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firefly algorithm HYBRIDIZED WITH GENETIC algorithm FOR MULTI-OBJECTIVE INTEGRATED PROCESS PLANNING AND SCHEDULING

JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2024年第7期20卷 2310-2328页

作者： Ri, Kwang-won Mun, Kyong-ho Kim Il Sung Univ Inst Nat Sci Pyongyang South Korea Kim Il Sung Univ Fac Elect Automat Pyongyang South Korea

The multi-objective integrated process planning and scheduling (MOIPPS) problem has a huge search space and complex technical constraints. Therefore, there is considerable difficulty in obtaining efficient solutions, and hence, metaheuristic-based solution algorithms have been actively introduced. In our paper, we propose a method to obtain a set of Pareto solutions using a firefly algorithm hybridized with a genetic algorithm for the MOIPPS problem. We considered a MOIPPS problem model that simultaneously optimizes the makespan, total flow time and total tardiness, maximum machine workload and total machine workload. Several different scale instances have been employed to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm has excellent performance in solving the MOIPPS problem.

关键词： Integrated process planning and scheduling multi-objective optimization genetic algorithm firefly algorithm

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firefly algorithm-Driven Development of Resistive Ink-Coated Glass and Mesh Fibers for Advanced Microwave Stealth and EMI Shielding Applications

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JOURNAL OF ELECTRONIC MATERIALS 2024年第11期53卷 7140-7151页

作者： Sahu, Deepanshu Panwar, Ravi Indian Inst Informat Technol Discipline Elect & Commun Engn Design & Mfg Jabalpur 482005 India Indian Inst Technol Sch Mat Sci & Technol BHU Varanasi Varanasi 221005 India

The design and development of efficient microwave-absorbing and electromagnetic interference (EMI) shielding materials and structures to conceal electromagnetic (EM) waves remains a consistent and challenging task. Despite advancements in materials science and microwave engineering, there is a need for optimized materials that offer both effective microwave absorption and EMI shielding while minimizing material layer thickness. This research aims to address this gap by utilizing the firefly algorithm (FFA) to predict the optimal medium properties and thickness of microwave-absorbing and EMI shielding materials under specific constraints. In this context, a comprehensive investigation was carried out at the X-band involving numerical and experimental EM characterization of novel lightweight fiber-based samples. Additionally, the FFA has been applied to optimize these fiber-based microwave structures within the given constraints. Two separate objective functions (OBF) targeting minimum sample thickness, maximum microwave absorption, and shielding effectiveness (SE) bandwidth have been integrated into the FFA to address the thickness-bandwidth trade-off issue. Subsequently, resistive ink-coated glass fiber (IGF) and ink-coated mesh fiber (IMF) were developed and characterized based on the optimal solutions provided by the FFA. Consequently, an optimized IMF sample provides a minimum reflection coefficient (RC) of -19.0 dB at 10.7 GHz with a bandwidth of 2.8 GHz (9.6 to 12.4 GHz) below the -10 dB threshold. Besides, the optimal IGF sample achieves maximum SE of 11 dB at thickness of only 0.8 mm and covers the entire operating band. Furthermore, the response of the proposed structure was assessed for various oblique angles of incidence, revealing significant potential for various practical applications. A strong correlation between measured and theoretical findings underscores the potential of the proposed approach in realizing efficient microwave stealth and EMI shieldi

关键词： Electromagnetic interference shielding fiber firefly algorithm microwave absorption stealth

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firefly algorithm for Movable Antenna Arrays

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第11期13卷 3157-3161页

作者： Hoang, Manh Kha Le, Tuan Anh Thuc, Kieu-Xuan Luyen, Tong Van Yang, Xin-She Ng, Derrick Wing Kwan Hanoi Univ Ind Fac Elect Engn Hanoi 100000 Vietnam Middlesex Univ Fac Sci & Technol Dept Design Engn & Math London NW4 4BT England Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

This letter addresses a multivariate optimization problem for linear movable antenna arrays (MAAs). Particularly, the position and beamforming vectors of the under-investigated MAA are optimized simultaneously to maximize the minimum beamforming gain across several intended directions, while ensuring interference levels at various unintended directions remain below specified thresholds. To this end, a swarm-intelligence-based firefly algorithm (FA) is introduced to acquire an effective solution to the optimization problem. Simulation results reveal the superior performance of the proposed FA approach compared to the state-of-the-art approach employing alternating optimization and successive convex approximation. This is attributed to the FA's effectiveness in handling non-convex multivariate and multimodal optimization problems without resorting approximations.

