Optimizing Bucket Elevator Performance through a Blend of Discrete Element Method, Response Surface Methodology, and Firefly Algorithm Approaches

This research introduces a novel approach to enhancing bucket elevator design and operation through the integration of discrete element method(DEM)simulation,design of experiments(DOE),and metaheuristic optimization algorithms.Specifically,the study employs the firefly algorithm(FA),a metaheuristic optimization technique,to optimize bucket elevator parameters for maximizing transport mass and mass flow rate discharge of granular materials under specified working conditions.The experimental methodology involves several key steps:screening experiments to identify significant factors affecting bucket elevator operation,central composite design(CCD)experiments to further explore these factors,and response surface methodology(RSM)to create predictive models for transport mass and mass flow rate discharge.The FA algorithm is then applied to optimize these models,and the results are validated through simulation and empirical experiments.The study validates the optimized parameters through simulation and empirical experiments,comparing results with DEM simulation.The outcomes demonstrate the effectiveness of the FA algorithm in identifying optimal bucket parameters,showcasing less than 10%and 15%deviation for transport mass and mass flow rate discharge,respectively,between predicted and actual values.Overall,this research provides insights into the critical factors influencing bucket elevator operation and offers a systematic methodology for optimizing bucket parameters,contributing to more efficient material handling in various industrial applications.

Fuzzy Firefly Based Intelligent Algorithm for Load Balancing inMobile Cloud Computing

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.

Shape and Size Optimization of Truss Structures under Frequency Constraints Based on Hybrid Sine Cosine Firefly Algorithm

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.

Robust Image Watermarking Using LWT and Stochastic Gradient Firefly Algorithm

Watermarking of digital images is required in diversified applicationsranging from medical imaging to commercial images used over the web.Usually, the copyright information is embossed over the image in the form ofa logo at the corner or diagonal text in the background. However, this formof visible watermarking is not suitable for a large class of applications. In allsuch cases, a hidden watermark is embedded inside the original image as proofof ownership. A large number of techniques and algorithms are proposedby researchers for invisible watermarking. In this paper, we focus on issuesthat are critical for security aspects in the most common domains like digitalphotography copyrighting, online image stores, etc. The requirements of thisclass of application include robustness (resistance to attack), blindness (directextraction without original image), high embedding capacity, high Peak Signalto Noise Ratio (PSNR), and high Structural Similarity Matrix (SSIM). Mostof these requirements are conflicting, which means that an attempt to maximizeone requirement harms the other. In this paper, a blind type of imagewatermarking scheme is proposed using Lifting Wavelet Transform (LWT)as the baseline. Using this technique, custom binary watermarks in the formof a binary string can be embedded. Hu’s Invariant moments’ coefficientsare used as a key to extract the watermark. A Stochastic variant of theFirefly algorithm (FA) is used for the optimization of the technique. Undera prespecified size of embedding data, high PSNR and SSIM are obtainedusing the Stochastic Gradient variant of the Firefly technique. The simulationis done using Matrix Laboratory (MATLAB) tool and it is shown that theproposed technique outperforms the benchmark techniques of watermarkingconsidering PSNR and SSIM as quality metrics.

Firefly-CDDL:A Firefly-Based Algorithm for Cyberbullying Detection Based on Deep Learning

There are several ethical issues that have arisen in recent years due to the ubiquity of the Internet and the popularity of social media and community platforms.Among them is cyberbullying,which is defined as any violent intentional action that is repeatedly conducted by individuals or groups using online channels against victims who are not able to react effectively.An alarmingly high percentage of people,especially teenagers,have reported being cyberbullied in recent years.A variety of approaches have been developed to detect cyberbullying,but they require time-consuming feature extraction and selection processes.Moreover,no approach to date has examined the meanings of words and the semantics involved in cyberbullying.In past work,we proposed an algorithm called Cyberbullying Detection Based on Deep Learning(CDDL)to bridge this gap.It eliminates the need for feature engineering and generates better predictions than traditional approaches for detecting cyberbullying.This was accomplished by incorporating deep learning—specifically,a convolutional neural network(CNN)—into the detection process.Although this algorithm shows remarkable improvement in performance over traditional detection mechanisms,one problem with it persists:CDDL requires that many parameters(filters,kernels,pool size,and number of neurons)be set prior to classification.These parameters play a major role in the quality of predictions,but a method for finding a suitable combination of their values remains elusive.To address this issue,we propose an algorithm called firefly-CDDL that incorporates a firefly optimisation algorithm into CDDL to automate the hitherto-manual trial-and-error hyperparameter setting.The proposed method does not require features for its predictions and its detection of cyberbullying is fully automated.The firefly-CDDL outperformed prevalent methods for detecting cyberbullying in experiments and recorded an accuracy of 98%within acceptable polynomial time.

Levy Flight Firefly Based Efficient Resource Allocation for Fog Environment

Fog computing is an emergent and powerful computing paradigm to serve latency-sensitive applications by executing internet of things(IoT)appli-cations in the proximity of the network.Fog computing offers computational and storage services between cloud and terminal devices.However,an efficient resource allocation to execute the IoT applications in a fog environment is still challenging due to limited resource availability and low delay requirement of services.A large number of heterogeneous shareable resources makes fog computing a complex environment.In the sight of these issues,this paper has proposed an efficient levy flight firefly-based resource allocation technique.The levy flight algorithm is a metaheuristic algorithm.It offers high efficiency and success rate because of its longer step length and fast convergence rate.Thus,it treats global optimization problems more efficiently and naturally.A system framework for fog computing is presented,followed by the proposed resource allocation scheme in the fog computing environment.Experimental evaluation and comparison with the firefly algorithm(FA),particle swarm optimization(PSO),genetic algorithm(GA)and hybrid algorithm using GA and PSO(GAPSO)have been conducted to validate the effectiveness and efficiency of the proposed algorithm.Simulation results show that the proposed algorithm performs efficient resource allocation and improves the quality of service(QoS).The proposed algorithm reduces average waiting time,average execution time,average turnaround time,processing cost and energy consumption and increases resource utilization and task success rate compared to FA,GAPSO,PSO and GA.

