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An image encryption scheme based on particle swarm optimization algorithm and hyperchaotic system

SOFT COMPUTING 2022年第11期26卷 5409-5435页

作者： Luo, Yuling Ouyang, Xue Liu, Junxiu Cao, Lvchen Zou, Yanli Guangxi Normal Univ Sch Elect Engn Guilin Peoples R China

Protecting digital images is crucial, and image encryption schemes based on chaotic systems have been extensively studied where image permutation is a critical process. The updating processes of particle swarm optimization algorithm have the ability to change the rules of particles motion, and it can be employed to permutate image. In this work, an image encryption scheme based on the updating processes of particle swarm optimization algorithm and hyperchaotic system is proposed. Specifically, a key generation mechanism combined with secure hash algorithm 256 hash is first introduced to generate the initial values of hyperchaotic complex Lu system. Then, the plain image is confused by the position and velocity updating processes of particle swarm optimization algorithm. In addition, an adaptive diffusion mechanism is designed and applied to the confused image to obtain the final cipher image. Simulation results and security analysis show that the proposed scheme exhibits good performance of sophisticated dynamic behavior, high sensitivity to key, and effectively resisting the typical cryptanalysis attacks, and provides an alternative to real-time image encryption application.

关键词： Image encryption Hyperchaotic system particle swarm optimization algorithm Secure hash algorithm 256 hash

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Drilling path optimization by the particle swarm optimization algorithm with global convergence characteristics

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2008年第8期46卷 2299-2311页

作者： Zhu, Guang-Yu Zhang, Wei-Bo Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350002 Peoples R China

Drilling path optimization is one of the key problems in holes-machining. This paper presents a new approach to solve the drilling path optimization problem belonging to discrete space, based on the particle swarm optimization (PSO) algorithm. Since the standard PSO algorithm is not guaranteed to be global convergent or local convergent, based on the mathematical model, the algorithm is improved by adopting the method to generate the stop evolution particle once again to obtain the ability of convergence on the global optimization solution. Also, the operators are proposed by establishing the Order Exchange Unit (OEU) and the Order Exchange List (OEL) to satisfy the need of integer coding in drilling path optimization. The experimentations indicate that the improved algorithm has the characteristics of easy realization, fast convergence speed, and better global convergence capability. Hence the new PSO can play a role in solving the problem of drilling path optimization.

关键词： particle swarm optimization algorithm drilling path optimization global convergence PSO

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Simulation optimization using particle swarm optimization algorithm with application to assembly line design

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APPLIED SOFT COMPUTING 2011年第1期11卷 605-613页

作者： Kuo, R. J. Yang, C. Y. Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan Natl Taipei Univ Technol Dept Ind Engn & Management Taipei 106 Taiwan

Assembly line design is an important part of production system. Some processes need to undergo changes in order to increase in efficiency. Computer simulation has been applied on process design for many decades. Traditionally, simulation had to run all possible alternatives of assembly line and was not considered as an optimization technique. Thus, this study employs particle swarm optimization (PSO) algorithm which is with mutation based on similarity for simulation optimization in order to optimize the managerial parameters in production system. Through experimentation designs and statistics tests, the simulation results show that the proposed method is better than other algorithms, like genetic algorithm and conventional PSO algorithm for solving assembly line design problem. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Simulation particle swarm optimization algorithm Simulation optimization Assembly line

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A Novel Dynamic Clustering Method by Integrating Marine Predators algorithm and particle swarm optimization algorithm

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IEEE ACCESS 2021年 9卷 3557-3569页

作者： Wang, N. Wang, J. S. Zhu, L. F. Wang, H. Y. Wang, G. Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 211106 Peoples R China Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Liaoning Peoples R China

Data clustering is the process of identifying natural groupings or clusters based on a certain similarity measure in muti-dimensional data. Aiming at the dynamic clustering problem where the number of clusters cannot be determined in advance, a hybrid dynamic clustering method based on the marine predators algorithm (MPA) and particle swarm optimization (PSO) algorithm was proposed. The position update strategy of the PSO algorithm was used to make up for the lack of MPA in global searching. The fixed-length coding strategy with the real number coding method was used to deal with the variable length clustering optimization problem, and the unfeasible solution processing strategy and the penalty function strategy are adopted to improve the performance of the algorithm and achieve simultaneous optimization of the number of clusters and cluster centers. The proposed MPA-PSO algorithm with PSO algorithm, MPA, Differential Evolution (DE) algorithm, Spotted Hyena Optimizer (SHO), Lightning Searching algorithm (LSA) and Equilibrium Optimizer (EO) are adopted to carry out the clustering simulation experiments on four artificial data sets and six real data sets (Iris, Wine, Wisconsin breast cancer, Vowel, Seeds, and Wdbc) in UCI databases. Three performance indicators (the number of clusters, ARI and Accuracy) are used to evaluate the clustering results. The experimental results show that the proposed method can not only successfully find the correct number of clusters, but also obtain stable results for most test problems.

