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Investigation of notch effect in the optimum weight design of steel truss towers via particle swarm optimization and Firefly Algorithm

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Frontiers of Structural and Civil Engineering 2025年第3期19卷 358-377页

作者： Elif YILMAZ Musa ARTAR Mustafa ERGÜN Department of Civil Engineering Bayburt UniversityBayburt 69000Turkey

In this study, the optimal weight designs of steel truss towers are determined, considering the notch effect. Thus, the impact of discontinuities in the cross-sections of steel elements on the total weight of the structure is revealed. For this purpose, the optimal weight designs of different truss towers analyzed by other researchers in previous years are reexamined using particle swarm optimization and Firefly Algorithm. The main program where finite element analyses and optimization algorithms are encoded has been developed in MATLAB. Displacement, stress, geometric, and section height constraints are used in optimization methods. The effectiveness of these methods has been demonstrated by comparing both the results in the literature and with each other under un-notched conditions. Subsequently, considering the notch effect on the tension bar with the highest stress capacity in each structure, the impact of stress concentration on the minimum weight sizing of the structure is investigated using these proven methods. When the analysis results of both cases are examined, it is observed that the optimum weights of all structures under the notch effect have slightly increased. The stress concentration around the notch severely raises the nominal stress in the cross-section. In this case, the cross-section becomes insufficient due to the overcapacity, requiring larger profiles. The structure’s weight shows an increasing trend depending on the number of notched elements and the severity of stress concentration. Additionally, SAP2000 software is utilized for numerical simulations of the structures under identical conditions, enhancing the research content and providing further support for the comprehensive design optimization analyses. Consequently, minimizing the adverse effects of notches through careful material selection, proper manufacturing and assembly techniques, and regular maintenance is essential. The effects of notches should be considered in structural anal

关键词： steel truss towers optimum weight design notch effect particle swarm optimization firefly algorithm

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Complex-Order Quantum-Behaved particle swarm optimization With Double Jump-Out Strategy

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Chen, Liping Wu, Xiaobo Lopes, Antonio M. Wang, Da Gu, Panpan Zhu, Min Chen, Yangquan Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Peoples R China Univ Porto Fac Engn LAETA INEGI P-4200465 Porto Portugal Shandong Normal Univ Res Ctr Dynam Syst & Control Sci Jinan 250014 Peoples R China Univ Calif Merced Mechatron Embedded Syst & Automation Lab Merced CA 95343 USA

This paper presents a complex-order quantum-behaved particle swarm optimization (CoQPSO) algorithm, aiming at improving the local exploitation and global exploration abilities of existing optimization methods. The core of the CoQPSO is the adoption of complex-order derivatives within the particles' position adaptation mechanisms. The complex-order derivatives are computed with historical state information of the particles, being naturally suitable for the iterative procedure of intelligent optimization algorithms. To prevent convergence to local optima, a double jump-out strategy is designed so that particles can escape from local attractors. The influence of the algorithm's control parameters on its performance is assessed by means of sensitivity analysis, which comprises a number of value- and rank-based tests on a set of classical benchmark functions and CEC 2022 single-objective global optimization competition test suite. The CoQPSO is compared with other PSO variants in terms of the mean, standard deviation and best value of the solutions. Additionally, the Wilcoxon-rank sum test assesses algorithms' performance differences. The experimental results illustrate the superiority of the CoQPSO in finding optimal solutions.

关键词： Stochastic processes Sensitivity analysis Quantum computing Predictive models Position control particle swarm optimization optimization methods Mathematical models Internet of Things Indexes Complex-order derivative quantum-behavior particle swarm optimization jump-out strategy

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Multi Actors-Critic based particle swarm optimization algorithm

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NEUROCOMPUTING 2025年 624卷

作者： Chen, Hui Shen, Li-Yong Wang, Chunmei Tian, Lei Zhang, Sanguo Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Jinhang Digital Technol Co Ltd Beijing 100028 Peoples R China

This paper introduces the Multi-Actor-Critic-based particle swarm optimization Algorithm (MACPSO), an innovative approach to optimizing particle performance through dynamic parameter control. MACPSO applies a multi-actor-critic framework to enhance exploration and maintain population diversity. It features multiple actor networks for adaptive parameter adjustment, coupled with a single critic network to guide the shared optimization goal. The algorithm incorporates particle grouping for intra-group updates and an inter-group updating scheme to facilitate the exchange of optimal information. Furthermore, MACPSO integrates a mutation mechanism aimed at improving the performance of the least effective particles, thereby ensuring sustained diversity within the population. To validate the superior performance of the proposed algorithm, a comparative study was conducted with nine advanced PSO variants using the CEC2017 benchmark suite. The experimental results demonstrate that MACPSO achieved the highest overall ranking in terms of average error across twenty-nine different benchmark functions. Additionally, numerical, graphical, and statistical analyses confirm that MACPSO outperforms the other nine PSO variants in terms of both effectiveness and robustness.

