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A diversity enhanced hybrid particle swarm optimization and crow search algorithm for feature selection

APPLIED INTELLIGENCE 2023年第17期53卷 20535-20560页

作者： Osei-kwakye, Jeremiah Han, Fei Amponsah, Alfred Adutwum Ling, Qing-Hua Abeo, Timothy Apasiba Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang Peoples R China Jiangsu Key Lab Secur Technol Ind Cyberspace Zhenjiang Peoples R China Univ Profess Studies Informat Technol Studies Dept Accra Ghana Univ Mines & Technol Comp Sci & Engn Dept Tarkwa Ghana Jiangsu Univ Sci & Technol Sch Comp Sci Zhenjiang Peoples R China Tamale Tech Univ Fac Appl Sci & Technol Dept Comp Sci Tamale Ghana

Feature Selection (FS) is choosing a subcategory of features purposed to construct a machine learning model. Among the copious existing FS algorithms, Binary particle swarm optimization algorithm (BPSO) is prevalent with applications in several domains. However, BPSO suffers from premature convergence that affects exploration, resulting in dilapidation. In this current work, we boost the exploration of BPSO, incorporating the intelligence of crows to hide their food sources from other crows and predators and maintain diversity by implementing a clustering strategy. The clustering technique guarantees that the starting population is evenly distributed over the feature space while including more promising features. Additionally, suppose a crow realizes another crow or a predator is tracking it. In that case, the crow moves randomly to evict the stalker, leading to a better exploration of unexplored regions within the search space. We named the proposed method Hybrid particle swarm optimization and Crow Search algorithm with clustering initialization strategy (HPSOCSA-CIS). To evaluate the performance of HPSOCSA-CIS, 15 standard UCI datasets are utilized, and the outcomes are compared with recently proposed hybrid and standard optimization algorithms. From observation, HPSOCSA-CIS outperforms the comparing approaches for feature selection challenges on representative datasets that fall in the three-category based on dimensions. The HPSOCSA-CIS improves performance in terms of mean classification accuracy by 8.87%, 17.5%, and 21.90% on Low, medium, and high dimensional datasets, respectively.

关键词： particle swarm optimization algorithm Crow search algorithm Clustering Feature selection

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An artificial-intelligence-driven product design framework with a synergistic combination of Genetic algorithm and particle swarm optimization

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SOFT COMPUTING 2023年第23期27卷 17621-17638页

作者： Liu, Yuge Kim, Kiesu Silla Univ Sch Design Busan 46958 South Korea

In recent years, China's rapid advancements in artificial intelligence (AI) technology have positioned it as a global leader in information processing, intelligent chip development, and deep learning technologies. The pervasive impact of AI has led to transformative shifts in various domains of life, with its integration across industries substantially enhancing overall production efficiency. Particularly evident in product design, the infusion of AI technology has catalyzed significant productivity improvements. This research investigates the amalgamation of Genetic algorithms (GA) and particle swarm optimization (PSO) within the framework of AI technology to establish a robust product design system. The system aims to enhance the efficiency and efficacy of design processes by leveraging the complementary strengths of GA and PSO. This integration has propelled product design into accelerated evolution and modernization. The study delves into AI's application of deep neural networks in product design within the context of the digital age. Artificial intelligence technology is used in product design in the context of the Internet. First, the concept of AI technology is briefly introduced, and DNN algorithms are described in detail, including the PSO and Genetic algorithms. Second, the product design and development process based on artificial intelligence technology, design guidelines, and technical features are mainly described. Finally, using the seat as the research object, the application effect of artificial intelligence technology in product design is analyzed, and 10 subjective evaluation questions are set to experiment. Regarding material texture, the highest score for the B3 Barcelona chair is 4.3. Users prefer leather seats with diverse materials, and the score for painted wood materials is lower. From the product structure and shape analysis, the highest score is the B3 Barcelona chair, which has a value of 4.1, and the lowest score is the B1 red and blue

关键词： Artificial intelligence Internet particle swarm optimization algorithm Genetic algorithm Product design

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Calculation and optimization of Electric Helicopter Flight Performance Based on particle swarm algorithm 36

Calculation and Optimization of Electric Helicopter Flight P...