关键词： Clustering algorithms Indexes Heuristic algorithms optimization particle swarm optimization Encoding Clustering methods Dynamic clustering marine predator algorithm particle swarm optimization algorithm

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PGA: A new particle swarm optimization algorithm based on genetic operators for the global optimization of clusters

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JOURNAL OF COMPUTATIONAL CHEMISTRY 2024年第32期45卷 2764-2770页

作者： Wang, Kai Henan Univ Urban Construct Henan Engn Res Ctr Bldg Photovolta Sch Math & Phys Pingdingshan 467036 Peoples R China

We have developed a global optimization program named PGA based on particle swarm optimization algorithm coupled with genetic operators for the structures of atomic clusters. The effectiveness and efficiency of the PGA program can be demonstrated by efficiently obtaining the tetrahedral Au-20 and double-ring tubular B-20, and identifying the ground state ZrSi17-20- clusters through the comparison between the simulated and the experimental photoelectron spectra (PESs). Then, the PGA was applied to search for the global minimum structures of Mg-n(-) (n = 3-30) clusters, new structures have been found for sizes n = 6, 7, 12, 14, and medium-sized 21-30 were first determined. The high consistency between the simulated spectra and the experimental ones once again demonstrates the efficiency of the PGA program. Based on the ground-state structures of these Mg-n(-) (n = 3-30) clusters, their structural evolution and electronic properties were subsequently explored. The performance on Au-20, B-20, ZrSi17-20-, and Mgn- (n = 3-30) clusters indicates the promising potential of the PGA program for exploring the global minima of other clusters. The code is available for free upon request.

关键词： clusters genetic operators global optimization particle swarm optimization algorithm

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An Improved particle swarm optimization algorithm with Harmony Strategy for the Location of Critical Slip Surface of Slopes

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China Ocean Engineering 2011年第2期25卷 357-364页

作者：李亮褚雪松 School of Civil Engineering Qingdao Technological University

The determination of optimal values for three parameters required in the original particle swarm optimization algorithm is very difficult. It is proposed that two new parameters simulating the harmony search strategy can be adopted instead of the three parameters which are required in the original particle swarm optimization algorithm to update the positions of all the particles. The improved particle swarm optimization is used in the location of the critical slip surface of soil slope, and it is found that the improved particle swarm optimization algorithm is insensitive to the two parameters while the original particle swarm optimization algorithm can be sensitive to its three parameters.

关键词： slope stability analysis limit equilibrium method particle swarm optimization algorithm harmony strategy

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Customer Segmentation Using K-Means Clustering and the Hybrid particle swarm optimization algorithm

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COMPUTER JOURNAL 2023年第4期66卷 941-962页

作者： Li, Yue Qi, Jianfang Chu, Xiaoquan Mu, Weisong China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Minist Agr Key Lab Viticulture & Enol Beijing 100083 Peoples R China

In a competitive market, it is of great significance to divide customer groups to develop customer-centered personalized products. In this paper, we propose a customer segmentation method based on the K-means algorithm and the improved particle swarm optimization (PSO) algorithm. As the PSO algorithm easily falls into local extremum, the improved hybrid particle swarm optimization (IHPSO) algorithm is proposed to improve optimization accuracy. The full factorial design is used to determine the optimal parameter combination;the roulette operator is used to select excellent particles;then, the selected particles are crossed according to their adaptive crossover probabilities;when the population falls into a local optimum, the particles are mutated according to their adaptive mutation probabilities. Aimed at the K-means' sensitivity to selecting the initial cluster centers, IHPSO is used to optimize the cluster centers (IHPSO-KM). We compare IHPSO with the PSO, LDWPSO, GA, GA-PSO and ALPSO algorithms on nine benchmark functions. We also conduct comparative experiments to compare IHPSO-KM with several conventional and state-of-the-art approaches on five UCI datasets. All results show that the two proposed methods outperform existing models. Finally, IHPSO-KM is applied in customer segmentation. The experimental results also prove the rationality and applicability of IHPSO-KM for customer segmentation.

