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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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Many-objective firefly algorithm with two archives for computation offloading

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INFORMATION SCIENCES 2025年 689卷

作者： Wang, Hui Liao, Futao Zhang, Shaowei Xiao, Dong Wang, Yun Wang, Wenjun Nanchang Inst Technol Sch Informat Engn Jiangxi Prov Key Lab Smart Water Conservancy Nanchang 330099 Peoples R China Nanchang Inst Technol Sch Business Adm Nanchang 330099 Peoples R China

firefly algorithm (FA) has shown good performance over many optimization problems. However, most studies on FA focus on problems with no more than three objectives. For problems with larger number of objectives, i.e. many-objective optimization problems (MaOPs), FA encounters some difficulties in providing sufficient selection pressure and maintaining population diversity. So far, FA has rarely been used to solve MaOPs. In this paper, we try to propose a many- objective FA variant with two archives (called TaMaOFA) to challenge MaOPs. Inspired by the idea of an improved two-archive method (called Two_Arch2), TaMaOFA employs two archives including convergence archive (CA) and diversity archive (DA), which are used to strengthen convergence and diversity, respectively. By utilizing the search information of CA and DA, a convergence and a diversity biased search strategies are designed, respectively. To enhance the search efficiency, a random search model is constructed based on the two biased search strategies and random attraction. To validate the performance of TaMaOFA, the well-known MaF benchmark is utilized. Results show that TaMaOFA obtains competitive performance when compared with ten other state-of-the-art approaches. Finally, TaMaOFA is applied to computation offloading in edge computing environments. Simulation results demonstrate that TaMaOFA still achieves promising performance.

关键词： firefly algorithm Two-archive Computation offloading Edge computing Many-objective optimization

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Role division approach for firefly algorithm based on t-distribution perturbation and differential mutation

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第2期28卷 1-30页

作者： Chen, Juan Zhao, Jia Xiao, Renbin Cui, Zhihua Wang, Hui Pan, Jeng-Shyang Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Jiangxi Peoples R China Nanchang Key Lab IoT Percept & Collaborat Comp Sma Nanchang 330099 Jiangxi Peoples R China Nanchang Elect Power Key Facil Intelligent Identif Nanchang 330096 Jiangxi Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 030024 Shanxi Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210000 Peoples R China

Aiming at the problems of premature convergence and insufficient diversity of multi-objective firefly algorithm, this paper proposes a role division approach for firefly algorithm based on t-distribution perturbation and differential mutation. The idea of role division in nature is integrated into the firefly algorithm, and different roles are assigned to fireflies with different performances by the role division index, and the best learning mode is assigned according to the different roles, so as to realize the diversified learning of the population. The t-distribution perturbation with different degrees of freedom parameters is used instead of the original random perturbation, which can dynamically adjust the development and exploration ability of the algorithm in different periods. To avoid the algorithm falling into local optimality due to individual convergence at a later stage, differential mutation of the global optimal solution is performed to reduce the probability of the algorithm falling into stagnation and to balance convergence and diversity of the population. MOFA-PD is compared with 5 classical and 12 recent multi-objective optimization algorithms on 18 test functions, and the experimental results show that MOFA-PD has better advantages in convergence and diversity.

关键词： firefly algorithm Multi-objective optimization t-Distribution perturbation Differential mutation

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Parameter tuning of the firefly algorithm by three tuning methods: Standard Monte Carlo, quasi-Monte Carlo and latin hypercube sampling methods a

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JOURNAL OF COMPUTATIONAL SCIENCE 2025年 87卷

作者： Joy, Geethu Huyck, Christian Yang, Xin-She Middlesex Univ Sch Sci & Technol London NW4 4BT England

There are many different nature-inspired algorithms in the literature, and almost all such algorithms have algorithm-dependent parameters that need to be tuned. The proper setting and parameter tuning should be carried out to maximize the performance of the algorithm under consideration. This work is the extension of the recent work on parameter tuning by Joy et al. (2024) presented at the International Conference on Computational Science (ICCS 2024), and the firefly algorithm (FA) is tuned using three different methods: the Monte Carlo method, the Quasi-Monte Carlo method and the Latin Hypercube Sampling. The FA with the tuned parameters is then used to solve a set of six different optimization problems, and the possible effect of parameter setting on the quality of the optimal solutions is analyzed. Rigorous statistical hypothesis tests have been carried out, including Student's t-tests, F-tests, non-parametric Friedman tests and ANOVA. Results show that the performance of the FA is not influenced by the tuning methods used. In addition, the tuned parameter values are largely independent of the tuning methods used. This indicates that the FA can be flexible and equally effective in solving optimization problems, and any of the three tuning methods can be used tune its parameters effectively.

