Chaotic Firefly Algorithm Research Articles

Attitude tracking control of a foldable wave energy powered AUV based on linear time-varying MPC under position constraints

In this paper, we investigate the tracking control of attitude and specific spatial position for a foldable wave energy powered autonomous underwater vehicle (FWEPAUV) under space constraints. In order to reduce energy consumption and maximizing the efficiency of the charging, a cascaded dual closed-loop control strategy is proposed. Firstly, we utilize model predictive control (MPC) with multi-variable constraint capabilities to design the kinematics controller. The output constraints are set as space constraints for MPC, and an objective function is used to strike a balance between control accuracy and energy consumption. The improved chaotic firefly algorithm (ICFA) is chosen due to its rapid convergence and strong robustness. It is employed in the rolling optimization process of MPC to secure a viable solution. Secondly, the kinematics controller is combined with the dynamics controller based on adaptive sliding mode control (ASMC) to achieve robust tracking control. In order to eliminate the interference from time-varying ocean currents and complex external forces meanwhile utilizing these interferes to reduce energy consumption, An ocean current observer based on ICFA-PI control and a prescribed time disturbance observer (PTDO) are devised to estimate them. Finally, an event-triggered (ET) mechanism is applied to integrate with the trajectory tracking controller to decrease the computational load. The stability of the ICFA-MPC controller and the ASMC controller is proven using the Lyapunov stability theory. Simulation results demonstrate that in complex external interference environments, the controller can achieve stable tracking control of the attitude and a specific spatial position, thereby meeting predefined goals such as space constraints, energy savings, and maximizing the efficiency of the charging.

Variable selection in Poisson regression model based on chaotic meta-heuristic search algorithm

By determining the most significant variables that are connected to the response variable, Increasing prediction accuracy and processing speed can be achieved through the process of variable selection. Regression modeling has drawn a lot of interest from several scientific domains. One of the most effective nature-inspired algorithms that has been suggested recently and can be used effectively for variable selection is the Firefly algorithm. The chaotic firefly algorithm is presented in this work to carry out the Poisson regression model's variable selection. A simulation study is carried out to assess how well the suggested strategy performs in terms of variable selection criteria and prediction accuracy. Its effectiveness is also contrasted with alternative approaches. The outcomes demonstrated the effectiveness of our suggested strategies, which beat other widely used approaches.

IoT Resource Allocation and Optimization Using Improved Reptile Search Algorithm

The Internet of Things (IoT) is a dispersed network system that connects the world through the Internet. The architecture of IoT consists of more gateways and resources which cannot be allocated in a manual process. The allocation of resources in IoT is a challenging process due to the higher consumption of energy and high latency rate. To overcome the challenges in existing works, this research introduced an Improved Reptile Search Algorithm (IRSA) to solve the optimization problem which occurs during the time of allocation resources among IoT networks. IRSA employs the methodology of levy flight and cross-over to update the candidate position and enhance the search speed in a single iteration. The proposed method consumes less energy and has low latency during data transmission from User equipment (UE) to the base station.IRSA has been compared with the existing Scalable Resource Allocation Framework (SRAF) and Improved Chaotic Firefly Algorithm (ICFA). The obtained experimental results show that the proposed IRSA attained better performance with an allocation rate of 96.40% which is comparatively higher than SRAF and ICFA with 92.40% and 91.67% respectively.

Selection of variables Affecting Red Blood Cell by Firefly Algorithm

Some maps of the chaotic firefly algorithm were selected to select variables for data on blood diseases and blood vessels obtained from Nasiriyah General Hospital where the data were tested and tracking the distribution of Gamma and it was concluded that a Chebyshevmap method is more efficient than a Sinusoidal map method through mean square error criterion.

Fault-Tolerant Trajectory Tracking Based on Improved Chaotic Firefly Algorithm Model Prediction Control for Human-Occupied Vehicle With Ocean Current

In this article, aiming at the characteristics of model uncertainty and low-speed of human-occupied vehicle (HOV) system, a cascaded controller in ocean current environment is proposed. First, the kinematics controller is designed by improved chaotic firefly algorithm (ICFA)-model predictive control (MPC) to get the speed control signal. The multivariable constraint capability of ICFA-MPC can solve the problem of speed jump caused by thruster fault and further solve the problem of driving saturation. Second, the kinematic controller is combined with the dynamics controller which is according to the adaptive algorithm. The adjustable control parameters capability of the adaptive control can further obtain the dynamic control law under the influence of ocean current. Finally, the hybrid allocation strategy based on ICFA is used to redistribute the thruster force/moments. Simulation results displayed the dynamic trajectory tracking fault-tolerant controller can steadily track the reference trajectory under thruster fault without tracking disturbance, speed jump, and driving saturation problem.

