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An image encryption scheme based on an optimal chaotic map derived by multi-objective optimization using ABC algorithm

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NONLINEAR DYNAMICS 2021年第2期105卷 1885-1909页

作者： Toktas, Abdurrahim Erkan, Ugur Ustun, Deniz Karamanoglu Mehmetbey Univ Fac Engn Dept Elect & Elect Engn TR-70200 Karaman Turkey Karamanoglu Mehmetbey Univ Fac Engn Dept Comp Engn TR-70200 Karaman Turkey Tarsus Univ Fac Engn Dept Comp Engn TR-33400 Tarsus Mersin Turkey

A novel optimal chaotic map (OCM) is proposed for image encryption scheme (IES). The OCM is constructed using a multi-objective optimization strategy through artificial bee colony (ABC) algorithm. An empirical model for the OCM with four unknown variables is first constituted, and then, these variables are optimally found out using ABC for minimizing the multi-objective function composed of the information entropy and Lyapunov exponent (LE) of the OCM. The OCM shows better chaotic attributes in the evaluation analyses using metrics such as bifurcation, 3D phase space, LE, permutation entropy (PE) and sample entropy (SE). The encrypting performance of the OCM is demonstrated on a straightforward IES and verified by various cryptanalyses that compared with many reported studies, as well. The main superiority of the OCM over the studies based on optimization is that it does not require any optimization in the encrypting operation;thus, OCM works standalone in the encryption. However, those reported studies use ciphertext images obtained through encrypting process in every cycle of optimization algorithm, resulting in long processing time. Therefore, the IES with OCS is faster than the others optimization-based IES. Furthermore, the proposed IES with the OCM manifests satisfactory outcomes for the compared results with the literature.

关键词： Image encryption Chaotic map optimization algorithm Multi-objective optimization Artificial bee colony

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An adaptive resistance and stamina strategy-based dragonfly algorithm for solving engineering optimization problems

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ENGINEERING COMPUTATIONS 2021年第5期38卷 2228-2251页

作者： Yuan, Yongliang Wang, Shuo Lv, Liye Song, Xueguan Henan Polytech Univ Sch Mech & Power Engn Jiaozuo Henan Peoples R China Dalian Univ Technol Sch Mech Engn Dalian Peoples R China Zhejiang Sci Tech Univ Fac Mech Engn & Automat Hangzhou Peoples R China

Purpose Highly non-linear optimization problems exist in many practical engineering applications. To deal with these problems, this study aims to propose an improved optimization algorithm, named, adaptive resistance and stamina strategy-based dragonfly algorithm (ARSSDA). Design/methodology/approach To speed up the convergence, ARSSDA applies an adaptive resistance and stamina strategy (ARSS) to conventional dragonfly algorithm so that the search step can be adjusted appropriately in each iteration. In ARSS, it includes the air resistance and physical stamina of dragonfly during a flight. These parameters can be updated in real time as the flight status of the dragonflies. Findings The performance of ARSSDA is verified by 30 benchmark functions of Congress on Evolutionary Computation 2014's special session and 3 well-known constrained engineering problems. Results reveal that ARSSDA is a competitive algorithm for solving the optimization problems. Further, ARSSDA is used to search the optimal parameters for a bucket wheel reclaimer (BWR). The aim of the numerical experiment is to achieve the global optimal structure of the BWR by minimizing the energy consumption. Results indicate that ARSSDA generates an optimal structure of BWR and decreases the energy consumption by 22.428% compared with the initial design. Originality/value A novel search strategy is proposed to enhance the global exploratory capability and convergence speed. This paper provides an effective optimization algorithm for solving constrained optimization problems.

