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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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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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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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A multi-objective parameter optimization and decision-making method for multi-pass end milling with firefly algorithm and Markov clustering

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2024年第4期46卷 1-22页

作者： Cai, Xu-Lin Yang, Wen-An Yang, Xue-Feng You, You-Peng Nanjing Univ Aeronaut & Astronaut Sch Mech & Elect Engn Natl Key Lab Sci & Technol Helicopter Transmiss Nanjing 210016 Peoples R China

Machining parameter optimization holds profound significance within the domain of milling operations, as the selection of these parameters can exert a substantial influence on both production efficiency and the quality of produced components. Notably, the integration of the chatter stability constraint into the optimization model remains underrepresented in the existing literature, despite its pivotal role in ensuring tool safety and machining quality. Furthermore, while several multi-objective optimization (MOO) algorithms have been devised, their effectiveness is often compromised due to the complexity of the milling parameter optimization model. In response to these challenges, this study developed an updated full discretization method (UFDM)-based three-dimensional (3-D) stability prediction model for multi-pass end milling. A milling parameter optimization model is meticulously formulated herein, which simultaneously optimizes the number of passes, feed rate, spindle speed, axial cutting depth, and radial cutting depth in multi-pass end milling while complying with constraints including machine tool performance, tool life, workpiece characteristics, and 3-D stability, to minimize production time and surface roughness. Moreover, a novel MOO and decision-making system for milling parameters is developed to solve the constructed model and assist decision-making, which includes a novel Markov clustering (MCL)-enabled MOO firefly algorithm (MEMOFA), a firefly algorithm-enabled MCL and a pseudo-weight-coefficient-vector-based decision-making method. The empirical findings encompassing six benchmark functions unequivocally attest to the superior performance of the elaborated MEMOFA in matters of convergence, diversity, and spread. Furthermore, an extensive optimization for the milling parameters model is also carried out to verify the superiority of the developed MOO and decision-making system in solving milling parameters optimization.

关键词： Markov clustering firefly algorithm Multi-objective optimization Multi-pass end milling

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Preference-Based Stepping Ahead firefly algorithm for Solving Real-World Uncapacitated Examination Timetabling Problem

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IEEE ACCESS 2024年 12卷 24685-24699页

作者： Nand, Ravneil Reddy, Emmenual Chaudhary, Kaylash Sharma, Bibhya Univ South Pacific Sch Informat Technol Engn Math & Phys Suva Fiji

Swarm-based intelligent optimization algorithms that employ the principles of collective behavior have been gaining traction as viable solutions in optimization research. One area of optimization is the Examination Timetabling Problem (ETP), which presents a significant challenge for many Higher Education Institutes (HEIs). This study proposes a novel approach for solving the Uncapacitated Examination Timetabling Problem (UETP), where a stepping-ahead mechanism is utilized with threshold acceptance in the firefly algorithm (FA). The proposed method improves exploration with the use of the stepping-ahead mechanism, while threshold acceptance allows for better exploitation of the search space. Initially, a neighborhood search mechanism is employed as the discretization of FA to improve solution quality, known as Kempe Chain-based neighborhoods. The proposed method is tested on seven UETP problems, with the results showing comparative performance to the best solutions available in the literature for the Toronto exam timetabling dataset. The selection of seven problems is made with exams totaling less than 400, this allows to create a manageable yet representative benchmark. The study further extends the experiment to a real-world dataset collected from an HEI. The use of a real-world dataset allows us to see the potential of the algorithm and at the same time evaluate its performance under realistic conditions and resource constraints. The proposed stepping-ahead mechanism has the potential for use in other domains, such as robotics and engineering. Overall, this paper presents a new methodology for solving the UETP that has the potential to offer superior results when compared to existing approaches.

关键词： Optimization methods Particle swarm optimization Benchmark testing Task analysis Schedules Education Behavioral sciences Time factors Mathematical models Scheduling Testing Educational institutions Performance analysis firefly algorithm optimization swarm intelligence uncapacitated examination timetabling problem

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

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Computer Modeling in Engineering & Sciences 2025年第3期142卷 2869-2892页

作者： Adil Yousif Department of Computer Science College of Science and Arts-SharourahNajran UniversityNajran Sharourah68378Saudi Arabia

The Internet of Things(IoT)has emerged as an important future ***-Fog is a new computing paradigm that processes IoT data on servers close to the source of the *** 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 *** 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 *** study proposes an Adaptive firefly algorithm(AFA)for dependent task scheduling in IoT-Fog *** 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 *** 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 *** proposed AFA aims to address the dynamic nature of IoT-Fog computing *** proposed AFA mechanism considers a dynamic light absorption coefficient to control the decrease in attractiveness over *** 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 *** 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,*** 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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Multi-Objectives firefly algorithm for Task Offloading in the Edge-Fog-Cloud Computing

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IEEE ACCESS 2024年 12卷 159561-159578页