关键词： K-means clustering algorithm particle swarm optimization algorithm hybrid mechanism cluster analysis customer segmentation

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Finding a good shape parameter of RBF to solve PDEs based on the particle swarm optimization algorithm

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ALEXANDRIA ENGINEERING JOURNAL 2018年第4期57卷 3641-3652页

作者： Koupaei, Javad Alikhani Firouznia, Marjan Hosseini, Seyed Mohammad Mahdi Payame Noor Univ Dept Math Box 19395-3697 Tehran Iran Univ Khansar Dept Comp Engn Khansar Iran Shahid Bahonar Univ Kerman Dept Appl Math Kerman Iran

The present study aims at integrating the particle swarm optimization (PSO) algorithm with Kansa's method based on meshless collocation methods in order to determine a good shape parameter of Radial Basis Function (RBF) for solving partial differential equations (PDEs). For this purpose, we use a two-staged experimental design. While in the first stage, PSO algorithm was used to determine an optimal shape parameter for the related RBFs, in the second stage, we employed Kansa's method to estimate the RMS error for specifying approximate solutions. To study the performance of the proposed algorithm, we offer numerical results for two examples of partial differential equations and show the effectiveness of the proposed method. Numerical results demonstrated the performance superiority of the new algorithm model. The findings also indicated that the evolutionary algorithm model is more effective than the golden section search algorithm in finding a good shape parameter of RBF. (C) 2018 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： particle swarm optimization algorithm RBF Kansa's method PDEs Evolutionary algorithm

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Design and Application of Vague Set Theory and Adaptive Grid particle swarm optimization algorithm in Resource Scheduling optimization

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JOURNAL OF GRID COMPUTING 2023年第2期21卷 1-15页

作者： Han, Yibo Han, Pu Yuan, Bo Zhang, Zheng Liu, Lu Panneerselvam, John Nanyang Inst Technol Nanyang Inst Big Data Res Nanyang 473004 Peoples R China Nanyang Inst Technol Sch Informat Engn Nanyang 473004 Peoples R China Univ Leicester Dept Informat Univ Rd Leicester LE1 7RH England Nanyang Inst Technol Sch Comp & Software Nanyang 473004 Peoples R China

The purpose of resource scheduling is to deal with all kinds of unexpected events that may occur in life, such as fire, traffic jam, earthquake and other emergencies, and the scheduling algorithm is one of the key factors affecting the intelligent scheduling system. In the traditional resource scheduling system, because of the slow decision-making, it is difficult to meet the needs of the actual situation, especially in the face of emergencies, the traditional resource scheduling methods have great disadvantages. In order to solve the above problems, this paper takes emergency resource scheduling, a prominent scheduling problem, as an example. Based on Vague set theory and adaptive grid particle swarm optimization algorithm, a multi-objective emergency resource scheduling model is constructed under different conditions. This model can not only integrate the advantages of Vague set theory in dealing with uncertain problems, but also retain the advantages of adaptive grid particle swarm optimization that can solve multi-objective optimization problems and can quickly converge. The research results show that compared with the traditional resource scheduling optimization algorithm, the emergency resource scheduling model has higher resolution accuracy, more reasonable resource allocation, higher efficiency and faster speed in dealing with emergency events than the traditional resource scheduling model. Compared with the conventional fuzzy theory emergency resource scheduling model, its handling speed has increased by more than 3.82 times.

关键词： Resource scheduling Vague set theory particle swarm optimization algorithm Model Multi objective

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Low Carbon optimization Scheduling of Micro Grid Based on Improved particle swarm optimization algorithm

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IEEE ACCESS 2024年 12卷 76432-76441页

作者： Sang, Yingjun Zhang, Wenzhi Ma, Jing Chen, Quanyu Tao, Jinglei Fan, Yuanyuan Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China Huaiyin Inst Technol Fac Math & Phys Huaian 223003 Peoples R China

This article proposes a low-carbon operation analysis method for micro grids based on improved particle swarm optimization algorithm. Corresponding improvements have been made to the inertia weight, learning factor, and individual extreme of the algorithm, depicting the comprehensive low-carbon operation information of micro grids under the influence of carbon emission quotas and carbon trading mechanisms from the perspective of data visualization. The low-carbon scheduling of micro grids is carried out from three perspectives: environmental protection, economy, and comprehensiveness, which compensates for the limitations of focusing on traditional low-carbon operation and provides a powerful tool for analyzing low-carbon operation of micro grids. Firstly, establish the energy consumption cost and carbon emission cost functions of the micro grid system, add the two cost functions together and take the minimum sum to form the objective function of this article. Then, based on the characteristics of each unit, a low-carbon model is constructed to constrain the carbon emissions of each unit. Finally, simulation analysis was conducted on the micro grid system based on the improved particle swarm optimization algorithm, verifying the effectiveness and practicality of the proposed algorithm. The simulation results show that the improved particle swarm optimization algorithm can quickly and effectively reduce energy consumption and carbon emission costs, and improve the comprehensive efficiency of micro grid systems.

关键词： Costs Carbon dioxide Turbines Photovoltaic systems State of charge Generators Wind power generation Microgrids particle swarm optimization Carbon emissions Emissions trading Micro grid low carbon particle swarm optimization algorithm carbon emissions carbon trading mechanism

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