关键词： Array signal processing Vectors Optimization Linear antenna arrays Brightness Interference Simulation firefly algorithm nature-inspired optimization movable antenna arrays

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firefly algorithm-Based Semi-Supervised Learning With Transformer Method for Shore Power Load Forecasting

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IEEE ACCESS 2023年 11卷 77359-77370页

作者： Li, Weihao Zuo, Youhong Su, Tao Zhao, Weiyou Ma, Xiaoxue Cui, Guangkai Wu, Jiabin Song, Yu Tianjin Survey & Design Inst Water Transport Engn Tianjin 300450 Peoples R China Hehai Univ Dayu Coll Nanjing 210098 Jiangsu Peoples R China Tianjin Univ Technol Sch Elect Engn & Automat Tianjin 300384 Peoples R China

Load forecasting of shore power (SP) plays an important role in the power decision-making of the electrical grid due to docked ships are necessary to plug into the electrical grid. However, obtaining a large amount of labeled data on docked ships is time-consuming, presenting a challenge for Shore Power Load Forecasting. Additionally, multiple raw information entries for docked ships could lead to feature redundancy. To address these issues, we proposed a novel three-stage load forecasting method which includes attributive feature selection, semi-supervised learning (SSL) method for the mean of load distribution prediction, and a transformer-based model for variance prediction. Firstly, firefly algorithm (FA) is adopted to extract representative attribute features of docked ships to deal with the feature redundancy. Next, the selected feature set and label set are divided into two parts: a few labeled data and a large amount of labeled data. And we propose a p -model-based SSL method to predict the load distribution. Finally, we propose a transformer-based model to predict the variance of load distribution. Our model takes into account all historical load data of each docked ship for context learning. Further, we consider that the attribute features would also affect the variance prediction, so the latent features of the p -model are served as the initial condition which concatenates historical load data. We evaluated our model using 328 power load data from various ships that berth at Zhenjiang Port with shore power, totaling approximately 21,521 hours. The experiments prove the accuracy and efficiency of our proposed model, producing promising forecasting results.

关键词： Shore power load forecasting firefly algorithm semi-supervised learning transformer

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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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Energy-aware mobile edge device deployment in health data detection scenario: a DQN optimized firefly algorithm

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COMPUTING 2025年第5期107卷 1-26页

作者： He, Shanshan Li, Huixi Ji, Yuelei Chen, Xingjian Zou, Feifei Zhao, Xia Guangdong Univ Finance & Econ Sch Informat Sci Guangzhou 510320 Peoples R China

During the COVID-19 pandemic, effective monitoring and prediction of the evolution of the epidemic are crucial for public health safety. However, traditional monitoring methods exhibit significant shortcomings in data collection efficiency and energy consumption management. In response to this challenge, this research explores the energy consumption issue of mobile edge devices monitoring residents' health data in densely populated urban centers, aiming to minimize the energy consumption and charging time of edge devices while ensuring comprehensive and efficient collection of individual health data. To achieve this goal, this paper proposes a novel optimization firefly algorithm based on the Deep Q-Network (DQN_FA), which is used to optimize the deployment strategy of mobile edge devices. Through simulation experiments, we compare five mobile edge device deployment strategies based on different swarm intelligence optimization algorithms. The experimental results show that the DQN_FA algorithm exhibits comprehensive advantages in performance compared to other intelligent optimization algorithms.

关键词： Energy consumption firefly algorithm Deep Q-Network Deployment strategy

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firefly algorithm Order Batching Problem Based on Local Search Optimization 24

Firefly Algorithm Order Batching Problem Based on Local Sear...

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5th International Conference on Computing, Networks and Internet of Things (CNIOT)

作者： Miao, Yumo Jia, Luyun Yu, Han Changan Univ Changan Dublin Int Coll Transportat Xian Peoples R China

ISBN: (纸本)9798400716751

Order processing efficiency determines the stability and efficiency of the whole logistics system, which is of great significance for warehouse operation management. The order batch problem(OBP) is a combinatorial optimization problem that arises in the warehouse order picking process. In this paper, we propose to use the firefly algorithm (FA) to solve the order batch processing problem and optimize the algorithm using local search optimization. The algorithm is utilized to verify the effectiveness and efficiency of the firefly algorithm on the order batching problem for an instance. Also, a comparison highlights the usefulness of local search operations for optimizing the firefly algorithm. The experimental results show that the optimized FA exhibits faster convergence speed during the iteration process and is not easy to fall into the local optimum, and the final distance required by the picker is 1113 m. Compared with the unoptimized FA, the optimized algorithm significantly improves the optimization effect while maintaining a lower time overhead. Specifically, the average running time of the optimized FA is 10.8 seconds, which is a significant optimization effect with low additional time overhead. This study provides a new implementation idea for the solution of OBP problems and the application of FA.

