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adaptive particle swarm optimization With Quantum-Inspired Quantum Walks for Robust Image Security

IEEE ACCESS 2023年 11卷 71143-71153页

作者： El-Latif, Ahmed A. Abd Abd-El-Atty, Bassem Prince Sultan Univ Coll Comp & Informat Sci EIAS Data Sci Lab Riyadh 11586 Saudi Arabia Menoufia Univ Fac Sci Dept Math & Comp Sci Shibin Al Kawm 32511 Egypt Luxor Univ Fac Comp & Informat Dept Comp Sci Luxor 85957 Egypt

In this paper, we propose a novel image cryptosystem that combines the adapted particle swarm optimization (PSO) algorithm with a quantum-inspired Discrete-time Quantum Walk (DTQW). The proposed approach leverages the strengths of both PSO and DTQW, integrating them to achieve high security and efficiency in image encryption. The main contribution of this work lies in the development of this unique cryptosystem. While previous approaches have separately explored PSO or DTQW, our integration of these two techniques offers a novel and innovative approach. By employing chaotic sequences generated by a 3-D chaotic system and a controlled DTQW model, our cryptosystem demonstrates a high sensitivity to even slight changes in the original image. This sensitivity ensures that minor modifications in the plaintext lead to significant changes in the ciphertext, enhancing the system's resistance against attacks. Simulation outcomes prove that the proposed encryption mechanism has high security as well as high *** makes it well-suited for practical implementation in a post-quantum computing era.

关键词： Image security adaptive particle swarm optimization chaotic maps data security quantum walks

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adaptive particle swarm optimization with population diversity control and its application in tandem blade optimization

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2019年第6期233卷 1859-1875页

作者： Song, Zhaoyun Liu, Bo Cheng, Hao Northwestern Polytech Univ Sch Power & Energy Xian Shaanxi Peoples R China

This paper proposes a new variant of particle swarm optimization, namely adaptive particle swarm optimization with population diversity control (APSO-PDC), to improve the performance of particle swarm optimization. APSO-PDC is formulated based on adaptive selection of particle roles, population diversity control, and adaptive control of parameters. The adaptive selection of particle roles which combines the evolutionary state and dynamic particle state estimation method will sort the particles into three roles to let different particles execute different search tasks during optimization process. The adaptive control of parameters which is created based on the evolutionary state and particle roles encourages the exploitation ability and enhances the algorithm's convergence speed. The population diversity control which combines comprehensive learning strategy of the comprehensive learning particle swarm optimizer with evolutionary state to update the individual best position strengthens exploration ability and thus increases the algorithm's robustness toward the premature convergence issue. The performance of APSO-PDC is comprehensively evaluated by 21 unimodal and multimodal functions with or without rotation. The results indicate APSO-PDC has more preferable searching accuracy, searching reliability, and convergence speed than the other well-established particle swarm optimization variants. Finally, compared with other six particle swarm optimization variants, APSO-PDC shows satisfactory performance in optimizing tandem blade. This excellent performance proves that APSO-PDC has a better control of swarm exploration and exploitation abilities.

关键词： adaptive particle swarm optimization population diversity control adaptive control of parameters comprehensive learning strategy adaptive selection of particle roles tandem blade

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adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第6期40卷 10703-10719页

作者： Shobana, J. Murali, M. SRM Inst Sci & Technol Sch Comp Dept CSE Chennai Tamil Nadu India

Text Sentiment analysis is the process of predicting whether a segment of text has opinionated or objective content and analyzing the polarity of the text's sentiment. Understanding the needs and behavior of the target customer plays a vital role in the success of the business so the sentiment analysis process would help the marketer to improve the quality of the product as well as a shopper to buy the correct product. Due to its automatic learning capability, deep learning is the current research interest in Natural language processing. Skip-gram architecture is used in the proposed model for better extraction of the semantic relationships as well as contextual information of words. However, the main contribution of this work is adaptive particle swarm optimization (APSO) algorithm based LSTM for sentiment analysis. LSTM is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are enhanced by presenting the adaptive PSO algorithm. Opposition based learning (OBL) method combined with PSO algorithm becomes the adaptive particle swarm optimization (APSO) classifier which assists LSTM in selecting optimal weight for the environment in less number of iterations. So APSO - LSTM 's ability in adjusting the attributes such as optimal weights and learning rates combined with the good hyper parameter choices leads to improved accuracy and reduces losses. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models.

