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Parallel non-dominated sorting genetic algorithm-II-based image encryption technique

IMAGING SCIENCE JOURNAL 2018年第8期66卷 453-462页

作者： Kaur, Manjit Kumar, Vijay Thapar Inst Engn & Technol Comp Sci & Engn Dept Patiala Punjab India

Chaotic system requires parameters to generate random sequences. Recent studies show that the improper selection of parameter values make secret keys generated from chaotic system vulnerable. Meta-heuristic techniques have been introduced in the area of image encryption to improve the selection of chaotic system parameters. But, these techniques suffer from poor computational speed. To overcome this issue, in this paper, a parallel non-dominated sorting genetic algorithm (NSGA-II)-based intertwining logistic map is proposed to encrypt the images. To implement NSGA-II in parallel fashion, master-slave environment is designed. Initially, the execution time analysis of NSGA-II is done to determine the computationally expensive operations. Thereafter, NSGA-II operators are divided into master and slave jobs. The Message Passing Interface (MPI) is used for intercommunication between master and slave nodes. The simulation results show that the parallel proposed technique provides a significant improvement in computational speed as compared to the existing techniques.

关键词： Image encryption non-dominated sorting genetic algorithm intertwining logistic map security analysis

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Parallel non-dominated sorting genetic algorithm-II-based image encryption technique

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The Journal of Photographic Science 2018年第8期66卷 453-462页

ABSTRACTABSTRACTChaotic system requires parameters to generate random sequences. Recent studies show that the improper selection of parameter values make secret keys generated from chaotic system vulnerable. Meta-heuristic techniques have been introduced in the area of image encryption to improve the selection of chaotic system parameters. But, these techniques suffer from poor computational speed. To overcome this issue, in this paper, a parallel non-dominated sorting genetic algorithm (NSGA-II)-based intertwining logistic map is proposed to encrypt the images. To implement NSGA-II in parallel fashion, master–slave environment is designed. Initially, the execution time analysis of NSGA-II is done to determine the computationally expensive operations. Thereafter, NSGA-II operators are divided into master and slave jobs. The Message Passing Interface (MPI) is used for intercommunication between master and slave nodes. The simulation results show that the parallel proposed technique provides a significant improvement in computational speed as compared to the existing techniques.

关键词： Image encryption non-dominated sorting genetic algorithm intertwining logistic map security analysis

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Multi-objective optimization of the aiming strategy for the solar power tower with a cavity receiver by using the non-dominated sorting genetic algorithm

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APPLIED ENERGY 2017年 205卷 399-416页

作者： Wang, Kun He, Ya-Ling Xue, Xiao-Dai Du, Bao-Cun Xi An Jiao Tong Univ Minist Educ Sch Energy & Power Engn Key Lab Thermofluid Sci & Engn Xian 710049 Shaanxi Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Control & Simulat Power Syst & Gene Beijing 100084 Peoples R China Qinghai Univ New Energy Photovolta Ind Res Ctr Xining 810016 Qinghai Peoples R China

The extremely non-uniform solar flux distribution in the solar power tower plant can badly cause some crucial problems for the solar receiver such as the local hot spot, the thermal stress, and the thermal deformation. Homogenization of the solar flux distribution is an effective method to avoid these problems, and has become an important research topic. The objective of the present study is to homogenize the solar flux distribution on the inner surfaces within the cavity receiver while keeping the optics loss as low as possible by replacing the conventional single-point aiming strategy with optimal multi-point aiming strategies. Multi-objective optimizations of the aiming strategy for the solar power tower with cavity receivers are performed by using the non-dominated sorting genetic algorithm. The distribution of the aiming points on the cavity aperture and the allocation of the aiming points for each heliostat are optimized simultaneously. The following conclusions can be made: (1) The uniformity of the solar flux distribution on the aperture does not always signify the uniformity of the solar flux distribution on the inner surfaces, where the later one is what we are truly concerned about. Therefore, the optimization of the aiming strategy should take charge of the solar flux distribution on the inner surfaces rather than on the aperture. (2) The multi-objective optimization can provide the trade-off between the non-uniformity of the solar flux distribution and the optics loss in the form of Pareto optimal fronts. (3) The optimal aiming strategies provided by the multi-objective optimization can significantly homogenize the solar flux distribution on the inner surfaces within the cavity at a minimum cost of optics loss. (4) For the optimal aiming strategies at all time except the noon, there exists a west-east asymmetry of the aiming point distribution on the aperture. Moreover, the asymmetry gets less obvious as the time gets closer to the noon.

