Firefly Optimization Research Articles

Blind and Secured Adaptive Digital Image Watermarking Approach for High Imperceptibility and Robustness.

In the past decade, rapid development in digital communication has led to prevalent use of digital images. More importantly, confidentiality issues have also come up recently due to the increase in digital image transmission across the Internet. Therefore, it is necessary to provide high imperceptibility and security to digitally transmitted images. In this paper, a novel blind digital image watermarking scheme is introduced tackling secured transmission of digital images, which provides a higher quality regarding both imperceptibility and robustness parameters. A block based hybrid IWT- SVD transform is implemented for robust transmission of digital images. To ensure high watermark security, the watermark is encrypted using a Pseudo random key which is generated adaptively from cover and watermark images. An encrypted watermark is embedded in randomly selected low entropy blocks to increase the security as well as imperceptibility. Embedding positions within the block are identified adaptively using a Blum–Blum–Shub Pseudo random generator. To ensure higher visual quality, Initial Scaling Factor (ISF) is chosen adaptively from a cover image using image range characteristics. ISF can be optimized using Nature Inspired Optimization (NIO) techniques for higher imperceptibility and robustness. Specifically, the ISF parameter is optimized by using three well-known and novel NIO-based algorithms such as Genetic Algorithms (GA), Artificial Bee Colony (ABC), and Firefly Optimization algorithm. Experiments were conducted for the proposed scheme in terms of imperceptibility, robustness, security, embedding rate, and computational time. Experimental results support higher effectiveness of the proposed scheme. Furthermore, performance comparison has been done with some of the existing state-of-the-art schemes which substantiates the improved performance of the proposed scheme.

A novel cluster head selection using Hybrid Artificial Bee Colony and Firefly Algorithm for network lifetime and stability in WSNs

Wireless Sensor Networks (WSNs) are capable of achieving data dissemination between them such that exploration of their potential could be performed based on their frequency range. It is considered to be highly difficult for recharging sensor devices under adverse situations. The main drawbacks of WSNs concern to the issue of network lifetime, coverage area, scheduling and data aggregation. In particular, prolonging network lifetime confirms the success together with the energy conservation of sensor nodes, data transmission reliability and scalability of their operation in data aggregation. Clustering schemes are considered to be highly suitable for effectively utilising the resources with lower overhead, such that energy consumption is enhanced for upgrading the network lifespan. In this paper, a Hybrid Modified Artificial Bee Colony and Firefly Algorithm (HMABCFA) -Based Cluster Head Selection is proposed for ensuring energy stabilisation, delay minimisation and inter-node distance reduction for improving the network lifetime. This proposed HMABCFA integrates the benefit of the Firefly optimisation algorithm for generating a new position that which has the capability of replacing the position, which is not updated in the scout bee phase of ABC. This incorporation of Firefly optimisation algorithm into the ABC algorithm prevents the limitations of premature convergence, slow convergence and the possibility of being trapped into the local point of optimality in the clustering process. The modified ABC-based clustering process is phenomenal in improving the feasible dimensions for enhancing the process of exploitation and exploration. The results of the HMABCFA, on an average are confirmed to enhance the network lifetime by 23.21%, energy stability by 19.84% and reduce network latency by 22.88%, compared to the benchmarked approaches.

Application of optimisation technique in PV integrated multilevel inverter for power quality improvement

In recent days multilevel inverters (MI) have played an essential role in photovoltaic (PV) integrated supply systems. MI has high efficiency and is more robust compared to the traditional two-level inverter. This paper presents a modular cascaded multilevel inverters (MCMI)-based hybrid shunt active power filter (HSAPF) with PV integrated for power quality (PQ) improvement in three-phase utility systems. The main aim is to deliver the PV system's active power and reduce the current harmonics by injecting the compensation current at the point of intersection (POI). The main contributions in this work presents an optimization based approach called the predator-prey based fire-fly optimization (PPFO) for reducing the total harmonic distortions (THD) and, the Adaptive Perturb and Observe–Fuzzy (APOF) is developed for tracking the maximum power. The effectiveness of the PPFO is tested with other methods like particle swarm optimization (PSO) and fire-fly optimization (FO). The proposed technique is performed using MATLAB/ Simulink tool under different supply conditions.

COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion.

