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A Firefly Algorithm-Optimized CNN-BiLSTM Model for Automated Detection of Bone Cancer and Marrow Cell Abnormalities 认领 引用
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作者 Ishaani Priyadarshini 《Computers, Materials & Continua》 SCIE EI 2026年第3期1510-1535,共26页
Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a ... Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems. 展开更多
关键词 Firefly optimization algorithm(FO) marrow cell abnormalities bidirectional long short term memory(Bi-LSTM) temporal dependency modeling
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An Adaptive Firefly Algorithm for Dependent Task Scheduling in IoT-Fog Computing 认领 引用
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作者 Adil Yousif 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第3期2869-2892,共24页
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 ... 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. 展开更多
关键词 Fog computing scheduling resource management firefly algorithm genetic algorithm ant colony optimization
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A comparative study of prey-handling behavior of the Chiwen keelback snake(Rhabdophis chiwen)feeding on earthworms and firefly larvae 认领 引用
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作者 Masaya Fukuda Qin Chen +1 位作者 Chengquan Cao Akira Mori 《Current Zoology》 SCIE CAS CSCD 2025年第5期573-580,共8页
Dietary specialists consume specific prey items,and they are often morphologically and behaviorally specialized to feed efficiently on those prey animals.Among specialist snakes,consumption of terrestrial arthropods i... Dietary specialists consume specific prey items,and they are often morphologically and behaviorally specialized to feed efficiently on those prey animals.Among specialist snakes,consumption of terrestrial arthropods is relatively rare.Because most terrestrial arthropods possess hardened sclerites and appendages,it is possible that snakes that feed on arthropods would show specialized prey-handling behavior.In this study,we describe prey-handling behavior of a snake feeding on terrestrial arthropods,which hitherto has not been well documented.We focused on Rhabdophis chiwen,which mainly feeds on earthworms,but also consumes lampyrine firefly larvae,sequestering cardiotonic steroids from them in its defensive organs,called nucho-dorsal glands.When feeding on earthworms,snakes showed size-dependent selection of swallowing direction,but this tendency was not observed when feeding on firefly larvae.Manipulation of firefly larvae did not seem to be efficient,probably because they possess sclerites and appendages such as legs that impede smooth handling.Although fireflies are an essential food for R.chiwen as a toxin source,our results showed that the snake is not adept at handling firefly larvae compared to earthworms,implying that dietary specialization does not necessarily accompany behavioral specialization.We discuss possible reasons for this inconsistency. 展开更多
关键词 behavior earthworm firefly prey-handling Rhabdophis
基于Firefly算法的电力系统线路保护整定计算研究 认领 引用 被引量：2
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作者 翟颖超 《电工技术》 2025年第8期131-133,137,共3页
电力系统面临的故障类型和程度复杂多变,线路保护整定计算的计算量大、迭代次数多,导致计算效率低下,影响故障处理的实时性,因此提出基于Firefly算法的电力系统线路保护整定计算研究。将分析得到的不同电力系统线路保护整定类型,通过电... 电力系统面临的故障类型和程度复杂多变,线路保护整定计算的计算量大、迭代次数多,导致计算效率低下,影响故障处理的实时性,因此提出基于Firefly算法的电力系统线路保护整定计算研究。将分析得到的不同电力系统线路保护整定类型,通过电力系统分区模型进行电力系统线路过电流保护阈值的计算;同时为了确保电力系统的协同性,需要将得到的多区域线路保护整定值进行一体化整合,并利用Firefly算法对其进行优化收敛,得出整定结果。实验结果表明,该方法在复杂条件下得到精确的整定值的运行时间仅需1 s,证明基于Firefly算法的电力系统线路保护整定计算方法能够更好地满足电力系统对实时性的要求,保障电力系统的稳定运行。 展开更多
关键词 Firefly算法 电力系统 线路保护 整定计算 过电流保护
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A Global Best-guided Firefly Algorithm for Engineering Problems 认领 引用 被引量：9
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作者 Mohsen Zare Mojtaba Ghasemi +4 位作者 Amir Zahedi Keyvan Golalipour Soleiman Kadkhoda Mohammadi Seyedali Mirjalili Laith Abualigah 《Journal of Bionic Engineering》 SCIE EI CSCD 2023年第5期2359-2388,共30页
The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when mating.This article proposes a method based on Differential Evoluti... The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when mating.This article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA's movement process.The proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best solution.However,employing the best solution can lead to premature algorithm convergence,but this study handles this issue using a loop adjacent to the algorithm's main loop.Additionally,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original FA.The GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha values.Additionally,the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced algorithms.In all cases,GbFA provides the optimal result compared to other methods.Note that the source code of the GbFA algorithm is publicly available at http://gffzz33d83747d65b4abdscpnvp6op6c6w65pn.ffgz.tsg.suse.edu.cn/projects/gbfa. 展开更多