New Model of Feature Selection based Chaotic Firefly Algorithm for Arabic Text Categorization

The dimensionality reduction is a type of problem that appear in the most classification processes. It contains a large number of features; these features may contain unreliable data which may lead the categorization process to unwanted results. Feature selection can be used for reducing dimensionality of datasets and find interesting relevant information. In Arabic language, the number of works applies a meta-heuristic algorithm for feature selection is still limited due to the complex nature of Arabic inflectional and derivational rules as well as its intricate grammatical rules and its rich morphology. This paper proposes a new model for Arabic Feature Selection that combines the chaotic method in the Firefly Algorithm (CFA). The Chaotic Algorithm replaces the attractiveness coefficient in firefly algorithm by the outputs of chaotic application. The enhancement of the new approach involves introducing a novel search strategy which is able to obtain a good ratio between exploitation and exploration abilities of the algorithm. In terms In terms of performance, the experiments of the proposed method are tested using classifiers, namely Naive Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) and three evaluation measures, including precision, recall, and F-measure. The experimental findings show that the combining of CFA and SVM classifiers outperforms other combinations in terms of precision.

Multiscale fatigue damage model for CFRP laminates considering the effect of progressive interface debonding

This paper presents a multiscale progressive fatigue damage model to predict the fatigue life of composite laminates. The multi-level damage model was employed considering the interfacial debonding effect, and effective material properties were calculated through homogenization. Damage variables and damage slopes were defined at the constituent level, and fatigue damage parameters were obtained using the residual stiffness data with the chaotic firefly algorithm. The model was implemented into ABAQUS, then validated with flat-bar and pin-loaded specimens of AS4/3501-6 composite. The numerical results corresponded well with the experimental data and showed the ability to capture the failure propagation of composite laminates.

Echo Location Based Bat Algorithm for Energy Efficient WSN Routing

Due to the wide range of applications, Wireless Sensor Networks (WSN) are increased in day to day life and becomes popular. WSN has marked its importance in both practical and research domains. Energy is the most significant resource, the important challenge in WSN is to extend its lifetime. The energy reduction is a key to extend the network's lifetime. Clustering of sensor nodes is one of the well-known and proved methods for achieving scalable and energy conserving WSN. In this paper, an energy efficient protocol is proposed using metaheuristic Echo location-based BAT algorithm (ECHO-BAT). ECHO-BAT works in two stages. First Stage clusters the sensor nodes and identifies tentative Cluster Head (CH) along with the entropy value using BAT algorithm. The second stage aims to find the nodes if any, with high residual energy within each cluster. CHs will be replaced by the member node with high residual energy with an objective to choose the CH with high energy to prolong the network's lifetime. The performance of the proposed work is compared with Low-Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient Zoning Clustering Algorithm (PEZCA) and Chaotic Firefly Algorithm CH (CFACH) in terms of lifetime of network, death of first nodes, death of 125th node, death of the last node, network throughput and execution time. Simulation results show that ECHO-BAT outperforms the other methods in all the considered measures. The overall delivery ratio has also significantly optimized and improved by approximately 8%, proving the proposed approach to be an energy efficient WSN.

Performance of a Novel Chaotic Firefly Algorithm with Enhanced Exploration for Tackling Global Optimization Problems: Application for Dropout Regularization

Swarm intelligence techniques have been created to respond to theoretical and practical global optimization problems. This paper puts forward an enhanced version of the firefly algorithm that corrects the acknowledged drawbacks of the original method, by an explicit exploration mechanism and a chaotic local search strategy. The resulting augmented approach was theoretically tested on two sets of bound-constrained benchmark functions from the CEC suites and practically validated for automatically selecting the optimal dropout rate for the regularization of deep neural networks. Despite their successful applications in a wide spectrum of different fields, one important problem that deep learning algorithms face is overfitting. The traditional way of preventing overfitting is to apply regularization; the first option in this sense is the choice of an adequate value for the dropout parameter. In order to demonstrate its ability in finding an optimal dropout rate, the boosted version of the firefly algorithm has been validated for the deep learning subfield of convolutional neural networks, with respect to five standard benchmark datasets for image processing: MNIST, Fashion-MNIST, Semeion, USPS and CIFAR-10. The performance of the proposed approach in both types of experiments was compared with other recent state-of-the-art methods. To prove that there are significant improvements in results, statistical tests were conducted. Based on the experimental data, it can be concluded that the proposed algorithm clearly outperforms other approaches.

Variable selection in gamma regression model using chaotic firefly algorithm with application in chemometrics

Variable selection is a very helpful procedure for improving computational speed and prediction accuracy by identifying the most important variables that related to the response variable. Regression modeling has received much attention in several science fields. Firefly algorithm is one of the recently efficient proposed nature-inspired algorithms that can efficiently be employed for variable selection. In this work, chaotic firefly algorithm is proposed to perform variable selection for gamma regression model. A real data application related to the chemometrics is conducted to evaluate the performance of the proposed method in terms of prediction accuracy and variable selection criteria. Further, its performance is compared with other methods. The results proved the efficiency of our proposed methods and it outperforms other popular methods.