In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomography (CT), and so on, that can be analyzed by artificial intelligence methods for early diagnosis of diseases. Recently, the outbreak of the COVID-19 disease caused many deaths. Computer vision researchers support medical doctors by employing deep learning techniques on medical images to diagnose COVID-19 patients. Various methods were proposed for COVID-19 case classification. A new automated technique is proposed using parallel fusion and optimization of deep learning models. The proposed technique starts with a contrast enhancement using a combination of top-hat and Wiener filters. Two pre-trained deep learning models (AlexNet and VGG16) are employed and fine-tuned according to target classes (COVID-19 and healthy). Features are extracted and fused using a parallel fusion approach—parallel positive correlation. Optimal features are selected using the entropy-controlled firefly optimization method. The selected features are classified using machine learning classifiers such as multiclass support vector machine (MC-SVM). Experiments were carried out using the Radiopaedia database and achieved an accuracy of 98%. Moreover, a detailed analysis is conducted and shows the improved performance of the proposed scheme.

Photo voltaic integrated multilevel inverter based hybrid filter using spotted hyena optimizer

In this work, a photovoltaic (PV) integrated modular multilevel inverter (MMI) is connected to a three-phase system for power quality (PQ) improvement. The MMI has modular design and high-quality output waveform. Thus, it is widely used for medium and high-power applications. The Spotted Hyena Optimization (SHO) technique is proposed for generating the reference current and tuning the parameter of the PI controller in MMI. The proposed SHO technique is operated under different loading conditions and is compared with the conventional particle swarm optimization (PSO) and firefly optimization (FO) technique. The controlling part of MMI is designed to supply power produced from the PV system to the grid and controlling the harmonics and reactive power. This paper employs an incremental conductance (INC) based maximum power point tracking (MPPT) technique for drawing maximum power. The performance of the proposed technique is analysed using the MATLAB/Simulink tool.

Oppositional firefly optimization based optimal feature selection in chronic kidney disease classification using deep neural network

Oppositional firefly optimization based optimal feature selection in chronic kidney disease classification using deep neural network

Comparison of Maximum Power Point Tracking Methods Using Metaheuristic Optimization Algorithms for Photovoltaic Systems

The maximum power value that can be obtained from photovoltaic systems can change continuously due to environmental conditions such as temperature, sunlight and partial shading. Direct current-direct current (DC-DC) converters and maximum power point tracking (MPPT) algorithms are required, especially in cases of partial shading, in order for the photovoltaic systems to operate at the maximum power point, that is, to draw the maximum possible power value from the system. In this study, simulation studies has been carried out for two different partially shaded scenarios using the boost-type DC-DC converter and MPPT algorithm in the PV array consisting of 3 panels connected in series. In the simulation studies, the output powers obtained by the application of particle swarm optimization, cuckoo optimization, bat optimization and firefly optimization techniques as MPPT algorithm has been compared. In the scenarios examined, the firefly optimization algorithm reached the maximum power point faster, and it has been observed that the firefly optimization method obtained the highest average power at the end of the simulation periods.

Two-loop controller design and implementations for an inverted pendulum system with optimal self-adaptive fuzzy-proportional–integral–derivative control

Since inverted pendulum systems (IPSs) are under-actuated systems, controlling these systems has become an important problem. Because of their irregular movements and being structurally unstable, the balancing of these systems poses a major problem. In this study, the dynamic model of a linear IPS has been obtained by using the Newton–Euler method and also conventional proportional–integral–derivative (PID), fuzzy logic (FLC), and self-adaptive fuzzy-proportional–integral–derivative (SAF-PID) control algorithms have been designed for the control of the system. The purpose of these designed controllers is to keep the arms of the IPS on the moving cart in a vertical position and bring the cart to the specified equilibrium position. In conventional control methods, unwanted oscillations have occurred during the movement of the cart. By using two-loop control methods, it is aimed to reduce these oscillations and to design a robust system. The results have been obtained using the conventional PID, FLC, and SAF-PID controllers. To increase the performance of the SAF-PID control type, the optimum value of the coefficients of the points where the legs of the membership functions touch were calculated using the firefly optimization algorithm. To conclude, the designed controllers were performed on computer simulation. The results are given graphically and numerically. Mean absolute error (MAE), mean squared error, integral absolute error IAE, and integral squared error have been compared and examined with each other and with the studies in the literature using performance criteria. As a result, the proposed control method was at least 26.31% more successful in the angular control of the pendulum and at least 9.26% better in the position control of the cart than the studies in the literature.