关键词： particle swarm optimization Parameter control Reinforcement learning Multi-agent

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Enhanced particle swarm optimization Algorithm Based on SVM Classifier for Feature Selection

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CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES 2025年第3期142卷 2791-2839页

作者： Wang, Xing Liu, Huazhen Hussien, Abdelazim G. Hu, Gang Zhang, Li Xian Univ Technol Sch Sci Xian 710054 Peoples R China Linkoping Univ Dept Comp & Informat Sci S-58183 Linkoping Sweden Changan Univ Sch Sci Xian 710064 Peoples R China

Feature selection (FS) is essential in machine learning (ML) and data mapping by its ability to preprocess high-dimensional data. By selecting a subset of relevant features, feature selection cuts down on the dimension of the data. It excludes irrelevant or surplus features, thus boosting the performance and efficiency of the model. particle swarm optimization (PSO) boasts a streamlined algorithmic framework and exhibits rapid convergence traits. Compared with other algorithms, it incurs reduced computational expenses when tackling high-dimensional datasets. However, PSO faces challenges like inadequate convergence precision. Therefore, regarding FS problems, this paper presents a binary version enhanced PSO based on the Support Vector Machines (SVM) classifier. First, the Sand Cat swarm optimization (SCSO) is added to enhance the global search capability of PSO and improve the accuracy of the solution. Secondly, the Latin hypercube sampling strategy initializes populations more uniformly and helps to increase population diversity. The last is the roundup search strategy introducing the grey wolf hierarchy idea to help improve convergence speed. To verify the capability of Self-adaptive Cooperative particle swarm optimization (SCPSO), the CEC2020 test suite and CEC2022 test suite are selected for experiments and applied to three engineering problems. Compared with the standard PSO algorithm, SCPSO converges faster, and the convergence accuracy is significantly improved. Moreover, SCPSO's comprehensive performance far exceeds that of other algorithms. Six datasets from the University of California, Irvine (UCI) database were selected to evaluate SCPSO's effectiveness in solving feature selection problems. The results indicate that SCPSO has significant potential for addressing these problems.

关键词： Feature selection SVM particle swarm optimization sand cat swarm optimization engineering problems

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Improved particle swarm optimization With Redundant Solution Constraints for Robust Data Reconciliation

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IEEE ACCESS 2025年 13卷 33995-34009页

作者： Qian, Xiaoshan Xu, Lisha Cui, Xingli Yichun Univ Coll Phys Sci & Engn Technol Yichun 336000 Jiangxi Peoples R China Hunan Womens Univ Coll Informat Sci & Engn Changsha 410004 Hunan Peoples R China

In the alumina evaporation production process, accurate and reliable process data is crucial for the effectiveness of the soft measurement models used to estimate the concentration of the outlet mother liquor. However, process data is often compromised by harsh industrial conditions such as high temperature, pressure, and alkalinity, leading to measurement errors, missing data, and anomalies. To address these issues, we propose a robust data reconciliation method that utilizes redundant solution constraints and an improved particle swarm optimization (PSO) algorithm with chaotic mutation to correct erroneous measurements and estimate unmeasured variables. This method leverages redundant process data and integrates statistical analysis with process mechanisms to enhance data accuracy. The proposed method was tested on actual industrial data from a multi-effect evaporation system. Results demonstrate that our approach significantly improves the reliability of the reconciled data, providing a robust foundation for soft measurement models. This work presents a novel solution to the challenges of data reconciliation in complex industrial processes, ensuring better accuracy and reliability in the presence of harsh operational conditions.

