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particle swarm optimization Algorithm for Feature Selection Inspired by Peak Ecosystem Dynamics

Computers, Materials & Continua 2025年第2期82卷 2723-2751页

作者： Shaobo Deng Meiru Xie Bo Wang Shuaikun Zhang Sujie Guan Min Li School of Information Engineering Nanchang Institute of TechnologyNanchang330099China

In recent years, particle swarm optimization (PSO) has received widespread attention in feature selection due to its simplicity and potential for global search. However, in traditional PSO, particles primarily update based on two extreme values: personal best and global best, which limits the diversity of information. Ideally, particles should learn from multiple advantageous particles to enhance interactivity and optimization efficiency. Accordingly, this paper proposes a PSO that simulates the evolutionary dynamics of species survival in mountain peak ecology (PEPSO) for feature selection. Based on the pyramid topology, the algorithm simulates the features of mountain peak ecology in nature and the competitive-cooperative strategies among species. According to the principles of the algorithm, the population is first adaptively divided into many subgroups based on the fitness level of particles. Then, particles within each subgroup are divided into three different types based on their evolutionary levels, employing different adaptive inertia weight rules and dynamic learning mechanisms to define distinct learning modes. Consequently, all particles play their respective roles in promoting the global optimization performance of the algorithm, similar to different species in the ecological pattern of mountain peaks. Experimental validation of the PEPSO performance was conducted on 18 public datasets. The experimental results demonstrate that the PEPSO outperforms other PSO variant-based feature selection methods and mainstream feature selection methods based on intelligent optimization algorithms in terms of overall performance in global search capability, classification accuracy, and reduction of feature space dimensions. Wilcoxon signed-rank test also confirms the excellent performance of the PEPSO.

关键词： Machine learning feature selection evolutionary algorithm particle swarm optimization

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particle swarm optimization based on particle perturbation and elite preservation strategies for dynamic optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 279卷

作者： Li, Fei Pan, Hao Ji, Yilong Ouyang, Haibin Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243032 Peoples R China Anhui Univ Technol Anhui Key Lab Low Carbon Met & Solid Waste Resourc Maanshan 243032 Peoples R China Anhui Prov Engn Lab Intelligent Demolit Equipment Maanshan 243032 Peoples R China Anhui Key Lab Power Elect & Mot Control Maanshan 243032 Peoples R China Guangzhou Univ Sch Mech & Elect Engn Guangzhou 51006 Peoples R China

Multi-population methods have an effective ability to solve various dynamic optimization problems. However, most multi-population methods still have much room for improvement in their global search capabilities. To address this issue, a particle swarm optimization based on particle perturbation and elite preservation strategies (PSO-PE) is designed to solve dynamic optimization problems. The particle perturbation strategy is first designed to enable the optimal particles of each explorer sub-population to escape from local optima. Secondly, the elite preservation strategy is proposed to prevent multiple sub-populations from converging on the same peak by preserving elite particles while excluding others. Finally, an elite particle migration strategy is proposed to cope with environmental changes. Specifically, the exploiter sub-populations can be used to trace the optimal solution in a new environment while the explorer sub-populations are adopted to maintain the diversity. We conduct the experiment on the Moving Peak Benchmark (MPB) and Generalized Moving Peaks Benchmark (GMPB) problems. The comprehensive results have demonstrated the superior performance of the proposed method compared with some related dynamic optimization algorithms.

关键词： Dynamic optimization problems particle swarm optimization particle perturbation strategy Elite preservation strategy Elite particle migration strategy

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particle swarm optimization for preference rankings

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ADVANCES IN DATA ANALYSIS AND CLASSIFICATION 2025年 1-25页

作者： Romano, Maurizio Conversano, Claudio Siciliano, Roberta D'Ambrosio, Antonio Univ Cagliari Dept Business & Econ Cagliari Italy Univ Naples Federico II Dept Elect & Informat Technol Naples Italy Univ Naples Federico II Dept Econ & Stat Naples Italy

Preference learning, or the analysis of preference rankings, is gaining more and more importance in various scientific disciplines. Preference learning methods allow predicting preferences on a set of alternatives. The ingredients are a pool of evaluators and a set of objects or items to be ranked in order of preference. The rank aggregation problem must be solved in order to aggregate preferences or rankings with the aim to find a consensus or collective decision. Branch-and-bound-like procedures are usable up to problems involving a relatively small number of objects, say less than 200. When the number of items becomes very large, the rank aggregation problem becomes increasingly difficult to approach so that it is universally recognized as an NP-hard problem. Several heuristic methods have been proposed to provide increasingly accurate solutions. These assume the Kemeny axiomatic approach that better deals with tied rankings. In this paper, we adopt a strategy based on particle swarm optimization by adapting procedures born to solve optimization problems in continuous spaces to discrete combinatorial optimization problems. A simulation study shows the performance of the proposed algorithm in a controlled environment. A benchmarking complex data set and two real world data sets with large number of items are considered. As a result, the proposed algorithm provides significant savings in computational time and comparable accuracy with respect to other recent algorithms.