关键词： Solar power tower Multi-point aiming strategy Solar flux distribution Uniformity Optics loss non-dominated sorting genetic algorithm

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Solving a New Multi-objective Inventory-Routing Problem by a non-dominated sorting genetic algorithm

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INTERNATIONAL JOURNAL OF ENGINEERING 2018年第4期31卷 588-596页

作者： Arab, R. Ghaderi, S. F. Tavakkoli-Moghaddam, R. Univ Tehran Sch Ind Engn Coll Engn Tehran Iran

This paper considers a multi-period, multi-product inventory-routing problem in a two-level supply chain consisting of a distributor and a set of customers. This problem is modeled with the aim of minimizing bi-objectives, namely the total system cost (including startup, distribution and maintenance costs) and risk-based transportation. Products are delivered to customers by some heterogeneous vehicles with specific capacities through a direct delivery strategy. Additionally, storage capacities are considered limited and the shortage is assumed to be impermissible. To validate this new bi-objective model, the epsilon-constraint method is used for solving problems. The epsilon-constraint method is an exact method for solving multi-objective problems, which offers Pareto's solutions, such as meta-heuristic algorithms. Since problems without distribution planning are very complex to solve optimally, the problem considered in this paper also belongs to a class of NP-hard ones. Therefore, a non-dominated sorting genetic algorithm (NSGA-II) as a well-known multi-objective evolutionary algorithm is used and developed to solve a number of test problems. In this paper, 20 sample problems with the epsilon-constraint method and NSGA-II are solved and compared in different dimensions based on Pareto's solutions and the time of resolution. Furthermore, the computational results showed the better performance of the NSGA-II.

关键词： Inventory-routing Problem Multi-objective Optimization e-constraint Method non-dominated sorting genetic algorithm

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A non-dominated sorting genetic algorithm for the location and districting planning of earthquake shelters

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INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2014年第7期28卷 1482-1501页

作者： Hu, Fuyu Yang, Saini Xu, Wei Beijing Normal Univ State Key Lab Earth Surface Proc & Resource Ecol Beijing 100875 Peoples R China Beijing Normal Univ Acad Disaster Reduct & Emergency Management Minist Civil Affairs Beijing 100875 Peoples R China Beijing Normal Univ Acad Disaster Reduct & Emergency Management Minist Educ Beijing 100875 Peoples R China

The improvement of emergency coping capacity is one of the most efficient measures for mitigating disaster impact. Shelter planning is an important strategy to reduce the number of casualties and injuries and facilitate disaster recovery. This study aims to address earthquake shelter location selection and the districting planning of service areas jointly. A bi-objective model is proposed to minimise the total evacuation distance and the total cost, subject to capacity and contiguity constraints. A non-dominated sorting genetic algorithm is developed to tackle the bi-objective model, which involves a multitude of decision variables. To fit the model, the chromosome structure, initialisation process and genetic operators in the algorithm are specifically designed to maintain the contiguity of the service area. And a hybrid strategy of bidirectional multi-point crossover and bidirectional single-point crossover helps promote the diversity of the solutions and accelerate the convergence. Moreover, the Pareto-optimal strategy and feasibility-based rule are combined to obtain trade-offs between objectives. The model and algorithm are validated in a case study of the earthquake shelter location and districting planning problem in Chaoyang District of Beijing, and the results confirm the effectiveness and efficiency of the method.

