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Semantic and Trade-Off Aware Location Privacy Protection in Road Networks Via Improved multi-objective Particle Swarm optimization

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IEEE ACCESS 2021年 9卷 54264-54275页

作者： Tian, Cenxi Xu, Hongyun Lu, Tao Jiang, Rui Kuang, Yong South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

Location privacy protection is an essential but challenging topic in the field of network security. Although the existing research methods, such as k-anonymity, mix zone, and differential privacy, show significant success, they usually neglect the location semantic and the proper trade-off between privacy and utility, which may allow attackers to obtain user privacy information by revealing the semantic correlation between the anonymous region and user's real location, thus causing privacy leakage. To solve this problem, we propose a location privacy protection scheme based on the k-anonymity technique, which provides practical location privacy-preserving through generating an anonymous set. This paper proposes a new location privacy attack strategy termed semantic relativity attack (SRA), which considers the location semantic problem. Correspondingly, a semantic and trade-off aware location privacy protection mechanism (STA-LPPM) is presented to achieve privacy protection with both high-level privacy and utility. To be specific, we model the location privacy protection as a multi-objective optimization problem and propose the Improved multi-objective Particle Swarm optimization (IMOPSO) to generate the optimal anonymous set calculating the well-design fitness functions of the multi-objective optimization problem. In this way, the privacy scheme can provide mobile users with the right balance of privacy protection and service quality. Experiments reveal that our privacy scheme can effectively resist the semantic relativity attack while preventing significant utility degrading.

关键词： K-anonymity location privacy location semantic multi-objective optimization problem particle swarm optimization algorithm road networks

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A multi-objective Design Approach for the c Chart Considering Taguchi Loss Function

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2014年第8期30卷 1179-1190页

作者： Lupo, Toni Univ Palermo Dipartimento Ingn Chim I-90128 Palermo Italy

The present paper proposes a multi-objective design approach for the c chart, considering in the optimization process of the chart parameters both the statistical and the economic objectives. In particular, the minimization of the hourly total quality related costs is the considered objective to carry out the economic goal, whereas the statistical objective is reached by the minimization the out-of-control average run length of the chart. A mixed integer non-linear constrained mathematical model is formulated to solve the treated multi-objective optimization problem, whereas the Pareto optimal frontier is described by the epsilon-constraint method. In order to show the employment of the proposed approach, an illustrative example is developed and the related considerations are given. Finally, some sensitivity analysis is also performed to investigate the effects of operative and costs parameters on the chart performance. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： statistical process control Taguchi loss function multi-objective optimization problem epsilon-constraint method non-linear constrained mathematical programming Model Pareto optimal frontier

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Relay selection and power allocation for energy-efficient cooperative cognitive radio networks

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PHYSICAL COMMUNICATION 2018年 28卷 1-10页

作者： Soleimanpour-Moghadam, M. Talebi, S. Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

In this paper, we apply the innovative multi-objective optimization methods to the challenge posed by rate maximization, total transmission power minimization and relay selection in cooperative cognitive radio networks. The proposed methods which are based on amplify and forward relaying strategy optimize the three conflicting objectives and, at same time, they maximize the rate quality, minimize the total transmission power allocated to the network relays and make the best relay node selection. The multi-objective optimization studied is a non-convex non-linear combinatorial algorithm which is converted to its convex smooth equivalent through two efficient approximation methods. We apply the multi-objective lexicographic method to overcome the challenge posed by these conflicting objectives simultaneously. The proposed relay node selection method is based on zero-norm principle which provides an effective technique to obtain a minimum node selection. Simulation results confirm that the proposed approaches offer superior performance over known schemes in terms of throughput gain and number of active relays. (C) 2017 Published by Elsevier B.V.

关键词： Cooperative cognitive networks Relay selection Zero-norm multi-objective optimization problem Lexicographic method

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Design of particle-reinforced polyurethane mould materials for soft tooling process using evolutionary multi-objective optimization algorithms

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SOFT COMPUTING 2012年第6期16卷 989-1008页

作者： Nandi, Arup Kumar Datta, Shubhabrata Deb, Kalyanmoy Cent Mech Engn Res Inst CSIR CMERI Durgapur 713209 W Bengal India Birla Inst Technol Jasidih 814142 Deoghar India Indian Inst Technol Dept Mech Engn Kanpur 208016 Uttar Pradesh India