关键词 Firefly algorithm New movement vector Global best-guided firefly algorithm Global optimization Engineering design
Path planning in uncertain environment by using firefly algorithm 认领 引用 被引量：19
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作者 B.K.Patle Anish Pandey +1 位作者 A.Jagadeesh D.R.Parhi 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2018年第6期691-701,共11页
Autonomous mobile robot navigation is one of the most emerging areas of research by using swarm intelligence. Path planning and obstacle avoidance are most researched current topics like navigational challenges for mo... Autonomous mobile robot navigation is one of the most emerging areas of research by using swarm intelligence. Path planning and obstacle avoidance are most researched current topics like navigational challenges for mobile robot. The paper presents application and implementation of Firefly Algorithm(FA)for Mobile Robot Navigation(MRN) in uncertain environment. The uncertainty is defined over the changing environmental condition from static to dynamic. The attraction of one firefly towards the other firefly due to variation of their brightness is the key concept of the proposed study. The proposed controller efficiently explores the environment and improves the global search in less number of iterations and hence it can be easily implemented for real time obstacle avoidance especially for dynamic environment. It solves the challenges of navigation, minimizes the computational calculations, and avoids random moving of fireflies. The performance of proposed controller is better in terms of path optimality when compared to other intelligent navigational approaches. 展开更多
关键词 Mobile robot navigation Firefly algorithm Path planning Obstacle avoidance
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Rayleigh wave nonlinear inversion based on the Firefly algorithm 认领 引用 被引量：6
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作者 周腾飞 彭更新 +3 位作者 胡天跃 段文胜 姚逢昌 刘依谋 《Applied Geophysics》 SCIE CSCD 2014年第2期167-178,253,共12页
Rayleigh waves have high amplitude, low frequency, and low velocity, which are treated as strong noise to be attenuated in reflected seismic surveys. This study addresses how to identify useful shear wave velocity pro... Rayleigh waves have high amplitude, low frequency, and low velocity, which are treated as strong noise to be attenuated in reflected seismic surveys. This study addresses how to identify useful shear wave velocity profile and stratigraphic information from Rayleigh waves. We choose the Firefly algorithm for inversion of surface waves. The Firefly algorithm, a new type of particle swarm optimization, has the advantages of being robust, highly effective, and allows global searching. This algorithm is feasible and has advantages for use in Rayleigh wave inversion with both synthetic models and field data. The results show that the Firefly algorithm, which is a robust and practical method, can achieve nonlinear inversion of surface waves with high resolution. 展开更多
关键词 Rayleigh wave near-surface Firefly algorithm shear velocity
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Repulsive firefly algorithm-based optimal switching device placement in power distribution systems 认领 引用 被引量：3
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作者 Yuanpeng Tan Hai Chen +4 位作者 Wei Liu Mingze Zhang Yinong Li Xincong Li Hanyang Lin 《Global Energy Interconnection》 2019年第6期490-496,共7页
To achieve optimal configuration of switching devices in a power distribution system,this paper proposes a repulsive firefly algorithm-based optimal switching device placement method.In this method,the influence of te... To achieve optimal configuration of switching devices in a power distribution system,this paper proposes a repulsive firefly algorithm-based optimal switching device placement method.In this method,the influence of territorial repulsion during firefly courtship is considered.The algorithm is practically applied to optimize the position and quantity of switching devices,while avoiding its convergence to the local optimal solution.The experimental simulation results have showed that the proposed repulsive firefly algorithm is feasible and effective,with satisfying global search capability and convergence speed,holding potential applications in setting value calculation of relay protection and distribution network automation control. 展开更多
关键词 Power distribution systems Switching device Repulsive firefly algorithm Optimal placement Reliability
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Defect image segmentation using multilevel thresholding based on firefly algorithm with opposition-learning 认领 引用 被引量：3
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作者 陈恺 戴敏 +2 位作者 张志胜 陈平 史金飞 《Journal of Southeast University(English Edition)》 EI CAS 2014年第4期434-438,共5页