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36th Chinese Control and Decision Conference (CCDC)

作者： Ji, Ming Qi, Yiwen Zhang, Qingxin Wang, Feng Zhou, Guoqing Lv, Yanbo Shenyang Aerosp Univ Sch Automat Shenyang 110136 Peoples R China Liaoning Gen Aviat Acad Shenyang 110136 Peoples R China

ISBN: (纸本)9798350387780;9798350387797

As energy issues become increasingly prominent, the electrification of aviation aircraft has gradually become a research hotspot, and electric helicopters are also in a stage of rapid development. Based on the flight performance analysis method of fuel-powered helicopters and combined with the characteristics of electric power system, this paper proposes a calculation method for the flight performance of electric helicopters and establishes an analysis model. This model can demonstrate the performance of electric helicopters in three aspects: vertical flight, climbing flight and horizontal flight. Then, the particle swarm optimization (PSO) algorithm is used to optimize the overall flight performance of the helicopter. It can allocate weights reasonably according to design requirements and selectively optimize key performance indicators. The results show that the adopted optimization algorithm has good effect and applicability to the overall parameters optimization problem of electric helicopters.

关键词： Electric helicopters Flight performance analysis particle swarm optimization algorithm Overall parameters optimization

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Performance comparison of particle swarm optimization and genetic algorithm for inverse surface radiation problem

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2015年 88卷 330-337页

作者： Lee, Kyun Ho Kim, Ki Wan Sejong Univ Dept Aerosp Engn Seoul 143741 South Korea Agcy Def Dev Daejeon 305600 South Korea

The heat transfer mechanism of thermal radiation is directly related to either the emission and propagation of electromagnetic waves or the transport of photons. Depending on the participation of the medium in space, thermal radiation can be classified into two forms, which are surface and gas radiation, respectively. In the present study, unknown surface radiation properties are estimated by an inverse analysis for a surface radiation in an axisymmetric cylindrical enclosure. For efficiency, the repulsive particle swarm optimization (RPSO) algorithm, which showed an outstanding effectiveness in the previous inverse gas radiation problem, is adopted as an inverse solver. By comparing the convergence rates of an objective function and the estimated accuracies with the results of the hybrid genetic algorithm (HGA) and the particle swarm optimization (PSO) method, the performance of the RPSO algorithm is verified to be quite an efficient method as the inverse solver when applied to the retrieval of unknown properties of the surface radiation problem. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Surface radiation Inverse analysis particle swarm optimization algorithm Genetic algorithm

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A Novel Quantum-behaved particle swarm optimization algorithm

A Novel Quantum-behaved Particle Swarm Optimization Algorith...

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第十四届分布式计算及其应用国际学术研讨会

作者： Jing Zhao Hong Liu School of Information Qilu University of Technology Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology College of Information Science and Engineering Shandong Normal University

A novel Quantum-behaved particle swarm optimization algorithm with probability(P-QPSO) is introduced to improve the global convergence property of QPSO. In the proposed algorithm, all the particles keep the original evolution with large probability, and do not update the position of particles with small probability, and re-initialize the position of particles with small probability. Seven benchmark functions are used to test the performance of P-QPSO. The results of experiment show that the proposed technique can increase diversity of population and converge more rapidly than other evolutionary computation methods.

关键词： particle swarm optimization algorithm quantum-behaved probability benchmark function

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Network Traffic Forecasting Based on Support Vector Machine Optimized by Keeping Activity particle swarm optimization algorithm

Network Traffic Forecasting Based on Support Vector Machine ...

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2015 International Conference on Advanced Educational Technology and Information Engineering(AETIE 2015)

作者： S.X.Xu X.J.Wang Xuchang Vocational Technical College

Keeping particle swarm alive support vector machine optimized algorithm network traffic forecasting model(EPSO-SVM) is proposed. First, building support vector machine learning sample by calculating the delay time and embedding dimension, second, learning network flow training set by using the maintaining the vitality of particle swarm optimization support vector machine, at last, validating performance EPSO-SVM's by using set of the network traffic tests. The results showed that the proposed model can improve the forecasting precision of network traffic. It has great practical application value.

关键词： particle swarm optimization algorithm vector machine forecast

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Cloud Adaptive Chaos particle swarm optimization algorithm for Economic Load Dispatch of Power System

Cloud Adaptive Chaos Particle Swarm Optimization Algorithm f...

