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Digital Image Encryption algorithm Based on Double Chaotic Map and LSTM

Computers, Materials & Continua 2023年第11期77卷 1645-1662页

作者： Luoyin Feng Jize Du Chong Fu School of Computer Science and Engineering Northeastern UniversityShenyang110819China School of Electrical Engineering and Computer Science University of MissouriMissouri65201USA

In the era of network communication,digital image encryption(DIE)technology is critical to ensure the security of image ***,there has been limited research on combining deep learning neural networks with chaotic mapping for the encryption of digital ***,this paper addresses this gap by studying the generation of pseudo-random sequences(PRS)chaotic signals using dual logistic chaotic *** signals are then predicted using long and short-term memory(LSTM)networks,resulting in the reconstruction of a new chaotic *** the research process,it was discovered that there are numerous training parameters associated with the LSTM network,which can hinder training *** overcome this challenge and improve training efficiency,the paper proposes an improved particle swarm optimization(IPSO)algorithm to optimize the LSTM ***,the obtained chaotic signal from the optimized model training is further scrambled,obfuscated,and diffused to achieve the final encrypted *** research presents a digital image encryption(DIE)algorithm based on a double chaotic map(DCM)and *** algorithm demonstrates a high average NPCR(Number of Pixel Change Rate)of 99.56%and a UACI(Unified Average Changing Intensity)value of 33.46%,indicating a strong ability to resist differential ***,the proposed algorithm realizes secure and sensitive digital image encryption,ensuring the protection of personal information in the Internet environment.

关键词： Digital image encryption LSTM particle swarm optimization algorithm DCM

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Prediction of high-performance concrete compressive strength through novel structured neural network

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第1期45卷 1791-1803页

作者： Li, Huan Nanchong Vocat & Tech Coll Dept Civil & Architectural Engn Nanchong Sichuan Peoples R China

The difficulties in determining the compressive strength of concrete are inherited due to the various nonlinearities rooted in the mix designs. These difficulties raise dramatically considering the modern mix designs of high-performance concrete. Presents study tries to define a simple approach to link the input ingredients of concrete with the resulted compressive with a high accuracy rate and overcome the existing nonlinearity. For this purpose, the radial base function is defined to carry out the modeling process. The optimal results were obtained by determining the optimal structure of radial base function neural networks. This task was handled well with two precise optimization algorithms, namely Henry's gas solubility algorithm and particle swarm optimization algorithm. The results defined both models' best performance earned in the training section. Considering the root mean square error values, the best value stood at 2.5629 for the radial base neural network optimized by Henry's gas solubility algorithm, whereas the same value for the the radial base neural network optimized by particle swarm optimization was 2.6583 although both hybrid models provided acceptable output results, the radial base neural network optimized by Henry's gas solubility algorithm showed higher accuracy in predicting high performance concrete compressive strength.

关键词： High-performance concrete Henry's gas solubility algorithm particle swarm optimization algorithm radial base function neural network

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optimization of the square and squared- off cascades to separate stable isotopes usingmeta-heuristic algorithms

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ATOMIC ENERGY 2023年第5-6期134卷 397-416页

作者： Ardestani, Fatemeh Safdari, Jaber HassanMallah, Mohammad Imani, Morteza Nucl Sci & Technol Res Inst Mat & Nucl Fuel Res Sch Tehran Iran

In this paper, the optimization of the square and squared-off cascades for the separation of stable isotopes with a certain number of gas centrifuges (GCs) using particle swarm optimization (PSO) and the whale optimization algorithm (WOA) was studied. For this purpose, the "SC-PSO" and "SC-WOA" codes for the square cascade (SC), and the "SOC-PSO" and "SOC-WOA" codes for the squared-off cascade (SOC) were developed. The performance of the PSO and WOA algorithms in the optimization of the square and squared-off cascades as an example for the separation of 124Xe from nine stable isotopes of xenon up to an enrichment of 85% was compared. The recovery coefficient of 124Xe isotope by the codes "SC-PSO", "SC-WOA", "SOC-PSO", and "SOC-WOA" was obtained 86.14, 78.11, 92.84, and 83.41% respectively. The performance of the algorithms indicates the higher yield of the PSO algorithm in the optimization of SC and SOC. The efficiency of the PSO has been demonstrated by the Griewank test function compared with the WOA, the sine cosine algorithm (SCA), and the dragon-fly algorithm ( DA) (PSO>WOA>SCA>DA). Furthermore, the results show that SOC performs better than SC.