关键词： Order Batch Problem firefly algorithm Local Search Optimization Combinatorial Optimization Warehouse Operations Management

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A large-scale multi-objective firefly algorithm based on reward and punishment mechanisms and adaptive dimensional reorganization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第5期27卷 5933-5951页

作者： Pan, Ningkang Lv, Li Xiao, Renbin Wang, Hui Cui, Zhihua Nanchang Inst Technol Sch Informat Engn Nanchang Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Peoples R China

Multi-objective optimization problems with large-scale variables are the focus and difficulty of current research in the field of multi-objective evolutionary algorithms. In order to cope with the problem of "dimensional catastrophe", this paper proposes a large-scale multi-objective firefly algorithm based on reward and punishment mechanisms and adaptive dimensional reorganization. First, a reward and punishment mechanisms is proposed to make full use of the information of the solution itself, so that the population can break the constraint and rapidly approach the Pareto frontier, effectively improving the convergence of the population;then, an adaptive dimensional reorganization mechanism is proposed to select individuals with large differences according to different iteration periods to interact and learn from each other, effectively improving the diversity of the population. In the experimental part, to verify the effectiveness of the algorithm, the UF series and the variable expanded ZDT series, 15 test problems are selected for simulation experiments and compared with 8 advanced large-scale optimization algorithms, the results show that L-MOFA-RA has the advantages of fast convergence speed and high convergence accuracy, and the comprehensive experimental results show that compared with the comparison algorithm L-MOFA-RA has better optimization performance. The comprehensive experimental results show that L-MOFA-RA has better optimization performance compared to the comparison algorithm.

关键词： firefly algorithm Large-scale multi-objective optimization Cauchy mutation Reward and punishment mechanisms Dimensional reorganization

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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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Forecasting medical waste in Istanbul using a novel nonlinear grey Bernoulli model optimized by firefly algorithm

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WASTE MANAGEMENT & RESEARCH 2025年第5期43卷 726-737页

作者： Konyalioglu, Aziz Kemal Ozcan, Tuncay Bereketli, Ilke Univ Strathclyde Hunter Ctr Entrepreneurship Strathclyde Business Sch 199 Cathedral St Glasgow G4 0QU Scotland Istanbul Tech Univ Fac Management Management Engn Dept Istanbul Turkiye Galatasaray Univ Fac Engn & Technol Ind Engn Dept Istanbul Turkiye

Waste management has gained global importance, aligning with the escalating impact of the COVID-19 pandemic and the associated concerns regarding medical waste, which poses threats to public health and environmental sustainability. In Istanbul, medical waste is considered a significant concern due to the rising volume of this waste, along with challenges in collection, incineration and storage. At this juncture, precise estimation of the waste volume is crucial for resource planning and allocation. This study, thus, aims to estimate the volume of medical waste in Istanbul using the nonlinear grey Bernoulli model (NGBM(1,1)) and the firefly algorithm (FA). In other words, this study introduces a novel hybrid model, termed as FA-NGBM(1,1), for predicting waste amount in Istanbul. Within this model, prediction accuracy is enhanced through a rolling mechanism and parameter optimization. The effectiveness of this model is compared with the classical GM(1,1) model, the GM(1,1) model optimized with the FA (FA-GM(1,1)), the fractional grey model optimized with the FA (FA-FGM(1,1)) and linear regression. Numerical results indicate that the proposed FA-NGBM(1,1) hybrid model yields lower prediction error with a mean absolute percentage error value 3.47% and 2.57%, respectively, for both testing and validation data compared to other prediction algorithms. The uniqueness of this study is rooted in the process of initially optimizing the parameters for the NGBM(1,1) algorithm using the FA for medical waste estimation in Istanbul. This study also forecasts the amount of medical waste in Istanbul for the next 3 years, indicating a dramatic increase. This suggests that new policies should be promptly considered by decision-makers and practitioners.

关键词： Medical waste grey forecasting nonlinear grey Bernoulli model firefly algorithm parameter optimization

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