Optimization of Cognitive Radio System Using Enhanced Firefly Algorithm

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.

Automatic Sentimental Analysis by Firefly with Levy and Multilayer Perceptron

The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and ideas.Many people share their views and ideas around the world through social media like Facebook and Twitter.The goal of opinion mining,commonly referred to as sentiment analysis,is to categorise and forecast a target’s opinion.Depending on if they provide a positive or negative perspective on a given topic,text documents or sentences can be classified.When compared to sentiment analysis,text categorization may appear to be a simple process,but number of challenges have prompted numerous studies in this area.A feature selection-based classification algorithm in conjunction with the firefly with levy and multilayer perceptron(MLP)techniques has been proposed as a way to automate sentiment analysis(SA).In this study,online product reviews can be enhanced by integrating classification and feature election.The firefly(FF)algorithm was used to extract features from online product reviews,and a multi-layer perceptron was used to classify sentiment(MLP).The experiment employs two datasets,and the results are assessed using a variety of criteria.On account of these tests,it is possible to conclude that the FFL-MLP algorithm has the better classification performance for Canon(98%accuracy)and iPod(99%accuracy).

Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud

CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.

A Modified Firefly Optimization Algorithm-Based Fuzzy Packet Scheduler for MANET

In Mobile ad hoc Networks(MANETs),the packet scheduling process is considered the major challenge because of error-prone connectivity among mobile nodes that introduces intolerable delay and insufficient throughput with high packet loss.In this paper,a Modified Firefly Optimization Algorithm improved Fuzzy Scheduler-based Packet Scheduling(MFPA-FSPS)Mechanism is proposed for sustaining Quality of Service(QoS)in the network.This MFPA-FSPS mechanism included a Fuzzy-based priority scheduler by inheriting the merits of the Sugeno Fuzzy inference system that potentially and adaptively estimated packets’priority for guaranteeing optimal network performance.It further used the modified Firefly Optimization Algorithm to optimize the rules uti-lized by the fuzzy inference engine to achieve the potential packet scheduling pro-cess.This adoption of a fuzzy inference engine used dynamic optimization that guaranteed excellent scheduling of the necessitated packets at an appropriate time with minimized waiting time.The statistical validation of the proposed MFPA-FSPS conducted using a one-way Analysis of Variance(ANOVA)test confirmed its predominance over the benchmarked schemes used for investigation.

High-quality Development Path of Night Tourism in Guangzhou:A Case Study of Firefly Night Tour

Night tourism often involves a large number of lighting facilities,which consume a large amount of energy.Therefore,one of the unique low energy consumption natural ecological tourism activities—firefly night tour has attracted attention and become an important breakthrough point for night tourism in tourist destinations.In this paper,Guangzhou firefly night tour project is taken as the research object.Based on the comprehensive economic,environmental,and socio-cultural benefits brought by the development of firefly night tour,the resources distribution,current development status,and existing problems of firefly night tour in Guangzhou are analyzed,and its high-quality development paths are proposed from three levels:government,industry,and tourist.The aim is to explore a new model for the economic development of Guangzhou night tour,boosting the transformation and upgrading of the night tourism economy,while also providing reference ideas and value for the development of night tourism economy in other tourist destinations.

Defect image segmentation using multilevel thresholding based on firefly algorithm with opposition-learning

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.

Phylogenetic Relationship of the Firefly,Diaphanes pectinealis(Insecta,Coleoptera,Lampyridae) Based on DNA Sequence and Gene Structure of Luciferase

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）.

Rayleigh wave nonlinear inversion based on the Firefly algorithm

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.

Cloning,Expression and Sequence Analysis of A Luciferase Gene from the Chinese Firefly Pyrocoelia pygidialis

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.

Hybrid Clustering Using Firefly Optimization and Fuzzy C-Means Algorithm

Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis of the increasing data. The Firefly Algorithm (FA) is one of the bio-inspired algorithms and it is recently used to solve the clustering problems. In this paper, Hybrid F-Firefly algorithm is developed by combining the Fuzzy C-Means (FCM) with FA to improve the clustering accuracy with global optimum solution. The Hybrid F-Firefly algorithm is developed by incorporating FCM operator at the end of each iteration in FA algorithm. This proposed algorithm is designed to utilize the goodness of existing algorithm and to enhance the original FA algorithm by solving the shortcomings in the FCM algorithm like the trapping in local optima and sensitive to initial seed points. In this research work, the Hybrid F-Firefly algorithm is implemented and experimentally tested for various performance measures under six different benchmark datasets. From the experimental results, it is observed that the Hybrid F-Firefly algorithm significantly improves the intra-cluster distance when compared with the existing algorithms like K-means, FCM and FA algorithm.

Path planning in uncertain environment by using firefly algorithm

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.

Nature Inspired Improved Firefly Algorithm for Node Clustering in WSNs

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.

Design of a Proportional-Integral-Derivative Controller for an Automatic Generation Control of Multi-area Power Thermal Systems Using Firefly Algorithm

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.

Opposition-Based Firefly Algorithm for Earth Slope Stability Evaluation

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.