关键词： Preference learning Kemeny problem Tied rankings Heuristics particle swarm optimization

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particle swarm optimization-Based Content Delivery Protocol for UAV VAENTs

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IEEE ACCESS 2025年 13卷 1594-1608页

作者： Shin, Yongje Choi, Hyun-Seok Nam, Youngju Lee, Euisin Chungbuk Natl Univ Res Inst Comp & Informat Commun Cheongju 28644 South Korea Chungbuk Natl Univ Sch Informat & Commun Engn Cheongju 28644 South Korea Kunsan Natl Univ Sch Software Gunsan 54150 South Korea

As the capabilities of mobile devices and mobile communication techniques expand, vehicular ad hoc networks (VANETs) play an increasingly significant role in transportation and traffic management, particularly in Intelligent Transportation Systems (ITS). However, the rapid topology changes, high vehicle mobility, and frequent link disconnections make routing in VANETs a challenging task, necessitating the development of efficient routing protocols. Moreover, as VANET services provided in our daily lives become more extensive and situations in which they are provided simultaneously to an increasing number of users become more frequent, numerous data delivery optimization algorithms have been researched to address these issues. Alongside this, the research related to Unnamed Aerial Vehicles (UAVs) has also been utilized as a solution to solve the physical data link disconnection problem of VANETs. In this paper, we propose a routing protocol called PSUV (particle swarm UAV-VANET). First, we have solved the software-related issues of VANETs by measuring data transmission availability of vehicles existing in the topology and conducting topology optimization by creating a vehicle routing table, utilizing a mathematical value calculation formula that combines particle swarm optimization (PSO) from swarm Intelligence (SI) and the directional vector information of the vehicle. Furthermore, by utilizing UAVs, we address the physical disconnectivity issues that arise during data transmission in VANETs, thereby minimizing the delay and maximizing the total data rate under realistic conditions. Finally, extensive simulations are conducted to validate the superiority of the proposed protocol in various circumstances in an NS-3 simulation. Simulation results indicate that the proposed protocol solves the challenging issues in realistic VANET with significant improvement.

关键词： Routing protocols Topology Vehicular ad hoc networks optimization particle swarm optimization Delays Routing Quality of service Roads Network topology swarm intelligence particle swarm optimization vehicular ad-hoc networks unmanned aerial vehicles topology evaluation metrics

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particle swarm optimization-based load balance-aware energy-efficient scheduling for mobile crowd computing

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2025年第2期101卷 159-176页

作者： Pramanik, Pijush Kanti Dutta Biswas, Tarun Choudhury, Prasenjit Galgotias Univ Sch Comp Applicat & Technol Greater Noida 203201 Uttar Pradesh India Indian Inst Informat Technol Ranchi Dept Comp Sci & Engn Ranchi 835217 India Natl Inst Technol Durgapur Dept Comp Sci & Engn Durgapur India

Due to their increasing computational power and energy-efficient hardware, today's smart mobile devices (SMDs) are replacing desktops and laptops as casual computing devices. Moreover, a cluster of such powerful SMDs can garner substantial high-performance computing (HPC). Such an HPC is achieved by utilizing publicly owned SMDs in mobile crowd computing (MCC). Here, a large computing-intensive task is divided and scheduled for the available SMDs for execution, and the results are recollected. This approach provides an economical and sustainable HPC. However, battery-powered constrained energy is a great hindrance to achieving this goal. Therefore, in the MCC, it is crucial to minimize the overall energy consumption to complete the task. This can be achieved to some extent by optimizing task scheduling to the appropriate SMDs. However, considering only energy efficiency might lead to an enormous load imbalance among SMDs, i.e., the most energy-efficient SMDs would be overloaded most of the time. Considering this, in this paper, we present a modified particle swarm optimization (PSO)-based scheduling algorithm to minimize the overall energy consumption among a set of SMDs designated to execute a set of MCC tasks while maintaining a satisfactory load balance level. Extensive simulations with both synthetic and real data sets are carried out to analyze and validate the proposed method. The work was compared with popular heuristic (minimum completion time (MCT), MinMin, MaxMin, and preconditioned progressive iterative approximation (PPIA)) and metaheuristic (genetic algorithm (GA)) optimization algorithms, which yields significant improvements over others in terms of the considered objectives. In addition, an analysis of variance (ANOVA) test is conducted to provide further evidence regarding the distinctiveness of the proposed algorithm.