To segment defects from the quad flat non-lead QFN package surface a multilevel Otsu thresholding method based on the firefly algorithm with opposition-learning is proposed. First the Otsu thresholding algorithm is ex... To segment defects from the quad flat non-lead QFN package surface a multilevel Otsu thresholding method based on the firefly algorithm with opposition-learning is proposed. First the Otsu thresholding algorithm is expanded to a multilevel Otsu thresholding algorithm. Secondly a firefly algorithm with opposition-learning OFA is proposed.In the OFA opposite fireflies are generated to increase the diversity of the fireflies and improve the global search ability. Thirdly the OFA is applied to searching multilevel thresholds for image segmentation. Finally the proposed method is implemented to segment the QFN images with defects and the results are compared with three methods i.e. the exhaustive search method the multilevel Otsu thresholding method based on particle swarm optimization and the multilevel Otsu thresholding method based on the firefly algorithm. Experimental results show that the proposed method can segment QFN surface defects images more efficiently and at a greater speed than that of the other three methods. 展开更多
关键词 quad flat non-lead QFN surface defects opposition-learning firefly algorithm multilevel Otsu thresholding algorithm
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Shape and Size Optimization of Truss Structures under Frequency Constraints Based on Hybrid Sine Cosine Firefly Algorithm 认领 引用 被引量：2
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作者 Ran Tao Xiaomeng Yang +1 位作者 Huanlin Zhou Zeng Meng 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第1期405-428,共24页
Shape and size optimization with frequency constraints is a highly nonlinear problem withmixed design variables,non-convex search space,and multiple local optima.Therefore,a hybrid sine cosine firefly algorithm(HSCFA)... Shape and size optimization with frequency constraints is a highly nonlinear problem withmixed design variables,non-convex search space,and multiple local optima.Therefore,a hybrid sine cosine firefly algorithm(HSCFA)is proposed to acquire more accurate solutions with less finite element analysis.The full attraction model of firefly algorithm(FA)is analyzed,and the factors that affect its computational efficiency and accuracy are revealed.A modified FA with simplified attraction model and adaptive parameter of sine cosine algorithm(SCA)is proposed to reduce the computational complexity and enhance the convergence rate.Then,the population is classified,and different populations are updated by modified FA and SCA respectively.Besides,the random search strategy based on Lévy flight is adopted to update the stagnant or infeasible solutions to enhance the population diversity.Elitist selection technique is applied to save the promising solutions and further improve the convergence rate.Moreover,the adaptive penalty function is employed to deal with the constraints.Finally,the performance of HSCFA is demonstrated through the numerical examples with nonstructural masses and frequency constraints.The results show that HSCFA is an efficient and competitive tool for shape and size optimization problems with frequency constraints. 展开更多
关键词 Firefly algorithm sine cosine algorithm frequency constraints structural optimization
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A Novel Binary Firefly Algorithm for the Minimum Labeling Spanning Tree Problem 认领 引用 被引量：2
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作者 Mugang Lin Fangju Liu +1 位作者 Huihuang Zhao Jianzhen Chen 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第10期197-214,共18页
Given a connected undirected graph G whose edges are labeled,the minimumlabeling spanning tree(MLST)problemis to find a spanning tree of G with the smallest number of different labels.TheMLST is anNP-hard combinatoria... Given a connected undirected graph G whose edges are labeled,the minimumlabeling spanning tree(MLST)problemis to find a spanning tree of G with the smallest number of different labels.TheMLST is anNP-hard combinatorial optimization problem,which is widely applied in communication networks,multimodal transportation networks,and data compression.Some approximation algorithms and heuristics algorithms have been proposed for the problem.Firefly algorithm is a new meta-heuristic algorithm.Because of its simplicity and easy implementation,it has been successfully applied in various fields.However,the basic firefly algorithm is not suitable for discrete problems.To this end,a novel discrete firefly algorithm for the MLST problem is proposed in this paper.A binary operation method to update firefly positions and a local feasible handling method are introduced,which correct unfeasible solutions,eliminate redundant labels,and make the algorithm more suitable for discrete problems.Computational results show that the algorithm has good performance.The algorithm can be extended to solve other discrete optimization problems. 展开更多
关键词 Minimum labeling spanning tree problem binary firefly algorithm meta-heuristics discrete optimization