Polyurethane is used for making mould in soft tooling (ST) process for producing wax/plastic components. These wax components are later used as pattern in investment casting process. Due to low thermal conductivity of polyurethane, cooling time in ST process is long. To reduce the cooling time, thermal conductive fillers are incorporated into polyurethane to make composite mould material. However, addition of fillers affects various properties of the ST process, such as stiffness of the mould box, rendering flow-ability of melt mould material, etc. In the present work, multi-objective optimization of various conflicting objectives (namely maximization of equivalent thermal conductivity, minimization of effective modulus of elasticity, and minimization of equivalent viscosity) of composite material are conducted using evolutionary algorithms (EAs) in order to design particle-reinforced polyurethane composites by finding the optimal values of design parameters. The design parameters include volume fraction of filler content, size and shape factor of filler particle, etc. The Pareto-optimal front is targeted by solving the corresponding multi-objective problem using the NSGA-II procedure. Then, suitable multi-criterion decision-making techniques are employed to select one or a small set of the optimal solution(s) of design parameter(s) based on the higher level information of the ST process for industrial applications. Finally, the experimental study with a typical real industrial application demonstrates that the obtained optimal design parameters significantly reduce the cooling time in soft tooling process keeping other processing advantages.

关键词： multi-objective optimization problem Evolutionary algorithm Particle-reinforced polyurethane mould material Soft tooling process Cooling time

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multi-objective probabilistic reactive power and voltage control with wind site correlations

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ENERGY 2014年 66卷 810-822页

作者： Zare, Mohsen Niknam, Taher Azizipanah-Abarghooee, Rasoul Amiri, Babak Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Univ Sydney Fac Engn & IT Sydney NSW 2006 Australia

This paper proposes a multi-objective probabilistic reactive power and voltage control in distribution networks using wind turbines, hydro turbines, fuel cells, static compensators and load tap changing transforms. The objective functions are total electrical energy costs, the electrical energy losses, total emissions produced, and voltage deviations during the next day. Since the wind sources and load demand have intermittent characteristics, a probabilistic load flow based on 2m + 1 point estimated method is used to investigate the objective functions. The correlation in wind speed is considered as the distances between WTs are not large in distribution systems. Furthermore, a multi-objective modified bee swarm optimization is proposed to solve the optimization problem by defining a set of non-dominated points as the solutions. A fuzzy based clustering is used to control the size of the repository and a niching method is utilized to choose the best solution during the optimization process. Performance of the proposed algorithm is tested on a 69-bus distribution feeder. The results confirm the necessity of modeling the reactive power and voltage control problem in a stochastic framework. Also, the effects of wind site correlations on different objective functions are discussed completely. (c) 2014 Elsevier Ltd. All rights reserved.

关键词： Modified bee swarm optimization multi-objective optimization problem Point estimated methods Reactive power and voltage control Wind site correlation

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Improved RM-MEDA with local learning

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SOFT COMPUTING 2014年第7期18卷 1383-1397页

作者： Li, Yangyang Xu, Xia Li, Peidao Jiao, Licheng Xidian Univ Minist Educ China Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

In this paper, local learning is proposed to improve the speed and the accuracy of convergence performance of regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA), a typical multi-objective optimization algorithm via estimation of distribution. RM-MEDA employs a model-based method to generate new solutions, however, this method is easy to generate poor solutions when the population has no obvious regularity. To overcome this drawback, our proposed method add a new solution generation strategy, local learning, to the original RM-MEDA. Local learning produces solutions by sampling some solutions from the neighborhood of elitist solutions in the parent population. As it is easy to search some promising solutions in the neighborhood of an elitist solution, local learning can get some useful solutions which help the population attain a fast and accurate convergence. The experimental results on a set of test instances with variable linkages show that the implement of local learning can accelerate convergence speed and add a more accurate convergence to the Pareto optimal.