关键词： location and districting model earthquake shelter non-dominated sorting genetic algorithm contiguity

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Fuzzy ranking based non-dominated sorting genetic algorithm-II for network overload alleviation

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ARCHIVES OF ELECTRICAL ENGINEERING 2014年第3期63卷 367-384页

作者： Pandiarajan, K. Babulal, C. K. Pandian Saraswathi Yadav Engn Coll Dept Elect & Elect Engn Arasanoor India Thiagarajar Coll Engn Dept Elect & Elect Engn Madurai Tamil Nadu India

This paper presents an effective method of network overload management in power systems. The three competing objectives 1) generation cost 2) transmission line overload and 3) real power loss are optimized to provide pareto-optimal solutions. A fuzzy ranking based non-dominated sorting genetic algorithm-II (NSGA-II) is used to solve this complex nonlinear optimization problem. The minimization of competing objectives is done by generation rescheduling. Fuzzy ranking method is employed to extract the best compromise solution out of the available non-dominated solutions depending upon its highest rank. N-1 contingency analysis is carried out to identify the most severe lines and those lines are selected for outage. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 30 and IEEE 118 bus systems with smooth cost functions and their results are compared with other single objective evolutionary algorithms like Particle swarm optimization (PSO) and Differential evolution (DE). Simulation results show the effectiveness of the proposed approach to generate well distributed pareto-optimal non-dominated solutions of multi-objective problem.

关键词： non-dominated sorting genetic algorithm generation rescheduling particle swarm optimization differential evolution overload index

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Multi-objective optimization and inverse design of complementary field-effect transistor using combined approach of machine learning and non-dominated sorting genetic algorithms for next-generation semiconductor devices

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartA期137卷

作者： Kim, Sangpill Min, Seong-Ji Jung, Seung-Geun Yu, Hyun-Yong Korea Univ Dept Semicond Syst Engn Seoul 02841 South Korea Korea Univ Dept Elect Engn Seoul 02841 South Korea

Complementary field-effect transistors (CFETs), which are structures in which different types of transistors are vertically stacked with a shared control gate, are being focused on for continuing to satisfy Moore's law by overcoming the limitations in pitch scaling. The structures of semiconductor devices become more complex as technology node shrinks, and interrelated multivariate parameters increase. In addition, predicting problems and proposing solutions by identifying complex patterns within extensive data collected for emerging semiconductor designs pose significant computational challenges and are inherently difficult. As a breakthrough in design technology co-optimization for advanced devices, this study developed a novel optimization framework integrating technology computer-aided design simulations, machine learning, and non-dominated sorting genetic algorithms. The developed framework provides unbiased optimal solutions, even in a high-dimensional objective space, while considering the tradeoff relationships between multiple variables. In addition, it enables inverse design to identify the design parameters of devices that satisfy specific electrical performance criteria using only a forward model, while achieving an error rate of less than 2%. Using this framework, we analyzed the operational mechanism of CFETs by comparing the inverse designs of various devices. This novel approach is particularly important when the design space is complex and extensive and is well suited for developing devices that emerge with technological advancements in the semiconductor industry.

关键词： Complementary field-effect transistor Machine learning Multi-objective optimization non-dominated sorting genetic algorithm Inverse design

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Multi-objective optimal design of magnetorheological engine mount based on an improved non-dominated sorting genetic algorithm

Multi-objective optimal design of magnetorheological engine ...

