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multi-objective Energy Efficient Resource Allocation in Massive multiple Input multiple Output-Aided Heterogeneous Cloud Radio Access Networks

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IEEE ACCESS 2023年 11卷 33480-33497页

作者： Amani, Nahid Parsaeefard, Saeedeh Yanikomeroglu, Halim ICT Res Inst Dept Commun Technol Tehran *** Iran Univ Toronto Dept Elect & Comp Engn M5S Toronto ON Canada Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

In this work, a novel energy efficient multi-objective resource allocation algorithm for heterogeneous cloud radio access networks (H-CRANs) is proposed where the trade-off between increasing throughput and decreasing operation cost is considered. H-CRANs serve groups of users through femto-cell access points (FAPs) and remote radio heads (RRHs) equipped with massive multiple input multiple output (MIMO) connected to the base-band unit (BBU) pool via front-haul links with limited capacity. We formulate an energy-efficient multi-objective optimization (MOO) problem with a novel utility function. Our proposed utility function simultaneously improves two conflicting goals as total system throughput and operation cost. With this MOO, we jointly assign the sub-carrier, transmit power, access point (AP)(RRH/FAP), RRH, front-haul link, and BBU. To address the conflicting objectives, we convert the MOO problem into a single-object optimization problem using an elastic-constraint scalarization method. With this approach, we flexibly adjust trade-off parameters to choose between two objective functions. To propose an efficient algorithm, we deploy successive convex approximation (SCA) and complementary geometric programming (CGP) approaches. Finally, via simulation results we discuss how to select the values of trade-off parameters, and we study their effects on conflicting objective functions (i.e., throughput and operation cost in MOO problem). Simulation results also show that our proposed approach can offload traffic from C-RANs to FAPs with low transmit power and thereby reduce operation costs by switching off the under-utilized RRHs and BBUs. It can be observed from the simulation results that the proposed approach outperforms the traditional approach in which each user is associated to the AP (RRHs/FAPs) with the largest average value of signal strength. The proposed approach reduces operation costs by 30 % and increases throughput index by 25 % which in turn leads to

关键词： 5G multi-objective optimization problem elastic-constraints method

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A Distributed Power Control Algorithm for Energy Efficiency Maximization in Wireless Cellular Networks

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IEEE WIRELESS COMMUNICATIONS LETTERS 2020年第11期9卷 1975-1979页

作者： Aslani, Rojin Rasti, Mehdi Amirkabir Univ Technol Dept Comp Engn Tehran *** Iran

In this letter, we propose a distributed power control algorithm for addressing the global energy efficiency (GEE) maximization problem subject to satisfying a minimum target SINR for all user equipments (UEs) in wireless cellular networks. We state the problem as a multi-objective optimization problem which targets minimizing total power consumption and maximizing total throughput, simultaneously, while a minimum target SINR is guaranteed for all UEs. We propose an iterative scheme executed in the UEs to control their transmit power using individual channel state information (CSI) such that the GEE is maximized in a distributed manner. We prove the convergence of the proposed iterative algorithm to its corresponding unique fixed point also shown by our numerical results. Additionally, simulation results demonstrate that our proposed scheme outperforms other algorithms in the literature and performs like the centralized algorithm executed in the base station and maximizes the GEE using the global CSI.

关键词： Power control Throughput Power demand Cellular networks Interference Signal to noise ratio Minimization Distributed power control energy efficiency multi-objective optimization problem wireless cellular networks

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multi-objective False Data Injection Attacks of Cyber-Physical Power Systems

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第9期69卷 3924-3928页

作者： Lu, Kang-Di Wu, Zheng-Guang Zhejiang Univ Inst Cyber Syst & Control Hangzhou 310027 Peoples R China Chengdu Univ Inst Adv Study Chengdu 610106 Peoples R China Zhejiang Normal Univ Coll Math & Comp Sci Jinhua 321004 Zhejiang Peoples R China