A novel strength behavior prediction model of self‐compacting concrete with foundry sand and hybrid fibers

AbstractIn this paper, an Improved FireFly Optimization Algorithm (IFFOA)‐based optimal Self‐Organizing Polynomial Neural Network (SOPNN) is proposed to analyze the compressive strength, tensile strength, and flexural strength of the self‐compacting concrete (SCC) with the inclusion of hybrid fibers whenever exposed to high temperature. The SCC mixture is formed by the materials taken in this study along with the hybrid fibers. The preparation of SCC is made by adding water to powder with a proportion of 3:10 and hybrid fibers content is about 2 kg/m3. The samples are heated to a temperature of 210, 320, 530, and 790° at the duration of 7 and 28 days. Experiments are performed to analyze the loss in compressive strength, tensile strength, and flexural strength. The proposed method is used to analyze the loss functions based on the amount of cement, aggregates, temperature, additives along with the inclusion, and exclusion of hybrid fibers. Further, it is found the proposed method analyzes the loss in compressive strength, tensile strength, and flexural strength of SCC more accurately than the other methods.

Cooperative routing in wireless multimedia sensor network with deep learning based decision making system

Wireless Multimedia Sensor Networks (WMSNs) provide great attention on various attractive applications and their scientific researches due to their potential impact. For the past few years, the most active area related to the research field is routing in WMSNs. Moreover, the multimedia data is transmitted based on the routing protocol for determining the efficient resources and stable path, and also provides different levels of Quality-of-Experience (QoE)/Quality-of-Service (QoS) assurance. Still, it is the difficult aspect based on the complicated operations of multimedia applications, dynamic changes of network condition and limited network resources. This paper intends to introduce a Deep Belief Network (DBN) oriented decision making system to select the suitable cooperative nodes. Moreover, constraints like (i) Tri-level energy consumption of nodes-Level 1: Low energy level for text information, Level 2: Medium energy level for text, image and video information, Level 3: High energy level for high definition pictures (ii) Reliability, and (iii) Delay are examined while transmitting the multimedia information in the network. To make the precise decision, it is planned to tune the optimal weight of DBN by a new hybrid Dragon Integrated Firefly (DIFF) algorithm that combines the concept of Firefly Optimization (FF) and Dragonfly Optimization (DA) algorithm. At last the performance of the adopted scheme is computed over other traditional schemes with respect to alive nodes, delay, residual energy, network lifetime and reliability analysis.

A Virtual Machine Migration Mechanism Based on Firefly Optimization for Cloud Computing

Background: Cloud computing is one of the prominent technology revolutions around us. It is changing the ways the consumer expends services, changing the ways the organization develop and run applications and is completely reshaping the old business models in multiple industries. Cloud service providers need large-scale data centers for offering cloud resources to users, the electric power consumed by these data centers has become a concrete and prudential concern. Most of the energy is dissipated in these data centers due to under-utilized hosts, which also subsidies to global warming. The broadly adept technology is virtual machine migration in cloud computing, therefore, the main focus is to save energy. Objective: Virtual Machine (VM) migration can reap various objectives like load balancing, ubiquitous computing, power management, fault tolerance, server maintenance, etc. This paper presents an energy-oriented mechanism for VM migration based on firefly optimization that reduces energy consumption and the number of VM migrations to a great extent. Method: A Firefly Optimization (FFO) oriented VM migration mechanism has been proposed, which allocates tasks to the physical machines in cloud data centers. It strives to migrates high loaded VMs from one physical node to another, which induces minimum energy consumption after VM migration. Results: The empirical result shows that the FFO based mechanism, implemented in the CloudSim simulator, performs better in terms of the number of hosts saved up to 13.91% in contrast to the First Fit Decreasing (FFD) algorithm and 8.21% as compared to Ant Colony Optimization (ACO). It reduced energy consumption up to 12.76% as compared to FFD and 7.78% as compared to ACO and, ultimately lesser number of migrations up to 52.49% when compared to FFD and 44.51% as compared to ACO. Conclusion: The proposed scheme performs better in terms of saving hosts, reducing energy consumption, and decreasing the number of migrations in contrast to FFD and ACO techniques. The research paper also presents challenges and issues in cloud computing, VM migration process, VM migration techniques, their comparative review as well.