关键词： Crowd computing mobile cloud mobile computing task scheduling energy efficiency load balance particle swarm optimization

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particle swarm optimization for hybrid mutant slime mold: An efficient algorithm for solving the hyperparameters of adaptive Grey-Markov modified model

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INFORMATION SCIENCES 2025年 689卷

作者： Hu, Gang Wang, Sa Zhang, Jiulong Houssein, Essam H. Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Minia Univ Fac Comp & Informat Al Minya Egypt

The grey prediction model is a scientific and effective prediction method for small amounts of incomplete data. In this paper, we proposed a particle swarm optimization for mixed mutant slime mold (namely SCPSO) combined with an extended adaptive Grey-Markov modified model called AOPGM(1,1,lambda,mu), which constructs an AOPGM(1,1,lambda,mu) based hyperparameters optimization for SCPSO. Firstly, the values of the exponential cumulative generation operator coefficient lambda and the background value mu of the adaptive Grey-Markov modified model are refined, and lambda and mu are also calculated using SCPSO. Secondly, a competitive SCPSO is formed by introducing a good point set, identifying attack strategy, mutation slime mold algorithm, and Sigmoid function into particle swarm optimization. It was also compared with the improved optimization algorithms on the CEC2020 test set and with classical and newer intelligent algorithms on the CEC2022 test set. The results of numerical experiments show that SCPSO can be considered a competitive and promising global optimization algorithm. Finally, SCPSO is used to optimize the hyperparameters of the AOPGM(1,1,lambda,mu) and applied to the prediction of oil production and proven reserves in China. In terms of optimizing the hyperparameters of the AOPGM(1,1,lambda,mu), the numerical experimental results of this method were better than the prediction results of 7 intelligent algorithms and the results of 4 prediction models. The validity of the proposed methodology was verified through five evaluation indicators.

关键词： particle swarm optimization Grey prediction model Adaptive Grey-Markov modified model Oil prediction

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particle swarm optimization based Linear Quadratic Gaussian Semi Active Control Algorithm for Space Framed Structure using Magneto-Rheological Damper

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JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2025年第5期13卷 1-26页

作者： Chaudhuri, P. Manikandan, R. Sakthivel, R. Indian Inst Technol Civil Engn Dept Kharagapur 721302 India Bharathiar Univ Dept Appl Math Coimbatore 641046 India Vignans Fdn Sci Technol & Res Civil Engn Dept Guntur 522213 India

PurposeIn this study, we explore the potential of structural vibration control for mitigating seismic hazards in civil engineering structures. The traditional control algorithms face challenges due to their mathematical complexity and the intricate dynamic behavior of structures. Therefore, to address these issues, the research proposes an Linear Quadrat ic Gaussian based particle swarm Optimized (LQG-PSO) semi-active control algorithm for managing the force exerted by Magneto-rheological (MR) dampers. The main goal is to enhance the structural response and stability of a benchmark space-framed structure encountered with various earthquake time ***, the Linear Quadratic Gaussian (LQG) design employs additive white Gaussian noises as inputs to stabilize the control system and determine the ideal control force. Moreover, the proposed LQG-PSO semi-active control algorithm efficiently assesses the optimum value of weighting matrices and demonstrates superior convergence capabilities compared to other optimization techniques. In support of the proposed control methodology, numerical and experimental investigations are carried out. Also, we compare the proposed methodology with a Linear Quadratic Gaussian-based constrained binary-coded Genetic Algorithm (LQG-GA) and a passive Tuned Liquid Column Damper (TLCD) design-based *** resultant outcomes depicts that constructed method performs well and visualizes the significant reductions in the top floor peak displacement, Fast Fourier Transform (FFT) and Root Mean Square(RMS) of Power Spectral Density (PSD) around 90-97% than the conventional one and LQG-GA based controller. Illustrations through the Monte-Carlo simulation conducted based on 10000 experiments confirm that the probability of occurring peak displacement less than the minimum displacement value obtained by the proposed LQG-PSO semi-active control algorithm is extremely high in all simulations compared to that of LQG-GA