In cyber-physical power systems (CPPSs), false data injection attack (FDIA) has drawn much attention due to its stealthiness. It is of great importance to investigate the potential behaviors of attackers to improve the cyber-security of CPPSs. However, most FDIA models are often constructed separately on the effect of attackers or the impact of attacks. Accordingly, this brief proposes a multi-objective stealthy FDIA scheme in AC grid model. The attack model is described as a multi-objective optimization problem, where minimization of contaminated measurements and maximization of the attack impact are considered as two objectives while remaining stealthy. To deal with the established attack model, a non-dominated sorting genetic algorithm II (NSGAII) is introduced as the solver. To improve the efficiency of generating attack vector, a new representation mechanism is proposed to describe locations and values of injected states. Additionally, during the evolutionary process, we propose a mutation operation to balance the sparsity and impact of FDIA. Simulation results on the IEEE 14-bus, 30-bus, and 118-bus systems demonstrate the feasibility of the NSGAII-based FDIA model.

关键词： Biological system modeling Pollution measurement Computational modeling Sociology optimization Mathematical models Sorting Cyber-physical power systems cyber-security false data injection attacks multi-objective optimization problem genetic algorithm

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An integrated relief network design model under uncertainty: A case of Iran

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SAFETY SCIENCE 2019年第1期111卷 22-36页

作者： Hasani, Aliakbar Mokhtari, Hadi Shahrood Univ Technol Dept Ind Engn & Management Shahrood Iran Univ Kashan Fac Engn Dept Ind Engn Kashan Iran

In this study, a scenario-based robust approach is suggested for a multi-objective mixed-integer linear programming model in designing the relief network. A new approach to humanitarian inventory grouping problem based on the relief management objectives is proposed. The proposed model simultaneously optimizes inventory groups' number and corresponding service levels, assignment of relief commodities to groups, relief facility location, and relief service assignment. The proposed model aims to minimize the risk and the total cost of network management and simultaneously maximize the network population coverage. The fault tree analysis technique is used for vulnerability assessment of each demand point. To tackle the proposed optimization model, a hybrid Taguchi-based non-dominated sorting genetic algorithm-II is developed that incorporates an enhanced variable decomposition neighborhood search algorithm with fitness landscape analysis as its local search heuristic. The results illustrate the efficiency of the proposed model and solution algorithm in dealing with the considered disaster management issues.

关键词： Relief network design Risk assessment multi-objective optimization problem Inventory grouping Hybrid meta-heuristic

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Parametric approach for multi-objective enhanced interval linear fractional programming problem

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ENGINEERING optimization 2024年第5期56卷 740-765页

作者： Patel, Mridul Behera, Jyotirmayee Kumar, Pankaj SRM Inst Sci & Technol Dept Math Chennai India

The design (decision) variables in the presented article of a multi-objective interval fractional optimization problem based on a linear function are assumed to take the form of a closed interval using the concept of the parametric form of an interval. The original problem is initially changed into equivalent multi-objective interval linear programming with the design variables as closed intervals. Further, it is made free from interval uncertainty by changing into a classical single-objective problem using the weighted-sum method. The solutions of the model are theoretically justified by its existence. Finally, a numerical example and a case study on the agricultural planting structure optimization problem with hypothetical data are presented to support the recommended technique for the model.

