Fuzzy Firefly Research Articles

Enhancing Distribution System Pliability and Planning with Distributed Generators Using Fuzzy Firefly Optimization

Enhancing Distribution System Pliability and Planning with Distributed Generators Using Fuzzy Firefly Optimization

Image Enhancement by Using Fuzzy Firefly Optimization and Fuzzy Perceptron Neural Network

The image enhancement methods play an important role in digital image processing. And by using a different kinds of image enhancement techniques, such as artificial intelligence techniques methods. The aim of this methods is to enhance the visual appearance of the digital image and to reduce image noise. In this paper, to enhance the corrupted image and de-noise image, we used swarm optimization algorithms such as a firefly algorithm (FA) and also used neural network such as the perceptron neural network algorithm (PNN). And then after we added the fuzzy membership function to these two algorithms, we obtained to a new method called a fuzzy firefly algorithm (FFA) and fuzzy perceptron neural network algorithm (FPNN). And was computed the performance and efficiency measures for all methods, such as RMSE and PSNR. And the FFA method was the best among the other methods used in this paper.

Fuzzy Firefly Based Intelligent Algorithm for Load Balancing in Mobile 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 firefly with 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), and Utility-based Firefly Algorithm (UFA) when compared in terms of makespan, Degree of imbalance, and Figure of Merit.

Application-specific clustering in wireless sensor networks using combined fuzzy firefly algorithm and random forest

Clustering in wireless sensor networks (WSNs) has proved to be one of the most efficient ways to hierarchically organize the network topology for the purposes of load-balancing and elongating the network lifetime. However, achieving optimal clustering in WSNs is an NP-hard problem, and consequently, heuristics and metaheuristics have been widely adopted. In this paper, a combined clustering technique based on a fuzzy-firefly algorithm (FFA) and random forest (RF), shortly named as: FFA-RF, is presented as an application-specific routing protocol for WSNs. Our FFA-RF protocol entails offline tuning and online routing phases: the offline phase consists of data collection using FFA, training and test of the RF, while the online phase is the actual application of the FFA-RF model to new network instances. In the offline phase, we construct an optimized fuzzy inference system based on FFA and apply it on different network topologies, to collect a comprehensive dataset. We then divide the resulting dataset into training and test sets to train and test the RF model. In the online phase, the trained RF model is used as an online clustering algorithm to estimate the fuzzy priority factor of the nodes for being cluster heads (CHs) in new network instances. To increase the generalizability of the RF for different configurations, node features as well as application features are used as inputs of the RF model. Simulation results for different network topologies demonstrate the superiority of the proposed FFA-RF protocol in prolonging the application-specific lifetime when compared against existing crisp heuristic, fuzzy heuristic, metaheuristic, and combined fuzzy-metaheuristic protocols.

Hybrid state analysis with improved firefly optimized linear congestion models of WSNs for DDOS & CRA attacks.

A decentralized form represents a wireless network that facilitates the computers to direct communication without any router. The mobility of individual nodes is necessary within the restricted radio spectrum where contact is often possible on an Adhoc basis. The routing protocol must face the critical situation in these networks forwarding exploration between communicating nodes may create the latency problem in the future. The assault is one of the issues has direct impact network efficiency by disseminating false messages or altering routing detail. Hence, an enhanced routing approach proposes to defend against such challenges. The efficiency of the designated model of wireless devices relies on various output parameters to ensure the requirements. The high energy efficient algorithms: LEACH with FUZZY LOGIC, GENETIC, and FIREFLY are the most effective in optimizing scenarios. The firefly algorithm applies in a model of hybrid state logic with energy parameters: data percentage, transmission rate, and real-time application where the architecture methodology needs to incorporate the design requirements for the attacks within the specified network environment, which can affect energy and packet distribution under various system parametric circumstances. These representations can determine with the statistical linear congestion model in a wireless sensor network mixed state environment.

Training Strategy of Fuzzy-Firefly Based ANN in Non-Linear Channel Equalization

Channel equalization is remaining a challenge for the researcher. Especially for the non-linear channel as well as the extremely dispersive channel, an effective channel equalizer is required. It is common knowledge that non-linear channel equalizers based on the neural networks (NN) outperform adaptive filter-based linear equalizers. To train NN equalizers, gradient-descent-based approaches like the back-propagation algorithm are often utilized, although they have drawbacks such as trapping of local minima, slower convergence, and compassion to log in. In this work, we presented a novel training strategy using a fuzzy firefly algorithm (FFA) for channel equalization. By using proper network topology and parameters, the suggested training system offers stronger exploitation and exploration skills, as well as the ability to solve the local minima issue. The performance of the equalizer can be analyzed by estimating two parameters i.e. MSE and BER. To exhibit the suggested technique’s resilience in performance, the burst error situation was used, and the outcomes showed that the strategy is more effective in managing such situations than previous methods. The outcomes of the proposed method are presented through simulation, Furthermore, it proved that the suggested method validates a wide range of SNR, and also it outperforms the existing NN-based equalizers.

