram function.

关键词： Discrete fracture network Spatial characteristics Direct sampling (DS) simulation multi-objective optimization problem Particle swarm optimization

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A multi-objective Evolutionary Algorithm With Hierarchical Clustering-Based Selection

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IEEE ACCESS 2023年 11卷 2557-2569页

作者： Zhou, Shenghao Chen, Ze Li, Qi Gu, Mengjun Bao, Zhoucheng He, Wenda Sheng, Weiguo Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou 311121 Peoples R China Hangzhou Normal Univ Engn Res Ctr Mobile Hlth Management Syst Hangzhou 311121 Peoples R China China Telecom Corp Ltd Zhejiang Branch Hangzhou 310022 Peoples R China

This paper proposes an evolutionary algorithm with hierarchical clustering based selection for multi-objective optimization. In the proposed algorithm, a hierarchical clustering is employed to design the environmental and mating selections, named local coverage selection and local area selection, respectively, for multi-objective evolutionary algorithm. The local coverage selection strategy aims to preserve well-distributed individuals with good convergence. While, the local area selection strategy is devised to deliver a balanced evolutionary search. This is achieved by encouraging individuals for exploration or exploitation according to the I-epsilon+ indicator. In both strategies, a hierarchical clustering method is employed to discover the population structure. Based on the clustering results, in local coverage selection, the individuals of different clusters will be retained according to their coverage areas and crowding distances, such that distributing as evenly as possible in the Pareto front. In local area selection, the individual(s) with the best value of I-epsilon+ indicator in each cluster will be selected to perform mating, with the purpose of achieving a balanced exploration and exploitation. The proposed algorithm has been evaluated on 26 bench-mark problems and compared with related methods. The results clearly show the significance of the proposed method.

关键词： multi-objective evolutionary algorithm multi-objective optimization problem hierarchical clustering

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An adaptive population multi-objective quantum-inspired evolutionary algorithm for multi-objective 0/1 knapsack problems

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INFORMATION SCIENCES 2013年 243卷 39-56页

作者： Lu, Tzyy-Chyang Yu, Gwo-Ruey Natl Chung Cheng Univ Adv Inst Mfg High Tech Innovat Chiayi Taiwan Natl Chung Cheng Univ Dept Elect Engn Adv Inst Mfg High Tech Innovat Chiayi Taiwan

The multi-objective quantum-inspired evolutionary algorithm (MQEA) is a relatively recent technique for solving multi-objective optimization problems (MOPs). In the MQEA, quantum bit (Q-bit) individuals are classified into several groups, with each group assigned one objective solution (one of the non-dominated solutions found so far) as the reference sign string. For a fixed population size, the number of Q-bit individuals assigned to each objective solution decreases with increasing number of found non-dominated solutions. As a result, more or fewer Q-bit individuals assigned to each objective solution may lead a confused order of local and global search. To mitigate this issue, an adaptive population MQEA (APMQEA) is proposed in this work. In the APMQEA, the number of Q-bit individuals assigned to each objective solution is fixed, and the population size is adaptively changed according to the number of found non-dominated solutions. Experimental results for the multi-objective 0/1 knapsack problem show that the APMQEA finds solutions close to the Pareto-optimal front and maintains a good spread of the non-dominated set. (C) 2013 Elsevier Inc. All rights reserved.

关键词： multi-objective quantum-inspired evolutionary algorithm multi-objective optimization problem Quantum bits multi-objective 0/1 knapsack problem

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