关键词： algorithm firefly algorithm Parameter tuning Monte Carlo method Latin hypercube sampling Optimization

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An Adaptive firefly algorithm for Dependent Task Scheduling in IoT-Fog Computing

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CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES 2025年第3期142卷 2869-2892页

作者： Yousif, Adil Najran Univ Coll Sci & Arts Sharourah Dept Comp Sci Najran 68378 Saudi Arabia

The Internet of Things (IoT) has emerged as an important future technology. IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data. In IoT-Fog computing, resource allocation and independent task scheduling aim to deliver short response time services demanded by the IoT devices and performed by fog servers. The heterogeneity of the IoT-Fog resources and the huge amount of data that needs to be processed by the IoT-Fog tasks make scheduling fog computing tasks a challenging problem. This study proposes an Adaptive firefly algorithm (AFA) for dependent task scheduling in IoT-Fog computing. The proposed AFA is a modified version of the standard firefly algorithm (FA), considering the execution times of the submitted tasks, the impact of synchronization requirements, and the communication time between dependent tasks. As IoT-Fog computing depends mainly on distributed fog node servers that receive tasks in a dynamic manner, tackling the communications and synchronization issues between dependent tasks is becoming a challenging problem. The proposed AFA aims to address the dynamic nature of IoT-Fog computing environments. The proposed AFA mechanism considers a dynamic light absorption coefficient to control the decrease in attractiveness over iterations. The proposed AFA mechanism performance was benchmarked against the standard firefly algorithm (FA), Puma Optimizer (PO), Genetic algorithm (GA), and Ant Colony Optimization (ACO) through simulations under light, typical, and heavy workload scenarios. In heavy workloads, the proposed AFA mechanism obtained the shortest average execution time, 968.98 ms compared to 970.96, 1352.87, 1247.28, and 1773.62 of FA, PO, GA, and ACO, respectively. The simulation results demonstrate the proposed AFA's ability to rapidly converge to optimal solutions, emphasizing its adaptability and efficiency in typical and heavy workloads.

关键词： Fog computing scheduling resource management firefly algorithm genetic algorithm ant colony optimization

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Optimization of BP neural network for fault parameter prediction in nuclear power plants utilizing the firefly algorithm

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JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY 2025年第1期62卷 86-97页

作者： Liu, Zhen Liu, Tao Peng, Guowen Univ South China Sch Resources Environm & Safety Engn Hengyang 421002 Hunan Peoples R China Univ South China Affiliated Hosp 2 Hengyang Peoples R China Univ South China Sch Nucl Sci & Technol Hengyang Peoples R China

The digitalization and intelligence of nuclear power plants (NPPs) enable neural networks to predict transient parameters, aiding operators in emergency responses. This paper addresses the limitations of the back propagation (BP) neural network, which can lead to local optima and reduced accuracy in predicting transient parameters, by introducing the firefly algorithm (FA) to optimize the BP network, creating the FA-BP neural network. Using transient data from PCTRAN, a nuclear power plant accident simulation software, the study compares the predictive performance of the FA-BP network with conventional BP and LSTM neural networks during loss of coolant accident and steam generator tube rupture accidents. The results show that the FA-BP network reduces prediction errors and achieves similar accuracy with shorter prediction times compared to LSTM, due to its simpler structure. The FA's global optimization capabilities enhance the BP network's performance, making it more effective in predicting long-term post-accident parameters. The FA-BP neural network model offers a promising approach for improving the accuracy and speed of transient parameter predictions, contributing to enhanced safety and control in nuclear power plants during emergencies.

关键词： Nuclear power plant transient parameter prediction firefly algorithm BP neural network

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An Explainable LSTM-Based Intrusion Detection System Optimized by firefly algorithm for IoT Networks

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SENSORS 2025年第7期25卷 2288-2288页

作者： Ogunseyi, Taiwo Blessing Thiyagarajan, Gogulakrishan Yibin Univ Sch Elect & Informat Engn Yibin 644000 Peoples R China Cisco Syst Inc Software Engn Austin TX 78759 USA