关键词： Sentimental analysis adaptive particle swarm optimization LSTM skip-gram feature extraction

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adaptive particle swarm optimization For Best Schedule In Algorithmic- Level Synthesis

Adaptive Particle Swarm Optimization For Best Schedule In Al...

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International Conference on Communication and Electronics Systems (ICCES)

作者： Shilpa, K. C. Lakshminarayana, C. Singh, Manoj kumar Manuro Tech Res Bangalore Karnataka India

ISBN: (纸本)9781509010660

this paper presents the adaptive particle swarm optimization for optimal scheduling in Algorithmic level synthesis. adaptive particle swarm optimization algorithm is executed on time constraint scheduling problem using Integer Linear Programming method. The adaptive evolutionary factor, adaptive inertia weight to update velocity equation is implemented. The experimental results evaluate the performance parameters for scheduling with comparison with adaptive particle swarm optimization(APSO) and particle swarm optimization (PSO).The optimal solution for differential equation solver adaptive particle swarm optimization outperforms scheduling optimally in few seconds, with few computations.

关键词： adaptive particle swarm optimization algorithmic level synthesis evolution computation optimal schedule particle swarm optimization second order differential equation solver Introduction

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adaptive particle swarm optimization Neural Network Genetic Algorithm in Nonlinear Function optimization Extreme

Adaptive Particle Swarm Optimization Neural Network Genetic ...

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World Automation Congress (WAC)

作者： Wei, Zhao Ying, Lan Jilin Agr Univ Informat Technol Acad Changchun Peoples R China

ISBN: (纸本)9781467344975

In order to more accurate for nonlinear function extreme, this paper used improved particle swarm optimization neural network combining with genetic algorithm method to solve the problem. In view of the particle swarm optimization algorithm is easy to appear "premature" faults, introducing the adaptive threshold, initializing particles if they were under the constraint conditions, making particles jump out to the optimal value of the position in previous search. Through the experiment, contrasts to the genetic neural network algorithm and traditional BP neural network, this method is faster in convergence and has the smallest prediction error. Finally, combining with genetic algorithm, calculating the extreme value of nonlinear function by using the above three kinds of neural network trained forecast as an individual output fitness value. The adaptive particle swarm optimization neural network proves the most close to the theoretical calculation. It shows that the method is effective.

关键词： adaptive particle swarm optimization neural network genetic algorithm

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Nonlinear system identification using least squares support vector machine tuned by an adaptive particle swarm optimization

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2015年第6期6卷 981-992页

作者： Wang, Shuen Han, Zhenzhen Liu, Fucai Tang, Yinggan Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Hebei Peoples R China Natl Engn Res Ctr Equipment & Technol Cold Strip Qinhuangdao 066004 Hebei Peoples R China

In this paper, we present a method for nonlinear system identification. The proposed method adopts least squares support vector machine (LSSVM) to approximate a nonlinear autoregressive model with eXogeneous (NARX). First, the orders of NARX model are determined from input-output data via Lipschitz quotient criterion. Then, an LSSVM model is used to approximate the NARX model. To obtain an efficient LSSVM model, a novel particle swarm optimization with adaptive inertia weight is proposed to tune the hyper-parameters of LSSVM. Two experimental results are given to illustrate the effectiveness of the proposed method.

关键词： Nonlinear system Identification Least squares support vector machine adaptive particle swarm optimization

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Support vector regression based on optimal training subset and adaptive particle swarm optimization algorithm

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APPLIED SOFT COMPUTING 2013年第8期13卷 3473-3481页

作者： Che, JinXing NanChang Inst Technol Dept Sci Nanchang 330099 Jiangxi Peoples R China

Support vector regression (SVR) has become very promising and popular in the field of machine learning due to its attractive features and profound empirical performance for small sample, nonlinearity and high dimensional data application. However, most existing support vector regression learning algorithms are limited to the parameters selection and slow learning for large sample. This paper considers an adaptive particle swarm optimization (APSO) algorithm for the parameters selection of support vector regression model. In order to accelerate its training process while keeping high accurate forecasting in each parameters selection step of APSO iteration, an optimal training subset (OTS) method is carried out to choose the representation data points of the full training data set. Furthermore, the optimal parameters setting of SVR and the optimal size of OTS are studied preliminary. Experimental results of an UCI data set and electric load forecasting in New South Wales show that the proposed model is effective and produces better generalization performance. (C) 2013 Elsevier B. V. All rights reserved.

