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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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Hybrid Heuristic Algorithm for Better Energy optimization and Resource Utilization in Cloud Computing

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WIRELESS PERSONAL COMMUNICATIONS 2021年第1期118卷 43-73页

作者： Al-Mahruqi, Ali Abdullah Hamed Morison, Gordon Stewart, Brian G. Athinarayanan, Vallavaraj Natl Univ Sci & Technol Dept Elect & Comp Engn Muscat Oman Glasgow Caledonian Univ Sch Engn & Built Environm Glasgow G4 0BA Lanark Scotland Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Lanark Scotland

Energy-efficient execution of the scientific workflow is a challenging task in cloud computing that demands high-performance computing to process growing datasets. Due to the interdependency of tasks in the scientific workflow applications, energy-efficient resource allocation is vital for large-scale applications running on heterogeneous physical machines. Thus, this paper proposes a hybrid heuristic algorithm based energy-efficient cloud computing service (HH-ECO) that offers a significant solution for resource allocation, task scheduling, and optimization of scientific workflows. To ensure the energy-efficient execution, the HH-ECO focuses on executing non-dominant workflow tasks through adaptive mutation and energy-aware migration strategy. HH-ECO adopts the chaotic based particle swarm optimization (C-PSO) principle to optimize the resource allocation, task scheduling, and resource migration by generating the global best plans without local convergence. C-PSO with adaptive mutation avoids the deterioration of global optima while finding the best host to place the virtual machine and ensures an appropriate resource allocation plan. By considering the workflow task precedence relationships during C-PSO based task scheduling, the novel hybrid heuristic method efficiently solves the multi-objective combinatorial optimization problem without dominance among the workflow tasks. The Cloudsim based simulation study delivers superior results compared to the existing methods such as the hybrid heuristic workflow scheduling algorithm (HHWS) and distributed dynamic VM management (DDVM). The proposed approach significantly improves the optimal makespan to 38.27% and energy conservation to 38.06% compared to the existing methods.

关键词： Green cloud computing VM migration Energy-efficient resource optimization multi-objective optimization problem Heuristic algorithm Load category

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

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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

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A novel clustering-based evolutionary algorithm with objective space decomposition for multi/many-objective optimization

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INFORMATION SCIENCES 2024年 677卷

作者： Zheng, Wei Tan, Yanyan Yan, Zeyuan Yang, Mingming Xian Univ Sci & Technol Coll Comp Sci & Technol 58 Yanta Rd Xian 710054 Shaanxi Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Shandong Peoples R China Sun Yat Sen Univ Sch Environm Sci & Engn 135 Xingang Xi Rd Guangzhou 510275 Guangdong Peoples R China Chongqing Jiaotong Univ Sch Aeronaut 66 Xuefu Rd Chongqing 400074 Peoples R China

The framework of decomposing a multi -objective optimization problem (MOP) into some MOPs holds considerable promise. However, its advancement is constrained by numerous elements, including the incorrect segmentation of the subspaces and the challenges in balancing convergence and diversity. To address these issues, an objective space Decomposition and Clustering-based Evolutionary Algorithm (DCEA) is proposed in this paper. Specifically, DCEA employs K-means clustering to create an appropriate mating pool for each individual without the necessity to predetermine the number of clusters. Within each mating pool, the proposed adaptive evolutionary operator is applied to produce offspring for balancing the convergence and diversity. To enhance the accuracy of partitioning, a refined environmental selection approach utilizing supplementary weight vectors is developed. Additionally, by utilizing historical clustering data, a straightforward approach to periodically adjust reference vectors for the allocation of computational resources is proposed. In experiments, both MOPs and many-objective optimization problems (MaOPs) are used to test DCEA. A total of 27 MOPs and 30 MaOPs are involved and 16 state-of-the-art algorithms are employed to compare with DCEA. Comprehensive experiments show that DCEA is an effective algorithm for solving both MOPs and MaOPs.