关键词： Adaptive resistance and stamina strategy Bucket wheel reclaimer Dragonfly algorithm optimization algorithm

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Multi-objective optimization of process parameters in stamping based on an improved RBM-BPNN network and MOPSO algorithm

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2021年第6期64卷 4209-4235页

作者： Xie, Yanmin Du, Lingfeng Zhao, Jiangbo Liu, Cheng Li, Wei Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Sichuan Peoples R China

Stamping is the main manufacturing process for sheet metal parts. However, during the stamping process, based on excessive blank holder force, unreasonable mold design, and other factors, it is easy to generate defects such as cracks in the drawing area and flange wrinkles. In this paper, a novel hybrid model based on a restricted Boltzmann machine and back-propagation neural network is proposed and its validity is verified through different testing functions. Additionally, an improved multi-objective particle swarm optimization (MOPSO) method based on a crowding operator is proposed and compared to several powerful existing algorithms. The proposed method was applied to the process optimization of a double-C part. The sensitivity of the forming quality to different process parameters was analyzed and a novel index was used to describe quality changes. A mapping relationship between the process parameters and forming quality was established based on the proposed hybrid model. Furthermore, optimal process parameters were obtained using MOPSO. The results demonstrated that the proposed method significantly reduces flange wrinkles without excessive thinning and improves the uniformity of formed parts.

关键词： Stamping Process parameters Neural network optimization algorithm

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Efficient Cooperative Target Node Localization with optimization Strategy Based on RSS for Wireless Sensor Networks

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Computers, Materials & Continua 2025年第3期82卷 5079-5095页

作者： Xinrong Zhang Bo Chang Faculty of Automation Huaiyin Institute of TechnologyHuaian223003China Faculty of Electronic Engineering Huaiyin Institute of TechnologyHuaian223003China

In the RSSI-based positioning algorithm,regarding the problem of a great conflict between precision and cost,a low-power and low-cost synergic localization algorithm is proposed,where effective methods are adopted in each phase of the localization process and fully use the detective information in the network to improve the positioning precision and *** the ranging period,the power attenuation factor is obtained through the wireless channel modeling,and the RSSI value is transformed into *** the positioning period,the preferred reference nodes are used to calculate *** the position optimization period,Taylor expansion and least-squared iterative update algorithms are used to further improve the location *** the positioning,the notion of cooperative localization is introduced,in which the located node satisfying certain demands will be upgraded to a reference node so that it can participate in the positioning of other nodes,and improve the coverage and positioning *** results show that on the same network conditions,the proposed algorithm in this paper is similar to the Taylor series expansion algorithm based on the actual coordinates,but much higher than the basic least square algorithm,and the positioning precision is improved rapidly with the reduce of the range error.

关键词： Wireless sensor networks received signal strength(RSS) optimization algorithm cooperative localiza-tion weighted least squares

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optimization algorithm of Crack Initial Angle Using the Extended Finite Element Method

Optimization Algorithm of Crack Initial Angle Using the Exte...

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2nd International Conference on Advances in Computational Modeling and Simulation (ACMS 2013)

作者： Su, Yi Wang, Shengnan Du, Yong'en Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China

ISBN: (纸本)9783037859063

The extended finite element method (XFEM) allows the entire crack to be represented independently from the mesh, which means re-mesh is unnecessary in model crack growth, reduces the computational time drastically. However, fatigue crack growth simulation has been computationally challenged by lots of analog computations in crack growth. Therefore, a new reanalysis algorithm based on incremental Cholesky factorization is derived. In this paper, we consider a variant of XFEM in which an exponent discontinuous function is used to simulate the crack through unit. Then the corresponding formula of XFEM with inclusion and crack problem with a new reanalysis algorithm is derived. In the end, we use the new reanalysis algorithm and an optimization algorithm to predict the angle of crack initiation from a hole in a plate with inclusion. It shows that the algorithm is effective.