关键词： multi-objective optimization problem Estimation of distribution algorithm Local learning

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multi-objective operation management of a multi-carrier energy system

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ENERGY 2015年 88卷 430-442页

作者： Shabanpour-Haghighi, Amin Seifi, Ali Reza Shiraz Univ Sch Elect & Comp Engn Shiraz Iran

In this paper, a multi-objective optimization approach for multi-carrier energy networks is discussed. A multi-carrier energy network is a system consists of various types of energy carrier such as electricity, natural gas, and heat. Minimizing the total cost of operation of such a system is a typical objective for optimization while another important objective is to minimize the total emission generated by the whole network. It is shown in the paper that the cost and emission functions are two opposite objectives that decreasing one of them would increase the other one and vice versa. Therefore, a multi-objective optimization should be utilized to obtain the global optima of the problem based on the priority of each objective. According to the large size of the problem in actual networks, this could be a non-linear, non-convex, non-smooth, and high-dimension optimization problem that mathematical techniques could be trapped in local minima. Hence, it is better to use evolutionary techniques instead. To do so, a fuzzy decision making method is proposed in this paper which is merged with the well-known modified teaching-learning based optimization algorithm. This approach is implemented and applied to a typical multi-carrier energy network to verify the proposed methodology. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： multi-objective optimization problem multi-carrier energy system Energy hub Heuristic algorithm

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Maximizing network lifetime based on transmission range adjustment in wireless sensor networks

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COMPUTER COMMUNICATIONS 2009年第11期32卷 1316-1325页

作者： Song, Chao Liu, Ming Cao, Jiannong Zheng, Yuan Gong, Haigang Chen, Guihai Univ Elect Sci & Technol China Sch Engn & Comp Sci Chengdu 610054 Peoples R China Hong Kong Polytech Univ Dept Comp Internet & Mobile Comp Lab Hong Kong Hong Kong Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Peoples R China

In a wireless sensor network (WSN), the unbalanced distribution of communication loads often causes the problem of energy hole, which means the energy of the nodes in the hole region will be exhausted sooner than the nodes in other regions. This is a key factor which affects the lifetime of the networks. In this paper we propose an improved corona model with levels for analyzing sensors with adjustable transmission ranges in a WSN with circular multi-hop deployment (modeled as concentric coronas). Based on the model we consider that the right transmission ranges of sensors in each corona is the decision factor for optimizing the network lifetime after nodes deployment. We prove that searching optimal transmission ranges of sensors among all coronas is a multi-objective optimization problem (MOP). which is NP hard. Therefore, we propose a centralized algorithm and a distributed algorithm for assigning the transmission ranges of sensors in each corona for different node distributions. The two algorithms can not only reduce the searching complexity but also obtain results approximated to the optimal solution. Furthermore, the simulation results of our solutions indicate that the network lifetime approximates to that ensured by the optimal under both uniform and non-uniform node distribution. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Wireless sensor networks Energy hole problem multi-objective optimization problem NP hard

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A Hyper Heuristic Algorithm for Efficient Resource Allocation in 5G Mobile Edge Clouds

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第1期23卷 29-41页

作者： Laboni, Nadia Motalib Safa, Sadia Jahangir Sharmin, Selina Razzaque, Md. Abdur Rahman, Md. Mustafizur Hassan, Mohammad Mehedi Univ Dhaka Dept Comp Sci & Engn Green Networking Res Grp Dhaka 1000 Bangladesh Jagannath Univ Dept Comp Sci & Engn Dhaka 1100 Bangladesh King Saud Univ Coll Comp & Informat Sci Informat Syst Dept Riyadh 11543 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Res Chair Pervas & Mobile Comp Riyadh 11543 Saudi Arabia

Emergence of intelligent devices and mobile edge clouds (MECs) in 5G networks has exponentially increased the number of applications that demand low latency services. However, their resource heterogeneity, limited computing power and storage including congestion in the ultra-dense 5G network, make the real-time services challenging. Existing works are limited either by addressing application delay requirements or computational load balancing. This article develops an efficient resource allocation framework for selecting optimal servers and routing paths in the 5G MEC network by jointly optimizing latency, computational, and network load variances. First, we formulate the above multi-objective problem as a mixed-integer non-linear programming problem. Further, we adopt a hyper-heuristic (AWSH) algorithm by leveraging the combined powers of Ant Colony, Whale, Sine-Cosine, and Henry Gas Solubility optimization algorithms. The proposed AWSH algorithm works at the higher level, and it explores and exploits one of the three lower-level heuristics in each iteration to efficiently capture the dynamically varying environmental parameters and thereby address the resource allocation problem. Their collaborative effort helps to achieve a global optimum in allocating resources of 5G MEC network. Simulation results prove the superiority of the AWSH algorithm compared to state-of-the-art solutions in terms of service latency, successful offloading ratio, and load balancing.

关键词： Servers Resource management optimization 5G mobile communication Delays Task analysis Mobile computing 5G Mobile edge cloud resource allocation load balancing multi-objective optimization problem hyper-heuristic algorithm

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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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