关键词： Fractional programming interval optimization multi-objective optimization problem linear programming problem interval analysis

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Time and Energy Minimization Communications Based on Collaborative Beamforming for UAV Networks: A multi-objective optimization Method

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2021年第11期39卷 3555-3572页

作者： Sun, Geng Li, Jiahui Liu, Yanheng Liang, Shuang Kang, Hui 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 vehicle (UAV) communications and networks are of utmost concern. However, they have challenges such as the limited on-board energy and restricted transmit power. In this paper, we study a UAV-enabled communication scenario that a set of UAVs perform a virtual antenna array (VAA) to communicate with different remote base stations (BSs) by using collaborative beamforming (CB). To achieve a better transmission performance, the UAV elements can fly to optimal positions by using optimal speeds and adjust to optimal excitation current weights for performing CB transmissions. However, there are some trade-offs between energy consumption and transmission performance. Thus, we formulate a time and energy minimization communication multi-objective optimization problem (TEMCMOP) of CB in UAV networks to simultaneously minimize the total transmission time, total performing time of VAAs and total motion and hovering energy consumptions of UAVs by jointly optimizing the positions, flight speeds and excitation current weights of UAVs, as well as the order of communicating with different BSs. Due to the complexity and NP-hardness of the formulated TEMCMOP, we propose an improved multi-objective ant lion optimization (IMOALO) algorithm with chaos-opposition based learning solution initialization and hybrid solution update operators to solve the problem. Simulation results verify that the proposed IMOALO can effectively solve the formulated TEMCMOP and it has better performance than some other benchmark approaches.

关键词： Energy consumption optimization Wireless communication Three-dimensional displays Task analysis Atmospheric modeling Minimization UAV communications collaborative beamforming energy efficiency multi-objective optimization problem swarm intelligence

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multi-objective Deployment optimization of UAVs for Energy-Efficient Wireless Coverage

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IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第6期72卷 3587-3601页

作者： Zhu, Xiumin Zhai, Linbo Li, Nianxin Li, Yumei Yang, Feng Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China

Recently, Unmanned Aerial Vehicles (UAVs) have attracted much attention due to their flexibility and low cost. However, there are limitations for multiple UAVs such as limited energy and collaborative coverage. To achieve a better coverage performance, each UAV needs to find the optimal position to cover many ground users while saving energy. However, there are trade-offs between coverage utility and energy consumption. In this paper, we study a multi-UAV communication scenario where multi-UAV array is deployed to provide wireless coverage for mobile ground users. Considering the number, 3D positions, and speeds of UAVs, we formulate a Coverage Utility and Energy multi-objective optimization problem (CUEMOP) to simultaneously maximize the total coverage utility and minimize the total energy consumption of UAVs. Due to the complexity and NP-hardness of the formulated CUEMOP, we propose Improved multi-objective Grey Wolf Optimizer (ImMOGWO) algorithm. In this algorithm, we design the Role Determination (RD) algorithm to cluster the ground users and prepare for initialization of UAV number and position. Hybrid solution initialization (HSI) algorithm is to initialize multi-dimensional variables and overcome algorithm inefficiency caused by random initialization. The Levy flight and Sin Cosine method based on the MOGWO algorithm (LSCMGA) is proposed to increase the diversity of solutions and ensure the convergence effect of the algorithm. Simulation results verify that proposed ImMOGWO algorithm has better performance than some other benchmark methods.

关键词： Autonomous aerial vehicles optimization Clustering algorithms Wireless communication Energy consumption Three-dimensional displays Base stations multiple UAVs multi-objective optimization problem coverage performance energy efficiency

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Covariance matrix adaptive strategy for a multi-objective evolutionary algorithm based on reference point

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第5期39卷 7315-7332页

作者： Wei, Lixin Zhang, JinLu Fan, Rui Li, Xin Sun, Hao Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao Hebei Peoples R China

In this article, an effective method, called an adaptive covariance strategy based on reference points (RPCMA-ES) is proposed for multi-objective optimization. In the proposed algorithm, search space is divided into independent sub-regions by calculating the angle between the objective vector and the reference vector. The reference vectors can be used not only to decompose the original multi-objective optimization problem into a number of single-objective subproblems, but also to elucidate user preferences to target a preferred subset of the whole Pareto front (PF). In this respect, any single objective optimizers can be easily used in this algorithm framework. Inspired by the multi-objective estimation of distribution algorithms, covariance matrix adaptation evolution strategy (CMA-ES) is involved in RPCMA-ES. A state-of-the-art optimizer for single-objective continuous functions is the CMA-ES, which has proven to be able to strike a good balance between the exploration and the exploitation of search space. Furthermore, in order to avoid falling into local optimality and make the new mean closer to the optimal solution, chaos operator is added based on CMA-ES. By comparing it with four state-of-the-art multi-objective optimization algorithms, the simulation results show that the proposed algorithm is competitive and effective in terms of convergence and distribution.