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Enhancing Firefly Algorithm with Best Neighbor Guided Search Strategy 认领 引用 被引量：2
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作者 WU Shuangke WU Zhijian PENG Hu 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2019年第6期524-536,共13页
Firefly algorithm(FA)is a recently-proposed swarm intelligence technique.It has shown good performance in solving various optimization problems.According to the standard firefly algorithm and most of its variants,a fi... Firefly algorithm(FA)is a recently-proposed swarm intelligence technique.It has shown good performance in solving various optimization problems.According to the standard firefly algorithm and most of its variants,a firefly migrates to every other brighter firefly in each iteration.However,this method leads to defects of oscillations of positions,which hampers the convergence to the optimum.To address these problems and enhance the performance of FA,we propose a new firefly algorithm,which is called the Best Neighbor Firefly Algorithm(BNFA).It employs the best neighbor guided strategy,where each firefly is attracted to the best firefly among some randomly chosen neighbors,thus reducing the firefly oscillations in every attraction-induced migration stage,while increasing the probability of the guidance a new better direction.Moreover,it selects neighbors randomly to prevent the firefly form being trapped into a local optimum.Extensive experiments are conducted to find out the optimal parameter settings.To verify the performance of BNFA,13 classical benchmark functions are tested.Results show that BNFA outperforms the standard FA and other recently proposed modified FAs. 展开更多
关键词 firefly algorithm(FA) global optimization random neighbour exploration and exploitation
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Phylogenetic Relationship of the Firefly,Diaphanes pectinealis(Insecta,Coleoptera,Lampyridae) Based on DNA Sequence and Gene Structure of Luciferase 认领 引用 被引量：3
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作者 李学燕 杨爽 梁醒财 《Zoological Research》 北大核心 2006年第4期367-374,共8页
Diaphanes is the fourth largest genus in Lampyridae, but no luciferase gene from this genus has been reported. In this paper, by PCR amplification of the genomic DNA, the luciferase gene of Diaphanes pectinealis, whic... Diaphanes is the fourth largest genus in Lampyridae, but no luciferase gene from this genus has been reported. In this paper, by PCR amplification of the genomic DNA, the luciferase gene of Diaphanes pectinealis, which is the first case from Diaphanes, was identified and sequenced. The luciferase gene from D. pectinealis spans 1958 base pairs （bp） from the start to the stop codon, including seven exons separated by six introns, and encoding a 547-residuelong polypeptide. Its deduced amino acid sequence showed high protein similarity to those of the Lampyrini tribe （93 - 94% ） and the Cratomorphini tribe （92%）, while low similarity was found with the North American firefly Photinus pyralis （83%） of the Photinini tribe within the same subfamily Lampyrinae. The phylogenetic analysis performed with the deduced amino acid sequences of the luciferase gene further confirms that D. pectinealis, Pyrocoelia, Lampyris, Cratomorphus, and Photinus belong to the same subfamily Lampyrinae, and Diaphanes is closely related to Pyrocoelia, Lampyris, and Cratomorphus. Furthemore, the phylogenetic analysis based on the nucleotide sequences of the luciferase gene indicates Diaphanes is a sister to Lampyris. The phylogenetic analyses are partly consistent with morphological （Branham ＆ Wenzel, 2003） and mitochondrial DNA analyses （Li et al, 2006）. 展开更多
关键词 Firefly Diaphanes pectinealis Luciferase gene Gene structure Phylogeny
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A Hybrid Firefly Algorithm for Optimizing Fractional Proportional-Integral-Derivative Controller in Ship Steering 认领 引用 被引量：1
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作者 薛晗 邵哲平 +2 位作者 潘家财 赵强 马峰 《Journal of Shanghai Jiaotong university(Science)》 EI 2016年第4期419-423,共5页
In this paper, a new algorithm which integrates the powerful firefly Mgorithm （FA） and the ant colony optimization （ACO） has been used in tracking control of ship steering for optimization of fractional-order prop... In this paper, a new algorithm which integrates the powerful firefly Mgorithm （FA） and the ant colony optimization （ACO） has been used in tracking control of ship steering for optimization of fractional-order proportional-integral-derivative （FOPID） controller gains. Particle swarm optimization （PSO） algorithm is also used to optimize FOPID controllers, and their performances are compared. It is found that FA optimized FOPID controller gives better performance than others. Sensitivity analysis has been carried out to see the robustness of optimum FOPID gains obtained at nominal conditions to wide changes in system parameters, and the optimum FOPID gains need not be reset for wide changes in system parameters. 展开更多