关键词： particle swarm optimization Evaporation Estimation Data models Accuracy Mathematical models Feeds optimization Heat recovery Measurement uncertainty Chaos mutation particle swarm optimization (CMPSO) robust data reconciliation redundant solution constraints evaporation process optimization soft measurement model

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ML-Assisted particle swarm optimization of a Perturbation-Based Model for Nonlinearity Compensation in Optical Transmission Systems

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2025年第5期43卷 2143-2150页

作者： Redyuk, Alexey Shevelev, Evgeny Danilko, Vitaly Fedoruk, Mikhail Novosibirsk State Univ Artificial Intelligence Res Ctr Novosibirsk 630090 Russia

Nonlinear signal distortions are one of the primary factors limiting the capacity and reach of optical transmission systems. Currently, several approaches exist for compensating nonlinear distortions, but for practical implementation, algorithms must be simultaneously accurate, fast, and robust against various interferences. One established approach involves applying perturbation theory methods to the nonlinear Schr & ouml;dinger equation, which enables the determination of the relation between transmitted and received symbols. In most studies, gradient methods are used to find perturbation coefficients by minimizing the mean squared error between symbols. However, the main parameter characterizing the quality of information transmission is the bit error rate. We propose a modification of the conventional perturbation-based approach for fiber nonlinearity compensation in the form of a two-stage scheme for calculating perturbation coefficients. In the first stage, the coefficients are computed using a single 1D convolutional layer by minimizing the mean squared error. In the second stage, the obtained solution is used as an initial approximation for minimizing the bit error rate using the particle swarm optimization method. In numerical experiments, using the nonlinearity compensation algorithm based on the proposed scheme, we achieved a 0.8 dB gain in the signal-to-noise ratio for a 16QAM 20 x 100 km link with a channel net rate of 222 Gbit/s and demonstrated improved accuracy compared to the single-stage scheme. We estimated computational complexity of the algorithm and demonstrated the relation between its complexity and accuracy. Additionally, we developed a method for learning perturbation coefficients without relying on ideal symbols from the transmitter, instead using the received symbols after hard decision detection.

关键词： Symbols Perturbation methods Mathematical models Nonlinear distortion Signal to noise ratio Optical distortion Optical transmitters Optical fiber theory Fiber nonlinear optics Computational complexity Machine learning nonlinearity compensation nonlinear signal distortions optical transmission system particle swarm optimization perturbation-based model

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A Multilayer Perceptron Feedforward Neural Network and particle swarm optimization Algorithm for Optimizing Biogas Production

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ENERGIES 2025年第4期18卷 1002-1002页

作者： Abdurrakhman, Arief Sutiarso, Lilik Ainuri, Makhmudun Ushada, Mirwan Islam, Md Parvez Univ Gadjah Mada Fac Agr Technol Yogyakarta 55281 Indonesia Inst Teknol Sepuluh Nopember Dept Instrumentat Engn Surabaya 60111 Indonesia Ehime Univ Fac Agr Ehime 7900825 Japan

Efficient biogas production significantly impacts greenhouse gas (GHG) emissions and carbon sequestration by reducing emissions and enhancing carbon storage. Nonetheless, the consistency and optimization of biogas production are hindered by fluctuations in key input variables, namely, pH, moisture content, organic loading rate (OLR), and temperature, which significantly impact the quality of agricultural waste biomass and biogas production. Any fluctuations in these variables can affect biogas productivity. This study aims to provide valuable optimization parameters for maximum biogas production using rice straw and cow dung as agricultural waste biomass for biogas input materials. Therefore, machine learning techniques such as multilayer perceptron feedforward neural networks with a particle swarm optimization (PSO) combination generate optimal values for each variable for maximum biogas production. This study uses three variants of the training function for neural networks, namely gradient descent with momentum and adaptive learning rate, gradient descent with momentum, and gradient descent with adaptive learning rate. The findings reveal that, under an optimum pH value of 6.0000, a humidity of 62.3176%, an OLR of 67.6823 kg.m3/day, and a temperature of 37.0482 degrees C, biogas production has the potential to increase to 2.91 m(3)/day with a high accuracy testing value of R2 = 0.90. These methods in use accurately predict the optimal parameters, with a maximum deviation of 8.48% from experimentally derived values and mean square error (MSE) of 0.0051243. This study emphasizes the benefits of using multilayer perceptron feedforward neural networks and particle swarm optimization to optimize operational parameters and accurately predict biogas production.

关键词： anaerobic digestion artificial neural network particle swarm optimization biogas production

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Performance evaluation of particle swarm optimization variants for trajectory tracking of a cable-driven continuum robot: descriptive, parametric, and non-parametric statistical analysis

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2025年第2期138卷 603-615页

作者： Amouri, Ammar Leksir, Yazid Laib Dit Merabti, Halim Belkhiri, Ayman Cherfia, Abdelhakim Freres Mentouri Constantine 1 Univ Dept Mech Engn Lab Mech BP 325 Ain Bey Rd Constantine 25017 Algeria Univ Larbi Ben Mhidi Lab Act Components & Mat BP 358 Constantine Rd Oum El Bouaghi 04000 Algeria Res Ctr Ind Technol CRTI POB 64 Algiers 16014 Algeria