As more IoT devices become connected to the Internet, the attack surface for cybercrimes expands, leading to significant security concerns for these devices. Existing intrusion detection systems (IDSs) designed to address these concerns often suffer from high rates of false positives and missed threats due to the presence of redundant and irrelevant information for the IDSs. Furthermore, recent IDSs that utilize artificial intelligence are often presented as black boxes, offering no explanation of their internal operations. In this study, we develop a solution to the identified challenges by presenting a deep learning-based model that adapts to new attacks by selecting only the relevant information as inputs and providing transparent internal operations for easy understanding and adoption by cybersecurity personnel. Specifically, we employ a hybrid approach using statistical methods and a metaheuristic algorithm for feature selection to identify the most relevant features and limit the overall feature set while building an LSTM-based model for intrusion detection. To this end, we utilize two publicly available datasets, NF-BoT-IoT-v2 and IoTID20, for training and testing. The results demonstrate an accuracy of 98.42% and 89.54% for the NF-BoT-IoT-v2 and IoTID20 datasets, respectively. The performance of the proposed model is compared with that of other machine learning models and existing state-of-the-art models, demonstrating superior accuracy. To explain the proposed model's predictions and increase trust in its outcomes, we applied two explainable artificial intelligence (XAI) tools: Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), providing valuable insights into the model's behavior.

关键词： explainable artificial intelligence (XAI) intrusion detection system LSTM-based model firefly algorithm LIME SHAP

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Enhancing feature selection for multi-pose facial expression recognition using a hybrid of quantum inspired firefly algorithm and artificial bee colony algorithm

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SCIENTIFIC REPORTS 2025年第1期15卷 1-23页

作者： Mu, Panliang Madaan, Sanjay Babikir Ali, Siddiq Ahmed J., Gowrishankar Khatibi, Ali Alsoud, Anas Ratib Mittal, Vikas Kumar, Lalit Santhosh, A. Johnson Sichuan Univ Coll Comp Sci Natl Key Lab Fundamental Sci Synthet Vis Chengdu 610017 Peoples R China GLA Univ Dept Comp Engn & Applicat Mathura 281001 Uttar Pradesh India Appl Sci Univ Coll Adm Sci Eker Bahrain JAIN Dept Comp Sci Engn Sch Engn & Technol Bangalore Karnataka India Management & Sci Univ Shah Alam Selangor Malaysia Harvard Univ Harvard John A Paulson Sch Engn & Appl Sci TECH Boston MA USA Chandigarh Univ Dept Elect & Commun Engn Mohali 140413 Punjab India Shri Vishwakarma Skill Univ Dept Ind Palwal Haryana India Jimma Univ Jimma Inst Technol Fac Mech Engn Jimma Ethiopia AL Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman Jordan

Facial expression recognition (FER) has advanced applications in various disciplines, including computer vision, Internet of Things, and artificial intelligence, supporting diverse domains such as medical escort services, learning analysis, fatigue detection, and human-computer interaction. The accuracy of these systems is of utmost concern and depends on effective feature selection, which directly impacts their ability to accurately detect facial expressions across various poses. This research proposes a new hybrid approach called QIFABC (Hybrid Quantum-Inspired firefly and Artificial Bee Colony algorithm), which combines the Quantum-Inspired firefly algorithm (QIFA) with the Artificial Bee Colony (ABC) method to enhance feature selection for a multi-pose facial expression recognition system. The proposed algorithm uses the attributes of both the QIFA and ABC algorithms to enhance search space exploration, thereby improving the robustness of features in FER. The firefly agents initially move toward the brightest firefly until identified, then search transition to the ABC algorithm, targeting positions with the highest nectar quality. In order to evaluate the efficacy of the proposed QIFABC algorithm, feature selection is also conducted using QIFA, FA, and ABC algorithms. The evaluated features are utilized for classifying face expressions by utilizing the deep neural network model, ResNet-50. The presented FER system has been tested using multi-pose facial expression benchmark datasets, including RaF (Radboud Faces) and KDEF (Karolinska Directed Emotional Faces). Experimental results show that the proposed QIFABC with ResNet50 method achieves an accuracy of 98.93%, 94.11%, and 91.79% for front, diagonal, and profile poses on the RaF dataset, respectively, and 98.47%, 93.88%, and 91.58% on the KDEF dataset.

关键词： Artificial Bee colony Deep learning Facial expression recognition Feature selection firefly algorithm Metaheuristic Multi-pose expressions Quantum Computing

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UV-Vis spectroscopy coupled with firefly algorithm-enhanced artificial neural networks for the determination of propranolol, rosuvastatin, and valsartan in ternary mixtures

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SCIENTIFIC REPORTS 2025年第1期15卷 1-11页