关键词 firefly algorithm （FA） fractional-order proportional-integral-derivative （FOPID） ant colony optimization （ACO） tracking control ship steering
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Cloning,Expression and Sequence Analysis of A Luciferase Gene from the Chinese Firefly Pyrocoelia pygidialis 认领 引用 被引量：1
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作者 董平轩 侯清柏 +1 位作者 李学燕 梁醒财 《Zoological Research》 北大核心 2008年第5期477-484,共8页
The cDNA encoding the luciferase from lantern mRNA of one diurnal firefly Pyrocoelia pygidialis Pic, 1926 has been cloned, sequenced and functionally expressed. The cDNA sequence of P pygidialis luciferase is 1647 bas... The cDNA encoding the luciferase from lantern mRNA of one diurnal firefly Pyrocoelia pygidialis Pic, 1926 has been cloned, sequenced and functionally expressed. The cDNA sequence of P pygidialis luciferase is 1647 base pairs in length, coding a protein of 548 amino acid residues. Sequence analysis of the deduced amino acid sequence showed that this luciferase had 97.8% resemblance to luciferases from the fireflies Lampyris noctiluca, Lampyris turkestanicus and Nyctophila cf. caucasica. Phylogenetic analysis using deduced amino acid sequence showed that P pygidialis located at the base of Lampyris＋Nyctophila clade with robust support （BP=97%）; but did not show a monophyletic relationship with its congeneric species P pectoralis, P tufa and P miyako, all three are strong luminous and nocturnal species. The expression worked in recombinant Escherichia coli. Expression product had a 70kDa band and emitted yellow-green luminescence in the presence of luciferin. Five loops in the P pygidialis luciferase, L1 （NI98-G208）, L2 （T240-G247）, L3 （G317-K322）, L4 （L343-I350） and L5 （G522-D532）, were found from the structure modeling analysis in the cleft, where it was considered the active site for the substrate compound entering and binding. Different amino acid residues between the luciferases of P. pygidialis and the three other known strong luminous species can not explain the situation of weak or strong luminescence. Future study of these loops, residues or crystal structure analysis may be helpful in understanding the real differences between the luciferases between diurnal and nocturnal species. 展开更多
关键词 Pyrocoelia Diurnal firefly Pyrocoelia pygidialis Luciferase Homology modeling
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Study of the Binding Mode of Quaternary Ammonium Cationic Surfactant to Firefly Luciferase and the Prediction of Binary Mixture Toxicity 认领 引用
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作者 MO Ling-Yun MA Wen +4 位作者 KONG Song QIN Li-Tang LIANG Yan-Peng DAI Jun-Feng LIU Shu-Shen 《Chinese Journal of Structural Chemistry》 SCIE CAS CSCD 2020年第6期1167-1177,共11页
The wide use of quaternary ammonium cationic surfactants(QACs)results in their release into the environment.Most surfactants have significant biotoxicity.However,existing toxicity data on QACs are still lacking,especi... The wide use of quaternary ammonium cationic surfactants(QACs)results in their release into the environment.Most surfactants have significant biotoxicity.However,existing toxicity data on QACs are still lacking,especially regarding the joint toxic effects of their mixtures.In computer simulation technology,molecular docking technology is commonly used for studying the mode of action of receptors docking with ligands.The research of QACs mixture interaction is relatively rare,and the binding mode of QACs is unknown.In this study,molecular docking technology was applied to explore the QAC binding mode,and the concentration addition(CA)and independent action(IA)models were applied for predicting the mixture toxicity.Firefly luciferase(FLuc)was used as a macromolecular receptor,and five typical QACs:benzalkonium bromide(BLB),tetraethylammonium bromide(TLB),N,N,N-trimethyl-1-tetradecyl ammonium bromide(CTE),tetrabutylammonium chloride(TAC),and dodecyltrimethylammonium chloride(DTC)were used as small molecule ligands.Molecular docking technology was used to investigate the binding mode of macromolecules and small molecules.The luminescence inhibitory effects of individual compounds and binary mixture on FLuc were determined by microplate toxicity assay of luciferase.The prediction of mixture toxicity was performed by CA and IA.The results showed that the relative toxicity follows:TLB<TAC<DTC<BLB<CTE.TLB and TAC showed the BS-Ⅱbinding mode,and BLB,CTE and DTC showed the BS-Ⅲbinding mode.The toxicity of compounds with binding mode BS-Ⅱwas less than that of those with BS-Ⅲbinding mode.Not all mixtures with the same binding mode could be predicted by CA model,and the IA model did not effectively predict the toxicity of mixtures with compound with different binding modes.The mixture toxicities of QACs with the same binding mode mostly presented additive and synergistic effects,while the mixture toxic effects of QACs with different binding modes presented additive or antagonistic effects. 展开更多