作者： Saif, Faten A. Latip, Rohaya Hanapi, Zurina Mohd Kamarudin, Shafinah Kumar, A. V. Senthil Bajaher, Awadh Salem Gulf Coll Dept Informat Technol Hafar Al Batin 39952 Saudi Arabia Univ Putra Malaysia UPM Fac Comp Sci & Informat Technol Dept Commun Technol & Network Serdang 43400 Malaysia Hindusthan Coll Arts & Sci Dept MCA Coimbatore 641028 India

This study proposes a two-stage strategy to ensure the successful execution of critical tasks. In the first stage, an Enhanced Task Offloading (ETO) algorithm is introduced to determine the appropriate computing layer-edge, fog, or cloud-for offloading incomplete tasks. The algorithm makes this decision by assessing the availability of idle computing resources relative to the task's computational requirements. Additionally, it verifies the status of the server (on/off) before offloading;if the server is unavailable, the algorithm proceeds to check the next layer. In the second stage, the strategy employs a Multi-objective firefly (MFA) algorithm to assign the optimal computational device within the selected layer. Experimental simulations compare the proposed strategy with a benchmark task offloading algorithm. The results demonstrate the superiority of the proposed strategy, achieving reductions in energy consumption and delay and maximizing resource utilization compared to the baseline algorithms.

关键词： Internet of Things Delays Cloud computing Energy consumption Scheduling Edge computing Optimization Heuristic algorithms Servers Quality of service Task offloading optimization firefly algorithm metaheuristic edge-fog-cloud computing energy consumption transmission delay Pareto

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Discrete Preference-Based Stepping Ahead firefly algorithm for Solving Multidimensional Knapsack Problems

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IEEE ACCESS 2024年 12卷 139154-139164页

作者： Nand, Ravneil Chaudhary, Kaylash Sharma, Bibhya Univ South Pacific Sch Informat Technol Engn Math & Phys Suva Fiji

Complex optimization problems, especially those encountered in real-life scenarios, pose significant challenges due to their multifaceted nature and the involvement of numerous variables. In such contexts, the application of intelligent optimization algorithms emerges as a valuable tool for effectively tackling these intricate problems. firefly algorithm (FA) is a popular meta-heuristic algorithm for continuous domain and lacks application in discrete domain. While there are a few applications but hardly on combinatorial optimization problems. Combinatorial optimization problem, which consist of selecting an optimal object from a finite number of objects is a challenging domain. In this study, a novel discrete version of stepping ahead FA together with its hybridization with another algorithm are proposed to solve the Multidimensional Knapsack Problem (MKP). The proposed algorithms are called discrete stepping ahead firefly algorithm (FA-Step) and hybridization of discrete stepping ahead firefly algorithm with Covariance Matrix Adaptation Evolution Strategy (FA-CMAES). The proposed algorithms make full use of the problem-solving expertise while also incorporating diversity to improve exploitation with stepping ahead mechanism and preference operator. The proposed algorithms are tested on 38 well-known knapsack instances and compared with some novel works from the literature. The proposed methods allow researchers to utilize discretization techniques in other state-of-the-art techniques to solve discrete domain problems with ease.

关键词： Optimization Search problems Covariance matrices Standards Mathematical models Transforms Time complexity firefly algorithm covariance matrix adaptation evolution strategy multidimensional knapsack problem optimization stepping ahead mechanism

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Hybrid firefly algorithm-neural network for battery remaining useful life estimation

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Clean Energy 2024年第5期8卷 157-166页

作者： Zuriani Mustaffa Mohd Herwan Sulaiman Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)26600 Pekan PahangMalaysia Faculty of Electrical&Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)26600 Pekan PahangMalaysia

Accurately estimating the remaining useful life(RUL)of batteries is crucial for optimizing maintenance,preventing failures,and enhancing reliability,thereby saving costs and *** study introduces a hybrid approach for estimating the RUL of a battery based on the firefly algorithm–neural network(FA–NN)model,in which the FA is employed as an optimizer to fine-tune the network weights and hidden layer biases in the *** performance of the FA–NN is comprehensively compared against two hybrid models,namely the harmony search algorithm(HSA)–NN and cultural algorithm(CA)–NN,as well as a single model,namely the autoregressive integrated moving average(ARIMA).The comparative analysis is based mean absolute error(MAE)and root mean squared error(RMSE).Findings reveal that the FA–NN outperforms the HSA–NN,CA–NN,and ARIMA in both employed metrics,demonstrating su-perior predictive capabilities for estimating the RUL of a ***,the FA–NN achieved a MAE of 2.5371 and a RMSE of 2.9488 compared with the HSA–NN with a MAE of 22.0583 and RMSE of 34.5154,the CA–NN with a MAE of 9.1189 and RMSE of 22.4646,and the ARIMA with a MAE of 494.6275 and RMSE of ***,the FA–NN exhibits significantly smaller maximum errors at 34.3737 compared with the HSA–NN at 490.3125,the CA–NN at 827.0163,and the ARIMA at 1.16e+03,further emphasizing its robust performance in minimizing prediction *** study offers important insights into battery health management,showing that the proposed method is a promising solution for precise RUL predictions.

关键词： battery remaining useful life firefly algorithm neural networks optimization

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