Eukaryotic genome size data are important both as the basis for comparative research into genome evolution and as estimators of the cost and difficulty of genome sequencing programs for non-model organisms. In this st...Eukaryotic genome size data are important both as the basis for comparative research into genome evolution and as estimators of the cost and difficulty of genome sequencing programs for non-model organisms. In this study, the genome size of 14 species of fireflies （Lampyridae） （two genera in Lampyrinae, three genera in Luciolinae, and one genus in subfamily incertae sedis） were estimated by propidium iodide （PI）-based flow cytometry. The haploid genome sizes of Lampyridae ranged from 0.42 to 1.31 pg, a 3.1-fold span. Genome sizes of the fireflies varied within the tested subfamilies and genera. Lamprigera and Pyrocoelia species had large and small genome sizes, respectively. No correlation was found between genome size and morphological traits such as body length, body width, eye width, and antennal length. Our data provide additional information on genome size estimation of the firefly family Lampyridae. Furthermore, this study will help clarify the cost and difficulty of genome sequencing programs for non-model organisms and will help promote studies on firefly genome evolution.展开更多
In this paper, a system of fractional differential equations that model the synchronized bioluminescence behavior of a set of fireflies put on two spatial arrangements is presented; the alternative representation of t...In this paper, a system of fractional differential equations that model the synchronized bioluminescence behavior of a set of fireflies put on two spatial arrangements is presented; the alternative representation of these equations contains fractional operators of IAouvillc-Caputo type. The objective of the model is to qualitatively recover synchronization and show that it is persistent. It is shown that the effort made by each firefly glow changes with respect to the number of male competitors and the distance between them. The conditions on biological parameters are interpreted.展开更多
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
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
Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing attention.However,traditional optimization methods often overlook the energy imbalance caus...Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing attention.However,traditional optimization methods often overlook the energy imbalance caused by node loads,which affects network performance.Methods To improve the overall performance and efficiency of wireless sensor networks,a new method for optimizing the wireless sensor network topology based on K-means clustering and firefly algorithms is proposed.The K-means clustering algorithm partitions nodes by minimizing the within-cluster variance,while the firefly algorithm is an optimization algorithm based on swarm intelligence that simulates the flashing interaction between fireflies to guide the search process.The proposed method first introduces the K-means clustering algorithm to cluster nodes and then introduces a firefly algorithm to dynamically adjust the nodes.Results The results showed that the average clustering accuracies in the Wine and Iris data sets were 86.59%and 94.55%,respectively,demonstrating good clustering performance.When calculating the node mortality rate and network load balancing standard deviation,the proposed algorithm showed dead nodes at approximately 50 iterations,with an average load balancing standard deviation of 1.7×10^(4),proving its contribution to extending the network lifespan.Conclusions This demonstrates the superiority of the proposed algorithm in significantly improving the energy efficiency and load balancing of wireless sensor networks to extend the network lifespan.The research results indicate that wireless sensor networks have theoretical and practical significance in fields such as monitoring,healthcare,and agriculture.展开更多
Recovery is a crucial supporting process for carrier aircraft,where a reasonable landing scheduling is expected to guide the fleet landing safely and quickly.Currently,there is little research on this topic,and most o...Recovery is a crucial supporting process for carrier aircraft,where a reasonable landing scheduling is expected to guide the fleet landing safely and quickly.Currently,there is little research on this topic,and most of it neglects potential influence factors,leaving the corresponding supporting efficiency questionable.In this paper,we study the landing scheduling problem for carrier aircraft considering the effects of bolting and aerial refueling.Based on the analysis of recovery mode involving the above factors,two types of primary constraints(i.e.,fuel constraint and wake interval constraint)are first described.Then,taking the landing sequencing as decision variables,a combinatorial optimization model with a compound objective function is formulated.Aiming at an efficient solution,an improved firefly algorithm is designed by integrating multiple evolutionary operators.In addition,a dynamic replanning mechanism is introduced to deal with special situations(i.e.,the occurrence of bolting and fuel shortage),where the high efficiency of the designed algorithm facilitates the online scheduling adjustment within seconds.Finally,numerical simulations with sufficient and insufficient fuel cases are both carried out,highlighting the necessity to consider bolting and aerial refueling during the planning procedure.Simulation results reveal that a higher bolting probability,as well as extra aerial refueling operations caused by fuel shortage,will lead to longer recovery complete time.Meanwhile,due to the strong optimum-seeking capability and solution efficiency of the improved algorithm,adaptive scheduling can be generated within milliseconds to deal with special situations,significantly improving the safety and efficiency of the recovery process.An animation is accessible at bilibili.com/video/BV1QprKY2EwD.展开更多