关键词 molecular docking firefly luciferase binding mode CA IA
Optimization of Cognitive Radio System Using Enhanced Firefly Algorithm 认领 引用
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作者 Nitin Mittal Rohit Salgotra +3 位作者 Abhishek Sharma Sandeep Kaur SSAskar Mohamed Abouhawwash 《Intelligent Automation & Soft Computing》 SCIE 2023年第9期3159-3177,共19页
The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this work.The Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of fi... The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this work.The Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of fireflies.It has already proved its competence in various optimization prob-lems,but it suffers from slow convergence issues.To improve the convergence performance of FA,a new variant named EFA is proposed.The effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions,and simulation results show its superior performance compared to biogeography-based optimization(BBO),bat algorithm,artificial bee colony,and FA.As an application of this algorithm to real-world problems,EFA is also applied to optimize the CR system.CR is a revolutionary technique that uses a dynamic spectrum allocation strategy to solve the spectrum scarcity problem.However,it requires optimization to meet specific performance objectives.The results obtained by EFA in CR system optimization are compared with results in the literature of BBO,simulated annealing,and genetic algorithm.Statistical results further prove that the proposed algorithm is highly efficient and provides superior results. 展开更多
关键词 Firefly algorithm cognitive radio bit error rate genetic algorithm simulated annealing biogeography-based optimization
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Fuzzy Firefly Based Intelligent Algorithm for Load Balancing inMobile Cloud Computing 认领 引用
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作者 Poonam Suman Sangwan 《Computers, Materials & Continua》 SCIE EI 2023年第1期1783-1799,共17页
This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits ... This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly.It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function.It works for 3-tier architecture,including cloudlet and public cloud.As cloudlets have limited resources,fuzzy logic is used for cloudlet selection using capacity and waiting time as input.Fuzzy provides human-like decisions without using any mathematical model.Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed.It balances the load on cloud and cloudlet while minimizing makespan and execution time.However,it may trap in local optimum;levy flight can handle it.Hybridization of fuzzy fireflywith levy flight is a novel technique that provides reduced makespan,execution time,and Degree of imbalance while balancing the load.Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration(NASA)and Clarknet datasets.Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker(ACOQDM),Distributed Scheduling Optimization Algorithm(DSOA),andUtility-based Firefly Algorithm(UFA)when compared in terms of makespan,Degree of imbalance,and Figure of Merit. 展开更多
关键词 Cloud computing cloudlet mobile cloud computing fuzzy firefly load balancing makespan degree of imbalance
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FireFly无线仪器性能分析与应用探讨 认领 引用 被引量：3
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作者 甘志强 《物探装备》 2012年第1期37-40,共4页
本文介绍了FireFly仪器的基本组成、工作原理和功能特性,特别针对现场采集质量控制进行了深入地分析,并针对如何使用好具有新型设计理念的FireFly仪器提出了一些建议,以期共同探讨。
关键词 FireFly仪器 中央控制系统(CSC) 基础导航系统(BNS) 数据转录系统(T3) 质量控制
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Design of a Proportional-Integral-Derivative Controller for an Automatic Generation Control of Multi-area Power Thermal Systems Using Firefly Algorithm 认领 引用 被引量：10
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作者 K.Jagatheesan B.Anand +3 位作者 Sourav Samanta Nilanjan Dey Amira S.Ashour Valentina E.Balas 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2019年第2期503-515,共13页
Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system ... Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control,automatic generation control(AGC) plays a crucial role. In this paper, multi-area(Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative(PID) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm(FFA). The experimental results demonstrated the comparison of the proposed system performance(FFA-PID)with optimized PID controller based genetic algorithm(GAPID) and particle swarm optimization(PSO) technique(PSOPID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error(ITAE) cost function with one percent step load perturbation(1 % SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller. 展开更多
关键词 Automatic generation control(AGC) firefly algorithm genetic algorithm(GA) particle swarm optimization(PSO) proportional-integral-derivative(PID) controller
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