Solving Interval Quadratic Programming Problems by Using the Numerical Method and Swarm Algorithms

In this paper, we present a new approach which is based on using numerical solutions and swarm algorithms (SAs) to solve the interval quadratic programming problem (IQPP). We use numerical solutions for SA to improve its performance. Our approach replaced all intervals in IQPP by additional variables. This new form is called the modified quadratic programming problem (MQPP). The Karush–Kuhn–Tucker (KKT) conditions for MQPP are obtained and solved by the numerical method to get solutions. These solutions are functions in the additional variables. Also, they provide the boundaries of the basic variables which are used as a start point for SAs. Chaotic particle swarm optimization (CPSO) and chaotic firefly algorithm (CFA) are presented. In addition, we use the solution of dual MQPP to improve the behavior and as a stopping criterion for SAs. Finally, the comparison and relations between numerical solutions and SAs are shown in some well-known examples.

Load balancing strategy for medical big data based on low delay cloud network

Since the cloud servers are far away from the medical detection terminal and user terminal, the communication overhead such as the delay caused by data transmission is large. At the same time, a large number of medical terminals and user terminals access the cloud servers, which makes the cloud servers overloaded, the overall robustness of the network is poor, and the network is prone to failure, which may lead to the work efficiency of doctors cannot be guaranteed, and the waiting time of patients will also increase. To solve the above problems, according to the characteristics of dynamic resource allocation in the medical big data environment, a new cloud network architecture is proposed. To solve the resource scheduling problem, a chaotic algorithm is introduced into the artificial firefly algorithm, and a load balancing optimisation strategy based on a chaotic firefly algorithm is proposed. The simulation results show that the convergence rate of the proposed algorithm is accelerated by adding chaos factor, to avoid the algorithm falling into the local optimal solution. Compared with other load balancing algorithms, the proposed algorithm is more suitable for solving the resource scheduling problem of large-scale tasks in cloud-fog networks.

A hybrid evolutionary approach for identifying spam websites for search engine marketing

With the increased digital usage, web visibility has become critically essential for organizations when catering to a larger audience. This visibility on the web is directly related to web searches on search engines which is often governed by search engine optimization techniques liked link building and link farming amongst others. The current study identifies metrics for segregating websites for the purpose of link building for search engine optimization as it is important to invest resources in the right website sources. These metrics are further used for detecting websites outliers for effective optimization and subsequent search engine marketing. Two case studies of knowledge management portals from different domains are used having 1682 and 1070 websites respectively for validation of the proposed approach. The study evolutionary intelligence by proposing a k-means chaotic firefly algorithm coupled with k-nearest neighbor outlier detection for solving the problem. Factors like Page Rank, Page Authority, Domain Authority, Alexa Rank, Social Shares, Google Index and Domain Age emerge significant in the process. Further, the proposed chaotic firefly variants are compared to K-Means integrated firefly algorithm, bat algorithm and cuckoo search algorithm for accuracy and convergence showing comparable accuracy. Findings indicate that the convergence speeds are higher for proposed chaotic firefly approach for tuning absorption and attractiveness coefficients resulting in faster search for optimal cluster centroids. The proposed approach contributes both theoretically and methodologically in the domain of vendor selection for identifying genuine websites for avoiding investment on untrustworthy websites.

Resource allocation solution for sensor networks using improved chaotic firefly algorithm in IoT environment

Aiming at the problem that the location of the secondary base station affects the interference between the primary and secondary systems directly and the reasonable allocation of channel resources, an Internet of Things (IoT) sensor network resource allocation scheme using an improved chaotic firefly algorithm is proposed. This solution builds a multi-objective optimization model based on interference analysis of the working scenario of the cognitive radio. The goal is to protect the primary user’s normal activity to maximize the throughput of the secondary system and maximize the number of users that can be covered by the secondary base station. Because the multi-objective model is a non-linear convex optimization problem, the paper uses an improved chaotic firefly algorithm to solve it. Chaos algorithm is introduced into the firefly algorithm. By perturbing individuals, the convergence speed is accelerated and the probability of local optimization is reduced. The algorithm can efficiently obtain the optimal solution while reducing the complexity of the problem. The simulation results show that the method proposed in this paper can optimize the performance of the secondary system while guaranteeing the priority of the primary user. And it is superior to several advanced algorithms.

Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis

Parkinson’s disease is found as a progressive neurodegenerative condition which affects motor circuit by the loss of up to 70% of dopaminergic neurons. Thus, diagnosing the early stages of incidence is of great importance. In this article, a novel chaos-based stochastic model is proposed by combining the characteristics of chaotic firefly algorithm with Kernel-based Naïve Bayes (KNB) algorithm for diagnosis of Parkinson’s disease at an early stage. The efficiency of the model is tested on a voice measurement dataset that is collected from “UC Irvine Machine Learning Repository.” The dynamics of chaos optimization algorithm will enhance the firefly algorithm by introducing six types of chaotic maps which will increase the diversification and intensification capability of chaos-based firefly algorithm. The objective of chaos-based maps is to select initial values of the population of fireflies and change the value of absorption coefficient so as to increase the diversity of populations and improve the search process to achieve global optima avoiding the local optima. For selecting the most discriminant features from the search space, Naïve Bayesian stochastic algorithm with kernel density estimation as learning algorithm is applied to evaluate the discriminative features from different perspectives, namely, subset size, accuracy, stability, and generalization. The experimental study of the problem established that chaos-based logistic model overshadowed other chaotic models. In addition, four widely used classifiers such as Naïve Bayes classifier, k-nearest neighbor, decision tree, and radial basis function classifier are used to prove the generalization and stability of the logistic chaotic model. As a result, the model identified as the best one and could be used as a decision making tool by clinicians to diagnose Parkinson’s disease patients.

Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

Identification in the delta domain: a unified approach via GWOCFA

The identification of linear dynamic systems in the delta domain has been proposed in this paper with the help of a hybrid metaheuristic algorithm combining chaotic firefly algorithm (CFA) and grey wolf optimiser (GWO). GWO performs the global search, while CFA fine-tunes the solutions through its local search abilities, thereby balancing exploration and exploitation features. Linear systems with static nonlinearities at the input are termed as the Hammerstein model, whereas linear systems with static nonlinearities at the output are known as the Wiener model. A test case with continuous polynomial nonlinearities has been taken up for Hammerstein and Wiener system identification in the delta domain. Delta operator parameterisation unifies identification of continuous-time systems with the discrete domain at a higher sampling rate. Pseudo-random binary sequence (PRBS), polluted with white Gaussian noise of fixed signal-to-noise ratio (SNR), has been considered as the input signal to estimate the unknown model parameters as well as static nonlinear coefficients. The hybrid algorithm not only supersedes the parent heuristics of which it is constituted but also proves better in comparison with some standard and latest heuristic approaches reported in the literature. Nonparametric statistical tests are performed to validate the results. The plots of fitness function (normalised value) against the number of iterations also support the convergence speed and accuracy of the results.

Optimal Power Flow with Considering Voltage Stability using Chaotic Firefly Algorithm

In transportation technology, the development of electric vehicle is growing rapidly. In the future, the availability of electrical power is crucial because every electric tool needs electrical power. Power plant must provide electrical power for all consumer include an electric vehicle. Sustainability of electrical power transmission and distribution must be considered as critical due to its high power consumption in the community. One of the problem to supply electrical power is how to keep the system’s voltage stability. Several variations on the load pattern and topological can lead to a substantial poor impact on the system. However, generation cost must be considered by utilities’ operator. This paper demonstrates a recently developed metaheuristic method called Chaotic Firefly Algorithm (CFA). Our simulation shows that this method may perform better than several well-known metaheuristic methods. Therefore, CFA may become a promising method to solve optimal power flow considering voltage stability cases.

Optimal Power Flow with Considering Voltage Stability using Chaotic Firefly Algorithm

In transportation technology, the development of electric vehicle is growing rapidly. In the future, the availability of electrical power is crucial because every electric tool needs electrical power. Power plant must provide electrical power for all consumer include an electric vehicle. Sustainability of electrical power transmission and distribution must be considered as critical due to its high power consumption in the community. One of the problem to supply electrical power is how to keep the system’s voltage stability. Several variations on the load pattern and topological can lead to a substantial poor impact on the system. However, generation cost must be considered by utilities’ operator. This paper demonstrates a recently developed metaheuristic method called Chaotic Firefly Algorithm (CFA). Our simulation shows that this method may perform better than several well-known metaheuristic methods. Therefore, CFA may become a promising method to solve optimal power flow considering voltage stability cases.

Optimum design of large steel skeletal structures using chaotic firefly optimization algorithm based on the Gaussian map

In this paper, a new chaotic firefly algorithm based on Gaussian map (CGFA) is proposed for structural optimization problems. Different chaotic maps have different behaviors, and it is acknowledged by researchers that these maps improve the convergence and prevent algorithms to get stuck on local solutions. The objective function in this paper is considered as the weight of the structure, which is minimized subjected to serviceability and strength requirements. Three steel structures are designed by the LRFD-AISC specification to illustrate the performance of the presented algorithm. Comparison of the results of CGFA with some other well-known metaheuristic algorithms shows the robustness of the algorithm.