关键词： Square cascade Squared-off cascade Gas centrifuge particle swarm optimization algorithm Whale optimization algorithm Multicomponent separation

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A new uncertain remanufacturing scheduling model with rework risk using hybrid optimization algorithm

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第22期30卷 62744-62761页

作者： Zhang, Wenyu Wang, Jun Liu, Xiangqi Zhang, Shuai Zhejiang Univ Finance & Econ Sch Informat Management & Artificial Intelligence Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Informat Engn Sch Hangzhou 311305 Peoples R China Hangzhou Dianzi Univ Sch Mech Engn Hangzhou 310018 Peoples R China

As a resource-conserving and environmentally friendly manufacturing paradigm, remanufacturing with the potential to realize sustainability in production has been extensively investigated. Scheduling plays a significant role in achieving the remanufacturing benefits. However, the remanufacturing process involves intricate uncertainties because it takes end-of-life products with different qualities as workblanks, which increases the risk of rework and complicates remanufacturing scheduling. Though the traditional stochastic optimization methods or fuzzy theory have been employed to address uncertainties in the remanufacturing scheduling problem, they are constrained with the limited historical data which renders it difficult to describe uncertainties accurately and intuitively. Therefore, a new uncertain remanufacturing scheduling model with rework risk is proposed, in which the interval grey numbers are applied to describe the uncertainty clearly and consider the rework risk in remanufacturing process. To solve this model, a hybrid optimization algorithm that combines differential evolution and particle swarm optimization algorithms through an efficient representation scheme is proposed. Besides, this algorithm integrates multiple improvements to maintain the diversity of the population and enhance its performance. Simulation experiments are conducted on 18 sets of instances with different scales, and the results demonstrated that the proposed algorithm obtains a better optimal solution than other baseline algorithms on 17 sets of instances. The main finding of this study is providing a new method for solving uncertain remanufacturing scheduling problem with rework risk practically and effectively.

关键词： Differential evolution algorithm Interval grey number particle swarm optimization algorithm Remanufacturing scheduling Rework risk

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A hybrid particle swarm optimization and recurrent dynamic neural network for multi-performance optimization of hard turning operation

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AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING 2022年 36卷

作者： Pourmostaghimi, Vahid Zadshakoyan, Mohammad Khalilpourazary, Saman Badamchizadeh, Mohammad Ali Univ Tabriz Fac Mech Engn Dept Mfg & Prod Engn Tabriz Iran Urmia Univ Technol Fac Mech Engn Dept Renewable Energy Orumiyeh Iran Univ Tabriz Fac Elect & Comp Engn Control Engn Dept Tabriz Iran

In the present work, a new hybrid approach combining particle swarm optimization (PSO) algorithm with recurrent dynamic neural network (RDNN), which is described as PSO-RDNN algorithm, is proposed for multi-performance optimization of machining parameters in finish turning of hardened AISI D2. The suggested optimization problem is solved using the weighted sum technique. Process parameters including cutting speed and feed rate are optimized for minimizing operation cost, maximizing tool life, and producing parts with acceptable surface roughness. Based on experimental results, two neural network models were developed for predicting tool flank wear and surface roughness during the machining process. Based on trained neural networks and structured hybrid algorithm, optimum cutting parameters were obtained. The coefficient of determination for trained neural networks was calculated as R-2 = 0.9893 and R-2 = 0.9879 for predicted flank wear and surface roughness, respectively, which proves the efficiency of trained neural models in real industrial applications. Furthermore, the offered methodology returns a Pareto optimality graph, which represents optimized cutting variables for several various cutting conditions.