关键词： The extended finite element method(XFEM) Cholesky factorization reanalysis algorithm optimization algorithm

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Process design and intensification of multicomponent azeotropes special distillation separation via molecular simulation and system optimization

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Chinese Journal of Chemical Engineering 2024年第7期71卷 24-44页

作者： Chunliang Liu Jianhui Zhong Ranran Wei Jiuxu Ruan Kaicong Wang Zhaoyou Zhu Yinglong Wang Limei Zhong College of Chemical Engineering Qingdao University of Science and TechnologyQingdao 266042China

This work provides an overview of distillation processes,including process design for different distillation processes,selection of entrainers for special distillation processes,system integration and intensification of distillation processes,optimization of process parameters for distillation processes and recent research progress in dynamic control ***,the feasibility of using thermodynamic topological theories such as residual curve,phase equilibrium line and distillation boundary line to analyze different separation regions is discussed,and the rationality of distillation process design is discussed by using its ***,the application of molecular simulation methods such as molecular dynamics simulation and quantum chemical calculation in the screening of entrainer is discussed for the extractive distillation *** thermal coupling mechanism of different distillation processes is used to explore the process of different process ***,a mixed integer nonlinear optimization strategy for the distillation process based on different algorithms is ***,the improvement of dynamic control strategies for different distillation processes in recent years is *** work focuses on the application of process intensification and system optimization in the design of distillation process,and analyzes the challenges,prospects,and development trends of distillation technology in the separation of multicomponent azeotropes.

关键词： Azeotrope separation Process design optimization algorithm Process integration Dynamic control Entrainer selection

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Biophysical Variable Retrieval of Silage Maize with Gaussian Process Regression and Hyperparameter optimization algorithms

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REMOTE SENSING 2023年第14期15卷 3690-3690页

作者： Akbari, Elahe Boloorani, Ali Darvishi Verrelst, Jochem Pignatti, Stefano Samany, Najmeh Neysani Soufizadeh, Saeid Hamzeh, Saeid Hakim Sabzevari Univ Fac Geog & Environm Sci Dept Remote Sensing & Geog Informat Syst Sabzevar *** Iran Univ Tehran Fac Geog Dept Remote Sensing & GIS Tehran 141785393 Iran Univ Valencia Image Proc Lab IPL C-Catedrat Jose Beltran 2 Paterna 46980 Valencia Spain Inst Methodol Environm Anal CNR IMAA I-85050 Tito PZ Italy Shahid Beheshti Univ Environm Sci Res Inst Dept Agroecol Tehran *** Iran

Quantification of vegetation biophysical variables such as leaf area index (LAI), fractional vegetation cover (fCover), and biomass are among the key factors across hydrological, agricultural, and irrigation management studies. The present study proposes a kernel-based machine learning algorithm capable of performing adaptive and nonlinear data fitting so as to generate a suitable, accurate, and robust algorithm for spatio-temporal estimation of the three mentioned variables using Sentinel-2 images. To this aim, Gaussian process regression (GPR)-particle swarm optimization (PSO), GPR-genetic algorithm (GA), GPR-tabu search (TS), and GPR-simulated annealing (SA) hyperparameter-optimized algorithms were developed and compared against kernel-based machine learning regression algorithms and artificial neural network (ANN) and random forest (RF) algorithms. The accuracy of the proposed algorithms was assessed using digital hemispherical photography (DHP) data and destructive measurements performed during the growing season of silage maize in agricultural fields of Ghale-Nou, southern Tehran, Iran, in the summer of 2019. The results on biophysical variables against validation data showed that the developed GPR-PSO algorithm outperformed other algorithms under study in terms of robustness and accuracy (0.917, 0.931, 0.882 using R-2 and 0.627, 0.078, and 1.99 using RMSE in LAI, fCover, and biomass of Sentinel-2 20 m, respectively). GPR-PSO also possesses the unique ability to generate pixel-based uncertainty maps (confidence level) for prediction purposes (i.e., estimated uncertainty level <0.7 in LAI, fCover, and biomass, for 96%, 98%, and 71% of the total study area, respectively). Altogether, GPR-PSO appears to be the most suitable option for mapping biophysical variables at the local scale using Sentinel-2 images.

关键词： biophysical variables optimization algorithm Gaussian process regression machine learning regression algorithms remote sensing

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optimization algorithm of Resource Allocation Based on Fuzzy Assessment Method

Optimization Algorithm of Resource Allocation Based on Fuzzy...