关键词： Semi-active control and vibration control Magneto-rheological damper particle swarm optimization Linear quadratic Gaussian Auto-regressive exogenous input model

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particle swarm optimization based analysis to unlocking the neutrino mass puzzle using SU(2) L x U(1) Y x A4 x S2 x Z10 x Z3 flavor symmetry

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SCIENTIFIC REPORTS 2025年第1期15卷 1-15页

作者： Aslam, Muhammad Waheed Zafar, Abrar Ahmad Aslam, Muhammad Naeem Bhatti, Abdul Aziz Hussain, Talab Iqbal, Munawar Abdo, Mohammed S. Univ Punjab Dept Phys Lahore Pakistan Lahore Garrison Univ Dept Math Lahore Pakistan Univ Punjab Ctr High Energy Phys Lahore Pakistan Univ Punjab Coll Stat Sci Lahore Pakistan Hodeidah Univ Dept Math POB 3114 Al Hudaydah Yemen

New research has highlighted a shortfall in the Standard Model (SM) because it predicts neutrinos to have zero mass. However, recent experiments on neutrino oscillation have revealed that the majority of neutrino parameters indeed indicate their significant mass. In response, scientists are increasingly incorporating discrete symmetries alongside continuous ones for the observed patterns of neutrino mixing. In this study, we have examined a model within SU(2) L x U(1) Y x A4 x S2 x Z10 x Z3 symmetry to estimate the neutrino masses using particle swarm optimization technique for both mass hierarchy of neutrino. This model employed a hybrid seesaw mechanism, a combination of seesaw mechanism of type-I and type-II, to establish the effective Majorana neutrino mass matrix. After calculating the mass eigenvalues and lepton mixing matrix upto second order perturbation theory in this framework, this study seeks to investigate the scalar potential for vacuum expectation values (VEVs), optimize the parameters, UPMNS matrix, neutrino masses: m' 1 (N)(upper) = 4.0000 x 10-2 eV, m' 2(N)(upper) = 4.0000 x 10-2 eV, m' 3 (N)(upper) = 4.0000 x 10-2 eV, m' 1(I)(upper) = 3.8628 x 10-2 eV, m' 2 (I)(upper) = 4.0548 x 10-2 eV, m' 3(I)(upper) = 3.8532 x 10-2 eV, m' 1 (N)(lower) = 2.0000 x 10-2 eV, m' 2 (N)(lower) = 2.0000 x 10-2 eV, m' 3 (N)(lower) = 2.0000 x 10-2 eV, m' 1 (I)(lower) = 1.1049 x 10-2 eV, m' 2 (I)(lower) = 3.9298 x 10-2 eV and m' 3 (I)(lower) = 9.6381 x 10-3 eV, effective neutrino mass parameters: < mee > N(upper) = 40.0050 meV, < m beta > N(upper) = 40.0025 meV, < mee > I(upper) = 39.2181 meV, < m beta > I(upper) = 39.2257 meV, < mee > N(lower) = 20.0024 meV, < m beta > N(lower) = 20.0012 meV, < mee > I(lower) = 19.6608 meV, < m beta > I(lower) = 23.5908 meV, are predicted for both mass hierarchy through particle swarm optimization (PSO), showing strong agreement with recent experimental findings. The Dirac CP-violating phase d is measured to be -pi/2.

关键词： Discrete symmetry Neutrino mixing particle swarm optimization

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particle swarm optimization 15

Particle swarm optimization

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17th Genetic and Evolutionary Computation Conference, GECCO 2015

作者： Engelbrecht, A.P. Department of Computer Science University of Pretoria South Africa

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particle swarm optimization

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AIAA JOURNAL 2003年第8期41卷 1583-1589页

作者： Venter, G Sobieszczanski-Sobieski, J Vanderplaats Res & Dev Inc Colorado Springs CO USA NASA Langley Res Ctr Analyt & Computat Methods Branch Hampton VA 23665 USA

How the search algorithm known as particle swarm optimization performs is demonstrated. particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. Improvements are contributed to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of the algorithm are made. Results of numerical experiments for both continuous and discrete applications are presented. The results indicate that the particle swarm optimization algorithm does locate the constrained minimum design in both continuous and discrete applications and problems with multiple local minima, with very good precision. Additionally, particle swarm optimization has the potential of efficient computation with very large numbers of concurrently operating processors.

关键词： particle swarm optimization Structural optimization Search Algorithm Cantilever Beam Number of particles Genetic Algorithm Parallel Computing Environment Allowable Stress Bending Moment Annealing Process

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