Conventional optimal sensor placement(OSP)methods employ the premise that all sensors work perfectly during long-term structural monitoring.However,this premise is often difficult to fulfill in real applications due t...Conventional optimal sensor placement(OSP)methods employ the premise that all sensors work perfectly during long-term structural monitoring.However,this premise is often difficult to fulfill in real applications due to poor manufacturing and material aging of sensors,human damage,and electromagnetic interference.This paper presents a robustness-oriented OSP method that considers sensor failures.The OSP problem is designed with consideration of sensor failures to ensure that both complete vibration data collected by all sensors and incomplete vibration data caused by individual sensor failures can accurately identify structural modal parameters.A dispersion-aggregation firefly algorithm(DAFA),which is derived from the basic firefly algorithm,has been proposed to solve this complicated optimization problem.The dispersion and aggregation operators are designed to prevent falling into local optima and to rapidly converge to the global optima.The proposed methodology is confirmed by extracting the robust sensor configuration for a long-span cable-stayed bridge.The robustness of the optimal sensor configurations against sensor failure is thoroughly explored,and the performance of the proposed DAFA is extensively examined.展开更多
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
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
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
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
Artificial night lighting is gaining attention as a new type of pollution;however, studies of its impacts are scarce. Fireflies provide good models to investigate its effects on nocturnal wildlife, since they depend o...Artificial night lighting is gaining attention as a new type of pollution;however, studies of its impacts are scarce. Fireflies provide good models to investigate its effects on nocturnal wildlife, since they depend on their bioluminescence for reproduction. This study investigated the impact of artificial illumination on firefly activity at the new campus of the Federal University of São Carlos (Sorocaba, Brazil). The flashing activity of different firefly species, especially Photinus sp1 (82% of all occurrences), was investigated during 3 years, before and after the installation of multi metal vapor spotlights. Quantitative and qualitative analysis, performed in transects at different distances from the artificial light sources, showed significant negative effects on Photinus sp1 occurrence. This study proposes fireflies as potential flagship species and bioindicators for artificial night lighting and for the first time quantifies its effects, providing subsidies for future conservationist legislations regarding photopollution.展开更多
Several Asian natricine snakes of the genus Rhabdophis feed on toads and sequester steroidal cardiac toxins known as bufadienolides(BDs)from them.A recent study revealed that species of the Rhabdophis nuchalis Group i...Several Asian natricine snakes of the genus Rhabdophis feed on toads and sequester steroidal cardiac toxins known as bufadienolides(BDs)from them.A recent study revealed that species of the Rhabdophis nuchalis Group ingest lampyrine fireflies to sequester BDs.Although several species of fireflies are distributed in the habitat of the R.nuchalis Group,only lampyrine fireflies,which have BDs,are included in the diet of these snakes.Thus,we hypothesized that the R.nuchalis Group chemically distinguishes fireflies that have BDs from those that do not have BDs.We also predicted that the R.nuchalis Group detects BDs as the chemical cue of toxin source.To test these predictions,we conducted 3 behavioral experiments using Rhabdophis chiwen,which belongs to the R.nuchalis Group.In the first experiment,R.chiwen showed a moderate tongue flicking response to cinobufagin,a compound of BDs.On the other hand,the snake showed a higher response to the chemical stimuli of lampyrine fireflies(BD fireflies)than those of lucioline fireflies(non-BD fireflies).In the second experiment,in which we provided live BD and non-BD fireflies,the snake voluntarily consumed only the former.In the third,a Y-maze experiment,the snake tended to select the chemical trail of BD fireflies more frequently than that of non-BD fireflies.These results demonstrated that R.chiwen discriminates BD fireflies from non-BD fireflies,but the prediction that BDs are involved in this discrimination was not fully supported.To identify the proximate mechanisms of the recognition of novel toxic prey in the R.nuchalis Group,further investigation is necessary.展开更多
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