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International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Li, Yaolei Wang, Congxian Li, Zhenjia Hebei Inst Phys Educ Fac Sports Training Shijiazhuang 050041 Peoples R China Heibei jiaotong Vocat & Tech Coll Dept Basic Courses Sports Teaching & Res Sect Shijiazhuang 050091 Peoples R China

ISBN: (纸本)9783038351153

The technology teaching of university basketball general course is a focus which all sectors of the community should pay more attention. How will the technology teaching of university basketball general course carry out optimization of integration, to play the biggest role, is also the key point of this paper. This paper from the main content of post-modern education and university basketball general course technology teaching, to understand the current situation of university basketball education, and construct basketball general course technology evaluation system. At the same time, using fuzzy evaluation method to do statistical analysis on the situation of basketball general course technology education, and in a targeted manner to set up teaching mode of basketball general course technology under Post-modern educational perspective, use the society, network, universities' and other resources effectively, to sharing the resources, realize technology education and the optimization configuration of resource.

关键词： Fuzzy evaluation Technical model optimization algorithm Weighting factor Project matrix

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optimization methods rooted in optimal control

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Science China(Information Sciences) 2024年第12期67卷 272-280页

作者： Huanshui ZHANG Hongxia WANG Yeming XU Ziyuan GUO School of Electrical and Automation Engineering Shandong University of Science and Technology School of Control Science and Engineering Shandong University

In the paper, we investigate the optimization problem(OP) by applying the optimal control method. The optimization problem is reformulated as an optimal control problem(OCP) where the controller(iteration updating) is designed to minimize the sum of costs in the future time instant, which thus theoretically generates the “optimal algorithm”(fastest and most stable). By adopting the maximum principle and linearization with Taylor expansion, new algorithms are proposed. It is shown that the proposed algorithms have a superlinear convergence rate and thus converge more rapidly than the gradient descent;meanwhile, they are superior to Newton's method because they are not divergent in general and can be applied in the case of a singular or indefinite Hessian matrix. More importantly, the OCP method contains the gradient descent and the Newton's method as special cases, which discovers the theoretical basis of gradient descent and Newton's method and reveals how far these algorithms are from the optimal algorithm. The merits of the proposed optimization algorithm are illustrated by numerical experiments.

关键词： optimal control optimization methods optimization algorithm maximum principle superlinear convergence

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Tolerance-cost optimization for multi-cabin spacecraft assembly considering form errors

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2025年第12期239卷 4367-4379页

作者： Yang, Jiangxin Wu, Chengjin Shao, Nan Cao, Yanlong Anwer, Nabil Cao, Yanpeng Zhejiang Univ Sch Mech Engn Key Lab Adv Mfg Technol Zhejiang Prov Hangzhou Peoples R China Beijing Inst Technol Sch Mech Engn Beijing Peoples R China Univ Paris Saclay ENS Paris Saclay LURPA 4 Ave Sci Gif Sur Yvette France

Tolerance-cost optimization plays an important role in trade-off between deviation reduction and manufacturing cost. However, very few studies focus on the assembly accuracy of the cabin using a more efficient tolerance allocation considering both quality and cost issues. Therefore, this paper proposes a tolerance-cost optimization method for multi-cabin assembly of spacecrafts based on the cloud model based genetic algorithm (CGA). To improve the accuracy of tolerance analysis, a comprehensive assembly accuracy analysis method is used, composed of tolerance representation and propagation. For tolerance representation, skin model shape and small displacement torsor theory are used to respectively represent the form and position errors. For tolerance propagation, the integrated Jacobian-skin model shapes model is used to compute the accumulative results of deviation. Simulation results demonstrate the capability of the proposed method to predict the assembly accuracy of cabins with non-ideal surfaces. To improve the efficiency of the optimization, CGA is utilized as an optimization technique. Compared with traditional optimization methods, simulation analysis shows superior performance of parameter optimization in the problem of tolerance-cost optimization of multi-cabin spacecraft.

关键词： Assembly tolerance analysis tolerance allocation spacecraft cabin optimization algorithm skin model shape

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