关键词： Hard turning hybrid algorithm multi-objective optimization particle swarm optimization algorithm recurrent dynamic neural network tool flank wear

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A particle swarm Optimizer with adaptive dynamic neighborhood for multimodal multi-objective optimization

A Particle Swarm Optimizer with adaptive dynamic neighborhoo...

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第35届中国控制与决策会议

作者： Jingyue Wei Enze Zhang Rui Ge Information Engineering College of Yangzhou University

In this paper,a multi-objective particle swarm optimizer based on adaptive dynamic neighborhood(ADNMOPSO) is proposed to locate multiple Pareto optimal solutions to solve multimodal multi-objective *** the proposed algorithm,a spatial distance-based non-overlapping ring topology is used to form multiple subpopulations for parallel search to enhance the local search capability of the *** addition,an adaptive dynamic neighborhood selection strategy is proposed to balance the exploration and exploitation capabilities of the algorithm,allowing the size of the subpopulation to change automatically when the neighborhood switch time is *** prevent the algorithm from premature convergence,a stagnation detection strategy is introduced to apply a Gaussian perturbation operation to the particles that fall into the neighborhood ***,the proposed algorithm is used to solve multimodal multi-objective test problems and compared with existing multimodal multiobjective optimization *** results show that the proposed algorithm can obtain more Pareto solutions when solving different types of multimodal multi-objective functions.

关键词： Multi-objective optimization multimodal multi-objective optimization particle swarm optimization algorithm sub-swarm regrouping ring topology

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Dual periodic event-triggered control for multi-agent systems with input saturation

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ISA TRANSACTIONS 2023年 136卷 61-74页

作者： Chen, Mengshen Yan, Huaicheng Zhang, Hao Fan, Sha Shen, Hao East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Tongji Univ Dept Control Sci & Engn Shanghai 200092 Peoples R China Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243002 Peoples R China Chengdu Univ Sch Informat Sci & Engn Chengdu 610106 Peoples R China

This paper is concerned with the periodic event-triggered consensus of multi-agent systems subject to input saturation. Due to the nonlinearity caused by the input saturation constraint, the accuracy of the event-triggered mechanism to screen data will be reduced. To deal with this problem, a novel dual periodic event-triggered mechanism is first proposed, in which a saturation-assisted periodic event-trigger and a complemental periodic event-trigger work synergistically to screen data more efficiently under the input saturation constraint. In addition, considering the various disturbances in the environment, a more general mixed H infinity and passive performance is introduced to describe the disturbance attenuation level. Based on the Lyapunov-Krasovskii functional, some less conservative consensus criteria are obtained for the multi-agent systems. In addition, under different input satura-tion constraints, the relationship between the disturbance attenuation level and the data transmission rate is explored. After that, a particle swarm optimization algorithm is a first attempt to estimate and enlarge the region of asymptotic consensus. Finally, an example is given to verify the effectiveness and superiority of our proposed method. (c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Multi-agent systems Dual periodic event-triggered mechanism Input saturation particle swarm optimization algorithm

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Estimation of geomechanical rock characteristics from specific energy data using combination of wavelet transform with ANFIS-PSO algorithm

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2023年第8期13卷 1715-1740页

作者： Behboud, Mohammad Mohammadi Ramezanzadeh, Ahmad Tokhmechi, Behzad Mehrad, Mohammad Davoodi, Shadfar Shahrood Univ Technol Sch Min Petr & Geophys Engn Shahrood Iran Tomsk Polytech Univ Sch Earth Sci & Engn Lenin Ave Tomsk Russia