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 https://www.optim-app.com/projects/gbfa.展开更多
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
Wireless Sensor Networks(WSNs)comprises low power devices that are randomly distributed in a geographically isolated region.The energy consumption of nodes is an essential factor to be considered.Therefore,an improved...Wireless Sensor Networks(WSNs)comprises low power devices that are randomly distributed in a geographically isolated region.The energy consumption of nodes is an essential factor to be considered.Therefore,an improved energy management technique is designed in this investigation to reduce its consumption and to enhance the network’s lifetime.This can be attained by balancing energy clusters using a meta-heuristic Firefly algorithm model for network communication.This improved technique is based on the cluster head selection technique with measurement of the tour length of fireflies.Time Division Multiple Access(TDMA)scheduler is also improved with the characteristics/behavior of fireflies and also executed.At last,the development approach shows the progression of the network lifetime,the total number of selected Cluster Heads(CH),the energy consumed by nodes,and the number of packets transmitted.This approach is compared with Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR)and Low Energy Adaptive Clustering Hierarchy(LEAH)protocols.Simulation is performed in MATLAB with the numerical outcomes showing the efficiency of the proposed approach.The energy consumption of sensor nodes is reduced by about 50%and increases the lifetime of nodes by 78%more than AODV,DSR and LEACH protocols.The parameters such as cluster formation,end to end delay,percentage of nodes alive and packet delivery ratio,are also evaluated...The anticipated method shows better trade-off in contrast to existing techniques.展开更多
This paper introduces a new approach of firefly algorithm based on opposition-based learning （OBFA） to enhance the global search ability of the original algorithm. The new algorithm employs opposition based learning...This paper introduces a new approach of firefly algorithm based on opposition-based learning （OBFA） to enhance the global search ability of the original algorithm. The new algorithm employs opposition based learning concept to generate initial population and also updating agents’ positions. The proposed OBFA is applied for minimization of the factor of safety and search for critical failure surface in slope stability analysis. The numerical experiments demonstrate the effectiveness and robustness of the new algorithm.展开更多
基金supported by grants from the National Natural Science Foundation of China(No.31472035)Yunnan Provincial Science and Technology Department(No.2014FB179)to LXY
文摘Eukaryotic genome size data are important both as the basis for comparative research into genome evolution and as estimators of the cost and difficulty of genome sequencing programs for non-model organisms. In this study, the genome size of 14 species of fireflies （Lampyridae） （two genera in Lampyrinae, three genera in Luciolinae, and one genus in subfamily incertae sedis） were estimated by propidium iodide （PI）-based flow cytometry. The haploid genome sizes of Lampyridae ranged from 0.42 to 1.31 pg, a 3.1-fold span. Genome sizes of the fireflies varied within the tested subfamilies and genera. Lamprigera and Pyrocoelia species had large and small genome sizes, respectively. No correlation was found between genome size and morphological traits such as body length, body width, eye width, and antennal length. Our data provide additional information on genome size estimation of the firefly family Lampyridae. Furthermore, this study will help clarify the cost and difficulty of genome sequencing programs for non-model organisms and will help promote studies on firefly genome evolution.
文摘In this paper, a system of fractional differential equations that model the synchronized bioluminescence behavior of a set of fireflies put on two spatial arrangements is presented; the alternative representation of these equations contains fractional operators of IAouvillc-Caputo type. The objective of the model is to qualitatively recover synchronization and show that it is persistent. It is shown that the effort made by each firefly glow changes with respect to the number of male competitors and the distance between them. The conditions on biological parameters are interpreted.
基金supported in part by JST SPRING(JPMJSP2110)by Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research(18KK0205,21H02551)Shosuke Takeuchi for personal funding.
文摘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.
基金the Deanship of Graduate Studies and Scientific Research at Najran University for funding this work under the Easy Funding Program grant code(NU/EFP/SERC/13/166).
文摘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.
基金Supported by 2021 Zhanjiang University of Science and Technology"Brand Enhancement Plan"Project:Network Series Course Teaching Team(PPJH202102JXTD)2022 Zhanjiang University of Science and Technology"Brand Enhancement Plan"Project:Network Engineering(PPJHKCSZ-2022301)+1 种基金2023 Zhanjiang Science and Technology Bureau Project:Design and Simulation of Zhanjiang Mangrove Wetland Monitoring Network System(2023B01017)2022 Zhanjiang University of Science and Technology Quality Engineering Project:Audiovisual Language Teaching and Research Office(ZLGC202203).