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2015 International Power,Electronics and Materials Engineering Conference(IPEMEC 2015)

作者： Zhucai QIN Department of Automation Wuhan University of Technology

ISBN: (纸本)9781510806474

A cloud adaptive chaos particle swarm optimization algorithm is proposed for economic load dispatch problems of power system,which has the characteristics of nonlinear,non-convex and *** cloud generator was used to adaptively adjust the inertia weight of each particles,so as to optimize their optimization direction and improve the convergence speed of the algorithm;and the chaotic variation operation was introduced to adjust the particle's positions,so as to improve the diversity of the solution and avoid falling into local *** of 6 unit system demonstrates that the proposed algorithm has high accuracy and quick speed used in economic load dispatch of power system.

关键词： Economic Load Dispatch Cloud Adaptive Chaotic Variation particle swarm optimization algorithm

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A Novel Quantum-behaved particle swarm optimization algorithm

A Novel Quantum-behaved Particle Swarm Optimization Algorith...

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The 14th International Symposium on Distributed Computing and Applications to Business,Engineering and Science(DCABES 2015)(第十四届分布式计算及其应用国际学术研讨会)

作者： Jing Zhao Hong Liu School of Information Qilu University of Technology Shandong Provincial Key Laboratory for Distribut Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology College of Inf

ISBN: (纸本)9781467365949

A novel Quantum-behaved particle swarm optimization algorithm with probability(P-QPSO)is introduced to improve the global convergence property of *** the proposed algorithm,all the particles keep the original evolution with large probability,and do not update the position of particles with small probability,and re-initialize the position of particles with small *** benchmark functions are used to test the performance of *** results of experiment show that the proposed technique can increase diversity of population and converge more rapidly than other evolutionary computation methods.

关键词： particle swarm optimization algorithm quantum-behaved probability benchmark function

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Network intrusion detection algorithm based on LightGBM model and improved particle swarm optimization

Network intrusion detection algorithm based on LightGBM mode...

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2024 IEEE Cyber Science and Technology Congress

作者： Geng, Yican Hu, Haoyang Ge, Zhaoxuan Lian, Zhichao Nanjing Univ Sci & Technol Sch Cyber Sci & Engn Wuxi Jiangsu Peoples R China

ISBN: (纸本)9798331532109;9798331532093

In order to solve the problem of insufficient adaptive ability of the network intrusion detection model, the large-scale fast search capability of the particle swarm optimization (PSO) algorithm is introduced into the intrusion detection model. In order to solve the problem that PSO is easy to fall into local optimality, the genetic algorithm (GA) is introduced. An improved particle swarm optimization (GAPSO) algorithm based on genetic algorithm is proposed. This algorithm optimizes the parameters that are difficult to adjust in the lightweight gradient boosting machine (LightGBM) algorithm, so that the PSO algorithm can quickly converge while ensuring the optimization accuracy, and obtain the optimal network intrusion detection model. Experimental results show that GAPSO is more effective than the basic PSO algorithm when dealing with high-dimensional, complex structure optimization problems.

关键词： Supervised learning genetic algorithm particle swarm optimization algorithm intrusion detection parameter optimization

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An improved reactive force field parameter optimization framework based on simulated annealing and particle swarm optimization algorithms

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COMPUTATIONAL MATERIALS SCIENCE 2025年 251卷

作者： Sun, Qinhao Zhong, Jinhuan Shi, Pengfei Xu, Huajie Wang, Yang Southwest Jiaotong Univ Res Inst Frontier Sci Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China

Atomic-scale simulations are important tools for microscopic phenomena study and material design, especially the cost-effective and large-scale reactive force field (ReaxFF). However, the poor transferability and tedious training process of ReaxFF parameters constrain its accuracy and application, urgently requiring more efficient automatic optimization methods. In this study, we propose a multi-objective optimization method that combines simulated annealing algorithm (SA) and particle swarm optimization algorithm (PSO) to optimize the ReaxFF parameters. Moreover, we innovatively introduce a concentrated attention mechanism (CAM) to improve the accuracy of parameter optimization. Finally, this study selects the H/S system as the testing target to evaluate the accuracy and efficiency of the above algorithm. It is found that our algorithm is faster and more accurate than traditional metaheuristic methods. Our automated optimization scheme efficiently optimizes ReaxFF parameters, providing crucial support for atomic-scale simulations.

关键词： ReaxFF Molecular dynamics Simulated annealing algorithm particle swarm optimization algorithm

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