作者： Serag, Ahmed Abduljabbar, Maram H. Althobaiti, Yusuf S. Almutairi, Farooq M. Alsharif, Shaker T. Alzhrani, Rami M. Ahmed, Marwa F. Almalki, Atiah H. Al Azhar Univ Fac Pharm Pharmaceut Analyt Chem Dept Nasr City Cairo 11751 Egypt Taif Univ Coll Pharm Dept Pharmacol & Toxicol POB 11099 Taif 21944 Saudi Arabia Taif Univ Addict & Neurosci Res Unit Hlth Sci CampusPOB 11099 Taif 21944 Saudi Arabia Univ Hafr Albatin Coll Appl Med Sci Dept Clin Labs Sci Hafr Albatin 39524 Saudi Arabia Umm Al Qura Univ Coll Pharm Dept Pharmaceut Sci Mecca Saudi Arabia Taif Univ Coll Pharm Dept Pharmaceut & Ind Pharm POB 11099 Taif 21944 Saudi Arabia Taif Univ Coll Pharm Dept Pharmaceut Chem POB 11099 Taif 21944 Saudi Arabia

In the present study, a simple, rapid and cost-effective analytical method was developed for the simultaneous determination of three commonly prescribed cardiovascular drugs: propranolol, rosuvastatin and valsartan. The method employed artificial neural networks (ANN) to model the relation between the UV absorption spectra of the drugs and their concentrations. An experimental design of 25 samples was employed as a calibration set, and a central composite design of 20 samples was used as a validation set. The firefly algorithm (FA) was evaluated as a variable selection procedure to optimize the developed ANN models resulting in simpler models with improved predictive performance as evident by lower relative root mean square error of prediction (RRMSEP) values compared to the full spectrum ANN models. Validation of the developed FA-ANN models demonstrated excellent accuracy, precision and selectivity for the quantification of the target analytes as per international conference on harmonisation (ICH) guidelines. Additionally, the greenness, analytical practicality and sustainability of the developed models were assessed using the analytical greenness (AGREE), blue applicability grade index (BAGI) and the red-green-blue (RGB) tools, confirming their environmentally friendly, practical and sustainable nature. This research shed the light on the potential of ANN coupled with UV fingerprinting for the rapid and simultaneous determination of critical cardiovascular drugs posing a significant impact on pharmaceutical quality control and patient monitoring.

关键词： Artificial neural networks Cardiovascular diseases firefly algorithm Greenness assessment Spectrophotometry

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Optimizing Social Media Marketing Strategies Through Sentiment Analysis and firefly algorithm Techniques

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第2期16卷 1131-1140页

作者： Anakal, Sudhir Lakshmi, P. N. S. Fofaria, Nishant Ramesh, Janjhyam Venkata Naga Muniyandy, Elangovan Sanjeera, Shaik El-Ebiary, Yousef A. Baker Patel, Ritesh Sharnbasva Univ Dept Master Comp Applicat Kalaburagi India Aditya Univ Dept CSE Surampalem Andhra Pradesh India Koneru Lakshmaiah Educ Fdn Dept CSE Vaddeswaram Andhra Pradesh India Graph Era Hill Univ Dept CSE Dehra Dun 248002 India Graph Era Deemed Be Univ Dept CSE Dehra Dun 248002 Uttarakhand India Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur Andhra Pradesh India UniSZA Univ Fac Informat & Comp Kuala Terengganu Malaysia Savitribai Phule Pune Univ Sanjivani Coll Engn Pune India

The dramatic expansion of social media platforms reshaped business-to-customer interactions so organizations need to refine their marketing strategies toward maximizing both user engagement and marketing return on investment (ROI). Presentday social media marketing methods struggle to embrace user emotions fully while responding to market variations thus demonstrating the necessity for developing innovative social media marketing tools. Studies seek to boost social media marketing performance through an FA integration with sentiment analysis for content strategy optimization and better user engagement results. This study adopts novel techniques by combining sentiment analysis with the firefly algorithm to optimize marketing strategies in real-time and it represents an underutilized approach in present research. Eventually combined fields generate a sentiment-driven and data-oriented decision- making capability in social media marketing applications. The proposed system combines sentiment analysis technology that measures social media emotion levels alongside the firefly algorithm which applies optimization methods to marketing tactics based on present feedback. The framework operates through dynamic adjustments of content strategies which maximize user engagement. The proposed method demonstrated 98.4% precision in forecasting user engagement metrics and adapting content strategies. Results show traditional marketing strategies yield to these approaches by improving user interaction alongside campaign effectiveness. The research introduces a new optimization method in social media marketing which integrates sentiment analysis with firefly algorithm technology. Research findings suggest this combined methodology brings substantial precision improvements to marketing strategies by offering companies an effective method to optimize digital marketplace outcomes.

关键词： Sentiment analysis firefly algorithm social media marketing optimization user engagement marketing strategies

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