关键词： multi-objective optimization problem Reference point Covariance matrix adaptation evolutionary strategy Chaos operator

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Reduction of carbon emission and total late work criterion in job shop scheduling by applying a multi-objective imperialist competitive algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2018年第1期11卷 805-829页

作者： Piroozfard, Hamed Wong, Kuan Yew Tiwari, Manoj Kumar Univ Teknol Malaysia Fac Mech Engn Dept Mfg & Ind Engn Utm Skudai 81310 Johor Malaysia Indian Inst Technol Dept Ind & Syst Engn Kharagpur 721302 W Bengal India

New environmental regulations have driven companies to adopt low-carbon manufacturing. This research is aimed at considering carbon dioxide in the operational decision level where limited studies can be found, especially in the scheduling area. In particular, the purpose of this research is to simultaneously minimize carbon emission and total late work criterion as sustainability-based and classical-based objective functions, respectively, in the multiobjective job shop scheduling environment. In order to solve the presented problem more effectively, a new multiobjective imperialist competitive algorithm imitating the behavior of imperialistic competition is proposed to obtain a set of non-dominated schedules. In this work, a three-fold scientific contribution can be observed in the problem and solution method, that are: (1) integrating carbon dioxide into the operational decision level of job shop scheduling, (2) considering total late work criterion in multi-objective job shop scheduling, and (3) proposing a new multi-objective imperialist competitive algorithm for solving the extended multi-objective optimization problem. The elements of the proposed algorithm are elucidated and forty three small and large sized extended benchmarked data sets are solved by the algorithm. Numerical results are compared with two well-known and most representative metaheuristic approaches, which are multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II, in order to evaluate the performance of the proposed algorithm. The obtained results reveal the effectiveness and efficiency of the proposed multi-objective imperialist competitive algorithm in finding high quality non-dominated schedules as compared to the other metaheuristic approaches.

关键词： Job shop scheduling environmentally sustainable operations management carbon footprint late work criterion multi-objective imperialist competitive algorithm multi-objective optimization problem

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Adoption of an improved PSO to explore a compound multi-objective energy function in protein structure prediction

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APPLIED SOFT COMPUTING 2018年 72卷 539-551页

作者： Song, Shuangbao Ji, Junkai Chen, Xingqian Gao, Shangce Tang, Zheng Todo, Yuki Univ Toyama Fac Engn Toyama 9308555 Japan Kanazawa Univ Fac Elect & Comp Engn Kanazawa Ishikawa 9201192 Japan

The protein structure prediction (PSP) problem, i.e., predicting the three-dimensional structure of a protein from its sequence, remains challenging in computational biology. The inaccuracy of existing protein energy functions and the huge conformation search space make the problem difficult to solve. In this study, the PSP problem is modeled as a multi-objective optimization problem. A physics-based energy function and a knowledge-based energy function are combined to construct the three-objective energy function. An improved multi-objective particle swarm optimization coupled with two archives is employed to execute the conformation space search. In addition, a mechanism based on Pareto non-dominated sorting is designed to properly address the slightly worse solutions. Finally, the experimental results demonstrate the effectiveness of the proposed approach. A new perspective for solving the PSP problem by means of multi-objective optimization is given in this paper. (C) 2018 Published by Elsevier B.V.

关键词： multi-objective optimization problem Particle swarm optimization Knowledge-based energy function Non-dominated sorting Protein structure prediction

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