A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid

Real-time, accurate, and stable forecasting plays a vital role in making strategic decisions in the smart grid (SG). This ensures economic savings, effective planning, and reliable and secure power system operation. However, accurate and stable forecasting is challenging due to the uncertain and intermittent electric load behavior. In this context, a rigid forecasting model with assertive stochastic and non-linear behavior capturing abilities is needed. Thus, a support vector regression (SVR) model emerged to cater the non-linear time-series predictions. However, it suffers from computational complexity and hard-to-tune appropriate parameters problem. Due to these problems, forecasting results of SVR are not as accurate as required. To solve such problems, a novel hybrid approach is developed by integrating feature engineering (FE) and modified fire-fly optimization (mFFO) algorithm with SVR, namely FE-SVR-mFFO forecasting framework. FE eliminates redundant and irrelevant features to ensure high computational efficiency. The mFFO algorithm obtains and tunes the SVR model’s appropriate parameters to effectively avoid trapping into local optimum and returns accurate forecasting results. Besides, most literature studies are focused on forecast accuracy improvement. However, the forecasting model’s effectiveness and productiveness are determined equally by its stability and convergence rate. Considering only one objective (accuracy or stability or convergence rate) is inadequate; thus, the proposed FE-SVR-mFFO forecasting framework achieves these three relatively independent objectives simultaneously. To evaluate the effectiveness and applicability of the proposed framework, real half-hourly load data of five states of Australia (New South Wales (NSW), Queensland (QLD), South Australia (SA), Tasmania (TAS), and Victoria (VIC)) are employed as a case study. Experimental results show that the proposed framework outperforms benchmark frameworks like EMD-SVR-PSO, FS-TSFE-CBSSO, VMD-FFT-IOSVR, and DCP-SVM-WO in terms of accuracy, stability, and convergence rate.

Improving K-means clustering based on firefly algorithm

Data clustering determines a group of patterns in a dataset which are homogeneous in nature. The objective is to develop an automatic algorithm which can accurately classify an unleveled dataset into groups. The K-means method is the most fundamental partitioned clustering concept. However, the performance of K-means method is fully depending on determining the number of clusters, K, and determining the optimal centroid for performing the clustering process. In this paper, an adaptive firefly optimization algorithm, which is a nature-inspired algorithm, is employed to improve the K-means clustering. The experimental results of clustering two real datasets show that the proposed method is able to effectively outperform other alternatives methods.

Implementation of a Novel Nature Inspired Firefly Optimizer for Power System Stability Enhancement

Enhancement of stability in interconnected power systems is a vital task in recent years. This paper provides a best solution to mitigate the low frequency inertial oscillations in a multimachine network, thereby enhance the system stability. An optimization criterion based on generator speed and load angle is developed with constraints to optimize the controller parameters required for closed loop model of the system. The controller is tuned using a novel nature inspired Improved Firefly Optimization Algorithm (FOA) and the simulation results of the proposed FOA based controller is compared and analyzed with that of Traditional controller and Genetic Algorithm based controllers to confirm the best performance of the proposed controller in enhancing the system stability.

Multi-modal cyber-aggression detection with feature optimization by firefly algorithm

Aggressive comments containing offensive images and inappropriate gesture signs together with textual comments have grown exponentially in the recent past on social media. These aggressive contents on social media are affecting the victims negatively causing fear, stress, sleeping problems and even suicide in some cases. Since social media contents are unmoderated, a technical solution with the characteristic of having automatic flagging of these contents considering the text and images together is highly needed. This article presents a deep learning and binary firefly-based optimization-based model to classify the social media posts into high-aggressive, medium-aggressive, and non-aggressive classes. The proposed model considers both text and images together to evaluate the aggression level of a post. In this model, the image features of the posts are extracted using pre-trained VGG-16 model, whereas the textual features are extracted using a three-layered convolutional neural network in parallel. The image and text features are then combined to get a hybrid feature set which is further optimized using a binary firefly optimization algorithm. Our proposed model improves the results by 11% in terms of the weighted F1-score with optimized features by binary firefly algorithm over non-optimized features.