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Conference on Active and Passive Smart Structures and Integrated Systems

作者： Zheng Ling Duan Xuwei Deng Zhaoxue Li Yinong Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China

ISBN: (纸本)9780819499837

A novel flow-mode magneto-rheological (MR) engine mount integrated a diaphragm de-coupler and the spoiler plate is designed and developed to isolate engine and the transmission from the chassis in a wide frequency range and overcome the stiffness in high frequency. A lumped parameter model of the MR engine mount in single degree of freedom system is further developed based on bond graph method to predict the performance of the MR engine mount accurately. The optimization mathematical model is established to minimize the total of force transmissibility over several frequency ranges addressed. In this mathematical model, the lumped parameters are considered as design variables. The maximum of force transmissibility and the corresponding frequency in low frequency range as well as individual lumped parameter are limited as constraints. The multiple interval sensitivity analysis method is developed to select the optimized variables and improve the efficiency of optimization process. An improved non-dominated sorting genetic algorithm (NSGA-II) is used to solve the multi-objective optimization problem. The synthesized distance between the individual in Pareto set and the individual in possible set in engineering is defined and calculated. A set of real design parameters is thus obtained by the internal relationship between the optimal lumped parameters and practical design parameters for the MR engine mount. The program flowchart for the improved non-dominated sorting genetic algorithm (NSGA-II) is given. The obtained results demonstrate the effectiveness of the proposed optimization approach in minimizing the total of force transmissibility over several frequency ranges addressed.

关键词： Magneto-rheological mount optimal design lumped parameter model bond graph method multi-objective non-dominated sorting genetic algorithm

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non-dominated sorting genetic algorithms for a Multi-objective Resource Constraint Project Scheduling Problem

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JOURNAL OF INTELLIGENT SYSTEMS 2019年第5期28卷 791-806页

作者： Wang, Xixi Yalaoui, Farouk Dugardin, Frederic Univ Technol Troyes ICD LOSI UMR CNRS 6281 12 Rue Marie CurieCS 42060 F-10004 Troyes France

The resource constraint project scheduling problem (RCPSP) has attracted growing attention since the last decades. Precedence constraints are considered as well as resources with limited capacities. During the project, the same resource can be required by several in-process jobs and it is compulsory to ensure that the consumptions do not exceed the limited capacities. In this paper, several criteria are involved, namely makespan, total job tardiness, and workload balancing level. Our problem is firstly solved by the non-dominated sorting genetic algorithm-II (NSGAII) as well as the recently proposed NSGAIII. Giving emphasis to the selection procedure, we apply both the traditional Pareto dominance and the less documented Lorenz dominance into the niching mechanism of NSGAIII. Hence, we adopt and modify L-NSGAII to our problem and propose L-NSGAIII by integrating the notion of Lorenz dominance. Our methods are tested by 1350 randomly generated instances, considering problems with 30-150 jobs and different configurations of resources and due dates. Hypervolume and C-metric are considered to evaluate the results. The Lorenz dominance leads the population more toward the ideal point. As experiments show, it allows improving the original NSGA approach.

关键词： Multi-objective scheduling problem non-dominated sorting genetic algorithm resource constraint Lorenz dominance

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A New Image Watermarking Scheme Using non-dominated sorting genetic algorithm II

A New Image Watermarking Scheme Using Non-dominated Sorting ...

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International Symposium on Biometrics and Security Technologies (ISBAST)

作者： Lee, Jiann-Shu Huang, Fei-Hsiang Kuo, Hsiao-Chen Natl Univ Tainan Dept Comp Sci & Informat Engn Tainan Taiwan Far East Univ Dept Management Informat Syst Tainan Taiwan

ISBN: (纸本)9780769550107

In recent years, singular value decomposition has become a popular tool for image watermarking. In this paper, we propose a new blind watermarking scheme by quantizing the singular values of wavelet component. To optimize the system performance, we treat the image watermarking as a multi-objective optimization problem based on non-dominated sorting genetic algorithm II. Based on this new perspective, the inherent conflict existing in image fidelity and watermark robustness for image watermarking can be objectively handled. The experimental study shows that our method indeed provide superior performance.

关键词： watermarking singular value decomposition non-dominated sorting genetic algorithm

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