文摘Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing attention.However,traditional optimization methods often overlook the energy imbalance caused by node loads,which affects network performance.Methods To improve the overall performance and efficiency of wireless sensor networks,a new method for optimizing the wireless sensor network topology based on K-means clustering and firefly algorithms is proposed.The K-means clustering algorithm partitions nodes by minimizing the within-cluster variance,while the firefly algorithm is an optimization algorithm based on swarm intelligence that simulates the flashing interaction between fireflies to guide the search process.The proposed method first introduces the K-means clustering algorithm to cluster nodes and then introduces a firefly algorithm to dynamically adjust the nodes.Results The results showed that the average clustering accuracies in the Wine and Iris data sets were 86.59%and 94.55%,respectively,demonstrating good clustering performance.When calculating the node mortality rate and network load balancing standard deviation,the proposed algorithm showed dead nodes at approximately 50 iterations,with an average load balancing standard deviation of 1.7×10^(4),proving its contribution to extending the network lifespan.Conclusions This demonstrates the superiority of the proposed algorithm in significantly improving the energy efficiency and load balancing of wireless sensor networks to extend the network lifespan.The research results indicate that wireless sensor networks have theoretical and practical significance in fields such as monitoring,healthcare,and agriculture.
基金the financial support of the National Natural Science Foundation of China(12102077,12161076)the Natural Science and Technology Program of Liaoning Province(2023-BS-061).
文摘Recovery is a crucial supporting process for carrier aircraft,where a reasonable landing scheduling is expected to guide the fleet landing safely and quickly.Currently,there is little research on this topic,and most of it neglects potential influence factors,leaving the corresponding supporting efficiency questionable.In this paper,we study the landing scheduling problem for carrier aircraft considering the effects of bolting and aerial refueling.Based on the analysis of recovery mode involving the above factors,two types of primary constraints(i.e.,fuel constraint and wake interval constraint)are first described.Then,taking the landing sequencing as decision variables,a combinatorial optimization model with a compound objective function is formulated.Aiming at an efficient solution,an improved firefly algorithm is designed by integrating multiple evolutionary operators.In addition,a dynamic replanning mechanism is introduced to deal with special situations(i.e.,the occurrence of bolting and fuel shortage),where the high efficiency of the designed algorithm facilitates the online scheduling adjustment within seconds.Finally,numerical simulations with sufficient and insufficient fuel cases are both carried out,highlighting the necessity to consider bolting and aerial refueling during the planning procedure.Simulation results reveal that a higher bolting probability,as well as extra aerial refueling operations caused by fuel shortage,will lead to longer recovery complete time.Meanwhile,due to the strong optimum-seeking capability and solution efficiency of the improved algorithm,adaptive scheduling can be generated within milliseconds to deal with special situations,significantly improving the safety and efficiency of the recovery process.An animation is accessible at bilibili.com/video/BV1QprKY2EwD.
基金The National Natural Science Foundation of China(No.51978243,52578360).
文摘Conventional optimal sensor placement(OSP)methods employ the premise that all sensors work perfectly during long-term structural monitoring.However,this premise is often difficult to fulfill in real applications due to poor manufacturing and material aging of sensors,human damage,and electromagnetic interference.This paper presents a robustness-oriented OSP method that considers sensor failures.The OSP problem is designed with consideration of sensor failures to ensure that both complete vibration data collected by all sensors and incomplete vibration data caused by individual sensor failures can accurately identify structural modal parameters.A dispersion-aggregation firefly algorithm(DAFA),which is derived from the basic firefly algorithm,has been proposed to solve this complicated optimization problem.The dispersion and aggregation operators are designed to prevent falling into local optima and to rapidly converge to the global optima.The proposed methodology is confirmed by extracting the robust sensor configuration for a long-span cable-stayed bridge.The robustness of the optimal sensor configurations against sensor failure is thoroughly explored,and the performance of the proposed DAFA is extensively examined.
基金The National Natural Science Foundation of China(No.50805023)the Science and Technology Support Program of Jiangsu Province(No.BE2008081)+1 种基金the Transformation Program of Science and Technology Achievements of Jiangsu Province(No.BA2010093)the Program for Special Talent in Six Fields of Jiangsu Province(No.2008144)
文摘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.
文摘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）.
基金supported by the National Basic Research Program of China(No.2013CB228602)the National Science and Technology Major Project of China(No.2011ZX05004-003)the National High Technology Research Program of China(No.2013AA064202)
文摘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.