E-Mail Spam Filtering Through Feature Selection Using Enriched Firefly Optimization Algorithm

E-mail is the most common method of communication because due to its ability to obtain, the rapid modification of messages and low cost of distribution. Spam causes traffic issues and bottlenecks that limit the amount of memory and bandwidth, power and computing speed. For data filtering, various approaches exist that automatically detect and suppress these indefensible messages. A methodology based on Sine- Cosine Algorithm (SCA) introduced which address the problem of space and time complexities are increased in E-Mail spam detection. In this method, WordNet optimized semantic ontology applies different methods based on semantics and similarity measures to reduce the large number of extracted textual features. This paper proposed the Enriched Firefly Optimization Algorithm (EFOA) method effectively selecting suitable features from an upper dimensional space using the fitness function. Once the best feature space is identified through EFOA, the spam classification is done using ANN. Intially, E-mail spam dataset is preprocessed, then the extracted textual features are Semantic-based reduction and Features weights updated using optimized semantic WordNet. The results obtained showed that the ANN classifier after selection of features using EFOA was able to classify e-mails as spam and non-spam. This EFOA demonstrates that the proposed method has led to a remarkable improvement compared to the SCA methods.

Firefly Optimization in IOT Applications for Wireless Mesh Networks

Internet of Things is an budding focus of technical, social, and economical importance. At the same time, Internet of Things hoist momentous challenges that may perhaps plunk in the way of apprehend its prospective reimbursement. The ‘thing’ in IoT may perhaps be a person with a heart monitor or an automobile in the midst of built-in-sensors, i.e. objects that have subsist dole out an IP address and encompass the capability to accumulate and convey data over a network without manual assistance or intervention. The business processes hosted on the wide-spread geographical distribution of sensors should retort to dissimilar sensing events congregate in the dynamic IoT environment as swift as they could. So to Produce better IoT applications, process optimization is indispensable in the IoT environment. For example, data analytics in energy embarrassed IoT Networks uses Optimization Framework to recuperate the network implementation delay and accuracy. Here we imply firefly Optimization Algorithm Framework, wherever the nodes make a decision where their data analytic tasks will be executed, in order to jointly optimize their average execution delay and accuracy, while respecting power consumption constraints compared to previous other techniques. Our new fire fly optimization techniques shows 81% better results compared to previous techniques such as Genetic and ant colony etc…

A novel weather prediction model using a hybrid mechanism based on MLP and VAE with fire-fly optimization algorithm

The future weather data source will continue to grow rapidly, and new developments in machine learning would allow government agencies and companies to use all this data further. The weather prediction will never be flawless, but artificial intelligence (AI) can strive to enhance the exactness and consistency of the process. This paper proposes an approach using a hybrid mechanism based on multi-layer perceptron (MLP) and variational auto-encoder (VAE) with a fire-fly optimization mechanism. Weather-related data contains many features. A few of which are global or generalized features, and some are local or internal features. Single mechanism may not be effective in the process of extracting the specified features. Hence, a hybrid mechanism with the support of VAE and MLP is proposed to extract features and do classification. VAE is used to extract the global features from the weather data and the obtained or processed intermediately output given to the input as the MLP, which will extract all local or internal features very effectively.

A novel optimal firefly algorithm based gain scheduling proportional integral derivative controller for rotor spinning machine speed control

In advanced yarn production, the rotor spinning machine plays a significant role due to its low power consumption and high speed of yarn delivery. Nevertheless, if the speed of the machine exceeds from its desired level, the yarn output will go worsen. Therefore, in this paper, a novel optimal firefly algorithm based gain scheduling proportional-integral-derivative controller is proposed to regulate the speed of linear parameter varying based rotor spinning machine at the desired level. Here, the OFA is added to the GSPID controller for tuning the controller parameters. Consequently, Spline piecewise interpolation is newly developed to regulate the gain tuning parameters in high desired rate. The stability of the projected controller is synthesized by the combination of linear matrix variations with $$H_{\infty }$$ control. Moreover, the error percentage and the accuracy of the proposed system are optimized using OFA. The implementation of this projected work was done in the MATLAB R2018b platform. Furthermore, the simulation result of the developed control strategy is compared with other traditional control approaches and its efficiency measure has proved by gaining less computational time as 10 s, error rate as 0.0982%, and high accuracy as 92%.

An improved firefly algorithm based on personalized step strategy

Firefly algorithm has shown good performance for solving various optimization problems. Most of the nature-inspired algorithms have the same problem which can easily trap in local optimal. To overcome this defect, a novel personalized step strategy for firefly algorithm (PSSFA) is presented. It uses large step for the optimal firefly and linearly decreasing step for the other fireflies to improve the ability of exploration. Experiments on 20 test functions show that the proposed algorithm can promote accuracy of the original method. Finally, we integrate PSSFA with k-means clustering for five datasets. The results show that PSSFA is an effective optimization algorithm.