关键词： Decomposition Convergence and diversity Clustering multi-objective optimization problem Many-objective optimization problem

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multi-objective QPSO for short-term load forecast based on diagonal recursive neural network

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2021年第5期21卷 1113-1124页

作者： Zhang, Lan Xu, Lei Xian Aeronaut Polytech Inst Xian Shaanxi Peoples R China

The short-term load forecast is an important part of power system operation, which is usually a nonlinear problem. The processing of load forecast data and the selection of forecasting methods are particularly important. In order to get accurate and effective prediction for power system load, this article proposes a hybrid multi-objective quantum particle swarm optimization (QPSO) algorithm for short-term load forecast of power system based on diagonal recursive neural network. Firstly, a multi-objective mathematical model for short-term load forecast is proposed. Secondly, the discrete particle swarm optimization (PSO) algorithm is used to select the characteristics of load data and screen out the appropriate data. Finally, the hybrid multi-objective QPSO algorithm is used to train diagonal recursive neural network. The experimental results show that the hybrid multi-objective QPSO for short-term load forecast based on diagonal recursive neural network is effective.

关键词： multi-objective optimization problem diagonal recursive neural network quantum particle swarm optimization algorithm load forecast

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multi-objective Optimal Power Flow Solutions Using Improved multi-objective Mayfly Algorithm (IMOMA)

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2023年第12期32卷

作者： Bhaskar, K. Vijaya Ramesh, S. Karunanithi, K. Raja, S. P. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Elect & Elect Engn Chennai 600062 Tamil Nadu India Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

This paper realizes the implementation of Improved multi-objective Mayfly Algorithm (IMOMA) for getting optimal solutions related to optimal power flow problem with smooth and nonsmooth fuel cost coefficients. It is performed by considering Simulated Binary Crossover, polynomial mutation and dynamic crowding distance in the existing multi-objective Mayfly Algorithm. The optimal power flow problem is formulated as a multi-objective optimization problem that consists of different objective functions, viz. fuel cost with/without valve point loading effect, active power losses, voltage deviation and voltage stability. The performance of Improved multi-objective Mayfly Algorithm is interpreted in terms of the present multi-objective Mayfly Algorithm and Nondominated Sorting Genetic Algorithm-II. The algorithms are applied under different operating scenarios of the IEEE 30-bus test system, 62-bus Indian utility system and IEEE 118-bus test system with different combinations of objective functions. The obtained Pareto fronts achieved through the implementation of Improved multi-objective Mayfly Algorithm, multi-objective Mayfly Algorithm and Nondominated Sorting Genetic Algorithm-II are compared with the reference Pareto front attained by using weighted sum method based on the Covariance Matrix-adapted Evolution Strategy method. The performances of these algorithms are individually analyzed and validated by considering the performance metrics such as convergence, divergence, generational distance, inverted generational distance, minimum spacing, spread and spacing. The best compromising solution is achieved by implementing the Technique for Order of Preference by Similarity to Ideal Solution method. The overall result has shown the effectiveness of Improved multi-objective Mayfly Algorithm for solving multi-objective optimal power flow problem.

关键词： Covariance Matrix-adapted Evolution Strategy Improved multi-objective Mayfly Algorithm multi-objective optimization problem TOPSIS valve point loading

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An improved regularity-based vector evolutionary algorithm for multi-objective optimizations

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ADVANCES IN ENGINEERING SOFTWARE 2023年 176卷

作者： Li, Yiying Yang, Shiyou Zhejiang Univ Coll Elect Engn 38 Zheda Rd Hangzhou 310027 Peoples R China

Inverse models-based evolutionary algorithms can generate as many solutions as required in interest regions at a low budget cost. However, the objective value used for the inverse model in the original regularity-based al-gorithm using Gaussian process-based inverse models, (IM-MOEA), is linearly and separately sampled for each objective according to its maximum and minimum values so far searched. Consequently, some non-optimal even infeasible solutions may be sampled. Also, the number of the predefined reference vectors remains constant throughout the whole evolution. Consequently, it may result in a low computational efficiency for multi-objective optimization ones (MOOs) with nonuniform or disconnected Pareto fronts. In this respect, an improved regularity-based vector evolutionary algorithm for multi-objective optimizations is presented in the paper. The main component of the proposed algorithm is to obtain the training data for inverse models by interpolation in the objective space. The population is divided into subpopulations according to the number of objectives. The non-dominated solutions are utilized for interpolations in each subpopulation. The inverse models are finally used to generate offspring using data by interpolation. An adaptive interpolation range co-efficient is proposed for adaptive adjustments of the interpolation region to balance the exploitation and the exploration searches. An elite strategy of hiring elite individuals in the whole population to the current sub-population is employed to enhance the algorithm convergence. Experimental results on two test suites show the superiority of the proposed algorithm.

关键词： Evolutionary algorithm multi-objective optimization problem Interpolation Inverse models

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