The geomechanical characteristics of a drill formation are uncontrollable factors that are crucial to determining the optimal controllable parameters for a drilling operation. In the present study, data collected in wells drilled in the Marun oilfield of southwestern Iran were used to develop adaptive network-based fuzzy inference system (ANFIS) models of geomechanical parameters. The drilling specific energy (DSE) of the formation was calculated using drilling parameters such as weight-on-bit (WOB), rate of penetration (ROP), rotational speed of drilling string (RPM), torque, bit section area, bit hydraulic factor, and bit hydraulic power. A stationary wavelet transform was subsequently used to decompose the DSE signal to the fourth level. The approximation values and details of each level served as inputs for ANFIS models using particle swarm optimization (PSO) algorithm and genetic algorithm (GA). As model outputs, the Young's Modulus, uniaxial compressive strength (UCS), cohesion coefficient, Poisson's ratio, and internal friction angle were compared to the geomechanical parameters obtained from petrophysical logs using laboratory-developed empirical relationships. Both models predicted the Young's modulus, UCS, and cohesion coefficient with high accuracy, but lacked accuracy in predicting the internal friction angle and Poisson's ratio. The root mean square error (RMSE) and determination coefficient (R-2) were lower for the ANFIS-PSO model than for the ANFIS-GA model, indicating that the ANFIS-PSO model presents higher accuracy and better generalization capability than the ANFIS-GA model. As drilling parameters are readily available, the proposed method can provide valuable information for strategizing a drilling operation in the absence of petrophysical logs.

关键词： Drilling specific energy ANFIS Wavelet function particle swarm optimization algorithm Genetic algorithm

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A Hybrid Genetic and particle swarm optimization algorithms for Dynamic Facility Layout Problem with Multiple Transporters 15

A Hybrid Genetic and Particle Swarm Optimization Algorithms ...

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15th Iran International Industrial Engineering Conference (IIIEC)

作者： Pourhassan, Mohammad Reza Raissi, Sadigh Islamic Azad Univ South Tehran Branch Sch Ind Engn Tehran Iran

ISBN: (纸本)9781728117751

Nowadays, manufacturing plants should be agile to changes their production mix plan based on dynamic demands. Here, layout design significantly could impact on manufacturing efficiency. When the flows of materials between departments embed variability during the planning horizon, this problem is known as the dynamic facility layout problem (DFLP). This paper extends such problem with considering multiple transporters, which commonly are used for transportation tasks among facilities. Hence, we extended the classical DFLP objective function in such a way that could encounter total combined rearrangement, material handling and transporting costs. Firstly, the relevant mathematical model is presented and then hybrid metaheuristic algorithms based on particle swarm optimization (PSO) and genetic algorithm (GA) presented to solve such problem efficiently. To achieve reliable results, a Taguchi's design of experiments is applied to calibrate initial parameters. Also, a few small-sized problems are solved using the CPLEX software. Analysis of the results shows that the proposed hybrid PSO algorithms have good solution quality according to the objective function and CPU time rather than hybrid GA and proved the effectiveness of this algorithm on the set of test problems.

关键词： Dynamic facility layout particle swarm optimization algorithm Genetic algorithm Taguchi design of experiments

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Economic optimization of business administration resources: Multi-objective scheduling method based on improved PSO

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Li, Sizheng Jiangxi Vocat Coll Mech & Elect Technol Sch Management Engn 168 Fenglin Ave Changbei Econ Dev Zone Nanchang 330000 Peoples R China

In the context of increasing environmental challenges and the demand for sustainable development, traditional resource scheduling models in business management often fail to balance economic efficiency with environmental constraints. To address this gap, this study proposes an enhanced particle swarm optimization (PSO) algorithm, termed OBLPSO, which integrates Opposition-Based Learning (OBL) and a perturbation mechanism. First, OBL generates a high-quality initial population to improve solution diversity, while a cosine curve adaptive strategy dynamically adjusts inertia weights to balance global exploration and local exploitation. Additionally, a perturbation mechanism expands the search range, preventing premature convergence. A multi-objective optimization model is established, incorporating task time, economic cost, and environmental impact (e.g., energy consumption and pollutant emissions) to maximize resource utilization and minimize ecological harm. Experimental results demonstrate that OBLPSO reduces task processing time by 29.7% and energy consumption by 16.1% compared to benchmark algorithms (e.g., ACO, GA, and standard PSO) under large-scale tasks (2000 tasks). The proposed method provides a robust solution for sustainable resource scheduling in an enterprise management environment with economic constraints.

关键词： Business management resource scheduling particle swarm optimization algorithm inverse learning perturbation mechanism resource mapping

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