This paper presents a comprehensive evaluation of particle swarm optimization (PSO) variants for trajectory tracking of a cable-driven continuum robot, utilizing descriptive statistical metrics along with parametric and non-parametric methods for performance assessment. The forward kinematic model of the robot was derived using the constant curvature approach, and the trajectory tracking problem was formulated as an optimization task. Five PSO variants, namely Standard PSO (S-PSO), Weighted PSO (W-PSO), Quantum PSO (Q-PSO), Sine-Cosine PSO (SC-PSO), and Constricted PSO (C-PSO), were reviewed and applied to two optimization scenarios: achieving a target point without considering end-tip orientation as a basic optimization problem and achieving both position and orientation as a more complex optimization problem. To evaluate their effectiveness and robustness, each algorithm was run 30 times per scenario to optimize the arc parameters necessary for tracking 500 randomly selected end-tip poses within the robot's workspace. Descriptive statistics, as well as parametric and non-parametric statistical tests, including one-way ANOVA, Kruskal-Wallis, and Dunn's post hoc test with Holm-Bonferroni correction, were used to compare the PSO variants based on tracking error, execution time, and number of iterations. The analysis showed that for simpler trajectory tracking problems, S-PSO and W-PSO were preferred, but as task complexity increased, these variants became less effective, with Q-PSO and SC-PSO performing better in more complex scenarios. Meanwhile, C-PSO consistently underperformed across all scenarios.

关键词： Continuum robot Cable-driven continuum robot Trajectory tracking particle swarm optimization Descriptive statistics Parametric and non-parametric statistical analysis

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Optimal Allocation of Phasor Measurement Units Using particle swarm optimization: An Electric Grid Planning Perspective

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ENERGIES 2025年第5期18卷 1225-1225页

作者： Haj-ahmed, Mohammed Aldwaik, Mais M. Abualnadi, Dia Univ Jordan Elect Engn Dept Amman 11942 Jordan Philadelphia Univ Civil Engn Dept Jarash 19392 Jordan

In this paper, the particle swarm optimization (PSO) technique is used to optimally allocate phasor measurement units (PMUs) within standard test systems and a real-world power system. PMUs are allocated at system substations in a manner that ensures complete system observability while minimizing installation costs. This study considers IEEE 14-, 30-, and 57-bus standard test systems, along with the Jordanian national high-voltage grid. The optimal allocation was performed separately on the 132 kV and 400 kV buses of the Jordanian grid. Additionally, a novel technique for further minimization of measurement units, considering electric grid planning, is investigated. The results demonstrate that the proposed approach successfully reduces the required number of PMUs while maintaining full system observability. For instance, the IEEE 14-bus system achieved complete observability with only four PMUs, while the IEEE 30-bus and 57-bus systems required ten and seventeen PMUs, respectively. For the Jordanian transmission network, the 400 kV system required only three PMUs, and the 132 kV system required twenty-six PMUs. Furthermore, it was found that integrating power system planning and grid expansion strategies into the PMU placement problem may further reduce installation costs. The results emphasize the effectiveness of the proposed approach in enhancing situational awareness, improving state estimation accuracy, and facilitating reliable protection, control, and monitoring schemes. This study concludes that an optimal PMU allocation strategy shall be incorporated into power system planning studies to maximize cost efficiency while ensuring full observability.

关键词： particle swarm optimization phasor measurement unit power system planning observability smart grid

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Hybrid Approach to Software Fault Prediction Using particle swarm optimization and Fuzzy Time Series

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2025年第0期

作者： Samal, Umashankar GLA Univ Dept Math Bharthia Uttar Pradesh India

Accurate software fault prediction is a critical aspect of proactive maintenance and overall software reliability enhancement. This paper introduces a model that combines particle swarm optimization (PSO) and fuzzy time series (FTS) to predict the number of faults present in the software. FTS, widely recognized for its forecasting capabilities, undergoes performance optimization in the proposed model through PSO, specifically by optimizing the length of intervals. The performance of the proposed model is evaluated on three real-world software fault datasets sourced from open-source software projects, revealing its superiority over alternative models. The evaluation is substantiated by consistently lower values of root mean absolute error, mean absolute error, and median absolute error. This research significantly contributes to the advancement of software reliability forecasting techniques, providing practitioners with a powerful tool for ensuring software reliability and enabling proactive measures for continuous improvement.

关键词： forecasting fuzzy time series particle swarm optimization software reliability

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