基金the Natural Foundation of Sciences of Yunnan Province (2006C0046Q)Partly by the Chinese Academy of Sciences (O706551141)
文摘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.
文摘Artificial night lighting is gaining attention as a new type of pollution;however, studies of its impacts are scarce. Fireflies provide good models to investigate its effects on nocturnal wildlife, since they depend on their bioluminescence for reproduction. This study investigated the impact of artificial illumination on firefly activity at the new campus of the Federal University of São Carlos (Sorocaba, Brazil). The flashing activity of different firefly species, especially Photinus sp1 (82% of all occurrences), was investigated during 3 years, before and after the installation of multi metal vapor spotlights. Quantitative and qualitative analysis, performed in transects at different distances from the artificial light sources, showed significant negative effects on Photinus sp1 occurrence. This study proposes fireflies as potential flagship species and bioindicators for artificial night lighting and for the first time quantifies its effects, providing subsidies for future conservationist legislations regarding photopollution.
基金This study was supported in part by Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research(17H03719,18KK0205,and 21H02551).
文摘Several Asian natricine snakes of the genus Rhabdophis feed on toads and sequester steroidal cardiac toxins known as bufadienolides(BDs)from them.A recent study revealed that species of the Rhabdophis nuchalis Group ingest lampyrine fireflies to sequester BDs.Although several species of fireflies are distributed in the habitat of the R.nuchalis Group,only lampyrine fireflies,which have BDs,are included in the diet of these snakes.Thus,we hypothesized that the R.nuchalis Group chemically distinguishes fireflies that have BDs from those that do not have BDs.We also predicted that the R.nuchalis Group detects BDs as the chemical cue of toxin source.To test these predictions,we conducted 3 behavioral experiments using Rhabdophis chiwen,which belongs to the R.nuchalis Group.In the first experiment,R.chiwen showed a moderate tongue flicking response to cinobufagin,a compound of BDs.On the other hand,the snake showed a higher response to the chemical stimuli of lampyrine fireflies(BD fireflies)than those of lucioline fireflies(non-BD fireflies).In the second experiment,in which we provided live BD and non-BD fireflies,the snake voluntarily consumed only the former.In the third,a Y-maze experiment,the snake tended to select the chemical trail of BD fireflies more frequently than that of non-BD fireflies.These results demonstrated that R.chiwen discriminates BD fireflies from non-BD fireflies,but the prediction that BDs are involved in this discrimination was not fully supported.To identify the proximate mechanisms of the recognition of novel toxic prey in the R.nuchalis Group,further investigation is necessary.
文摘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.
文摘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 https://www.optim-app.com/projects/gbfa.
文摘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.
文摘Wireless Sensor Networks(WSNs)comprises low power devices that are randomly distributed in a geographically isolated region.The energy consumption of nodes is an essential factor to be considered.Therefore,an improved energy management technique is designed in this investigation to reduce its consumption and to enhance the network’s lifetime.This can be attained by balancing energy clusters using a meta-heuristic Firefly algorithm model for network communication.This improved technique is based on the cluster head selection technique with measurement of the tour length of fireflies.Time Division Multiple Access(TDMA)scheduler is also improved with the characteristics/behavior of fireflies and also executed.At last,the development approach shows the progression of the network lifetime,the total number of selected Cluster Heads(CH),the energy consumed by nodes,and the number of packets transmitted.This approach is compared with Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR)and Low Energy Adaptive Clustering Hierarchy(LEAH)protocols.Simulation is performed in MATLAB with the numerical outcomes showing the efficiency of the proposed approach.The energy consumption of sensor nodes is reduced by about 50%and increases the lifetime of nodes by 78%more than AODV,DSR and LEACH protocols.The parameters such as cluster formation,end to end delay,percentage of nodes alive and packet delivery ratio,are also evaluated...The anticipated method shows better trade-off in contrast to existing techniques.
文摘This paper introduces a new approach of firefly algorithm based on opposition-based learning （OBFA） to enhance the global search ability of the original algorithm. The new algorithm employs opposition based learning concept to generate initial population and also updating agents’ positions. The proposed OBFA is applied for minimization of the factor of safety and search for critical failure surface in slope stability analysis. The numerical experiments demonstrate the effectiveness and robustness of the new algorithm.