关键词： Support vector regression adaptive particle swarm optimization Optimal training subset Parameters selection

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Improved multi-layer hybrid adaptive particle swarm optimization based artificial bee colony for optimizing feature selection and classification of microarray data

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第26期83卷 67259-67281页

作者： Kilicarslan, Serhat Donmez, Emrah Bandirma Onyedi Eylul Univ Fac Engn & Nat Sci Dept Software Engn TR-10200 Balikesir Turkiye

Early diagnosis of cancer allows for easy follow-up of patients' treatment processes. The utilization of microarray gene technology has become increasingly prevalent in the detection of cancer. However, the limited success of classical data mining methods can be attributed to the absence of a linear relationship among the data in microarray datasets. Artificial intelligence-based classification methods are employed to address classification challenges in datasets with a high number of attributes, due to the following reasons: This study presents a novel approach that combines adaptive particle swarm optimization (PSO) with the artificial bee colony (ABC) algorithm to effectively classify microarray datasets. Firstly, Chi2 and ANOVA F-test feature selection algorithms are applied to the microarray datasets to prevent the model from getting stuck in the local optimum. Thus, the most defining features in the dataset are selected, and the dimension is reduced. For classification success increases, we have selected optimal features with the proposed hybrid adaptive ABC + PSO, gray wolf algorithm (GWO), PSO, and ABC algorithms on the new dataset obtained. The last step is performed to classify using a support vector machine (SVM), an artificial neural network (ANN), and a nearest-neighbor (k-NN) algorithm. In the study, eight microarray datasets liver, lung, renal, pancreatic, prostate, breast, colorectal, and brain tumors were used.

关键词： Feature selection Microarray adaptive particle swarm optimization Artificial bee algorithm Classification

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An adaptive particle swarm optimization Algorithm for Solving DNA Fragment Assembly Problem

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CURRENT BIOINFORMATICS 2015年第1期10卷 97-105页

作者： Rajagopal, Indumathy Sankareswaran, Uma Maheswari Coimbatore Inst Technol Dept Elect & Commun Engn Coimbatore 641014 Tamil Nadu India

This paper proposes an efficient method to solve the DNA fragment assembly problem using adaptive particle swarm optimization (APSO). The DNA fragment assembly for shotgun sequencing has been under study with great significance and complexity. It refers to the arrangement of the fragments in an accurate sequence. This fragment assembly problem is an NP-hard combinatorial optimization problem. In this paper, three different methods namely Constant Inertia Weight (CIW), Dynamically Varying Inertia Weight (DVIW) and An adaptive particle swarm optimization (APSO) with Smallest Position Value (SPV) rule are proposed to solve the DNA fragment assembly problem. The objective of the proposed method is to obtain the maximum overlapping score by assembling the fragments. particle swarm optimization algorithm is used to analyze the impact of inertia weight, the cognitive and social components. The PSO algorithm was simulated for each of the methods individually. The experimental results are obvious that the proposed APSO method yields better overlap score when tested with different sized benchmark instances. The proposed APSO method is effective and efficient in assembling the fragments and getting the maximum overlap score when compared to other heuristic techniques.

关键词： adaptive particle swarm optimization bioinformatics DNA fragment assembly inertia weight smallest position value

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Parameter estimation of bilinear systems based on an adaptive particle swarm optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2010年第7期23卷 1105-1111页

作者： Modares, Hamidreza Alfi, Alireza Sistani, Mohammad-Bagher Naghibi Shahrood Univ Technol Fac Elect & Robot Engn Shahrood *** Iran Ferdowsi Univ Mashhad Dept Elect Engn Mashhad 917751111 Iran

Bilinear models can approximate a large class of nonlinear systems adequately and usually with considerable parsimony in the number of coefficients required. This paper presents the application of particle swarm optimization (PSO) algorithm to solve both offline and online parameter estimation problem for bilinear systems. First, an adaptive particle swarm optimization (APSO) is proposed to increase the convergence speed and accuracy of the basic particle swarm optimization to save tremendous computation time. An illustrative example for the modeling of bilinear systems is provided to confirm the validity, as compared with the Genetic Algorithm (GA), Linearly Decreasing Inertia Weight PSO (LDW-PSO), Nonlinear Inertia Weight PSO (NDW-PSO) and Dynamic Inertia Weight PSO (DIW-PSO) in terms of parameter accuracy and convergence speed. Second. APSO is also improved to detect and determine varying parameters. In this case, a sentry particle is introduced to detect any changes in system parameters. Simulation results confirm that the proposed algorithm is a good promising particle swarm optimization algorithm for online parameter estimation. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Bilinear systems Parameter estimation adaptive particle swarm optimization Inertia weight optimization algorithms

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