An Improved Fuzzy Adaptive Firefly Algorithm-Based Hybrid Clustering Algorithms

The implication of firefly and fuzzy firefly optimization algorithms has been greatly witnessed in clustering techniques and extensively used in applications such as Image segmentation. Parameters such as step factor and attractiveness have been kept constant in these algorithms, which affect the convergence rate and accuracy of the clustering process. Though fuzzy adaptive firefly algorithm tackled this problem by making those parameters an adaptive one, issues such as low convergence rate, and provision of non-optimal solutions are still there. To tackle these issues, this paper proposed a novel fuzzy adaptive fuzzy firefly algorithm that significantly improves the accuracy and convergence rate while comparing with the existing optimization algorithms. Further, the proposed algorithm fused with existing hybrid clustering algorithms involving fuzzy set, intuitionistic fuzzy set, and rough set resulted in eight novel hybrid clustering algorithms which lead to better performance in optimizing the selection of initial centroids. To validate the proposal, experimental studies have been conducted on datasets found in bench-marking data repositories such as UCI, and Kaggle. The performance and accuracy evaluation of proposed algorithms have been carried out with the aid of seven accuracy measures. Results clearly indicate the improved accuracy and convergence rate of the proposed algorithms.

A Path Planning Method for Mobile Robots Based on Fuzzy Firefly Algorithms

Introducction: Mobile Robot is a kind of robot system consisting of sensors, remote control operators and automatic control mobile carriers. It is a product of integrated application of integrated disciplines developed in recent years. In the research of mobile robot related technology, navigation technology is its core, and path planning is an important link and subject of navigation research. Objective: An improved firefly algorithm is proposed for path planning of Mobile Robots in this paper. Methods: In this paper, an improved firefly algorithm is proposed. Compared with traditional firefly algorithm, this algorithm has three main improvements: (1) using Sobol sequence to initialize population; (2) adding dynamic disturbance coefficient to enhance the global search ability of the algorithm; (3) considering the uncertainty of search, the attraction between individuals is strong. Fuzzy control is carried out by setting membership function. Results: The new algorithm takes advantage of the uniformity of Sobol sequence sampling and starts to optimize in a wider range, which makes the initial path of the algorithm longer, but because the new algorithm introduces the dynamic disturbance coefficient and the fuzzy control strategy, the average running time is shorter. Conclusion: In the simulation experiment of mobile robot path planning problem, the improved firefly algorithm proposed in this paper is easier to jump out of local optimum than the traditional firefly algorithm, and has more robust search ability. Discussion: It is obvious from the graph that in 100 iterations, the FaFA algorithm takes advantage of the uniformity of Sobol sequence sampling and starts to optimize in a wider range, which makes the initial path of the algorithm longer, but because the FaFA algorithm introduces the dynamic disturbance coefficient and the fuzzy control strategy, it makes the algorithm able.

An Efficient Ids Based on Fuzzy Firefly Optimization and Fast Learning Network

Overseen Interruption Recognition Framework is a framework that has the capacity of picking up from cases about past attacks to perceive new strikes. Using ANN based interruption discovery is promising for decreasing the amount of false negative or false positives in light of the fact that ANN has the capacity of picking up from certified cases. In this article, a made learning model for Quick Learning System (FLN) in light of fluffy firefly streamlining (FFO) has been proposed and named as FF-FLN. The model has been associated with the issue of interruption location and endorsed in perspective of the famous dataset KDD99. Our created strategy has been taken a gander at against a broad assortment of meta-heuristic figurings for planning ELM, and FLN classifier. FF-FLN has defeated other learning approaches in the testing exactness of the learning.

Trust Factor and Fuzzy Firefly Integrated Particle Swarm Optimization Based Intrusion Detection and Prevention System for Secure Routing of MANET

Trust Factor and Fuzzy Firefly Integrated Particle Swarm Optimization Based Intrusion Detection and Prevention System for Secure Routing of MANET

Trust Factor and Fuzzy Firefly Integrated Particle Swarm Optimization Based Intrusion Detection and Prevention System for Secure Routing of MANET

Trust Factor and Fuzzy Firefly Integrated Particle Swarm Optimization Based Intrusion Detection and Prevention System for Secure Routing of MANET

Trust Factor and Fuzzy Firefly Integrated Particle Swarm Optimization Based Intrusion Detection and Prevention System for Secure Routing of MANET

Trust Factor and Fuzzy Firefly Integrated Particle Swarm Optimization Based Intrusion Detection and Prevention System for Secure Routing of MANET

FFBF: cluster-based Fuzzy Firefly Bayes Filter for noise identification and removal from grayscale images

Image denoising gains more attention in the field of image processing, which is essential to sustain the originality of the digital images in order to preserve all the essential information buried in the image. Even though lots of denoising techniques are available, the existing methods failed to denoise the image efficiently, and they are applicable only with lower noise probability. Thus, this paper proposes a Fuzzy Firefly Bayes Filter (FFBF) to perform the noise identification and removal. FFBF employs the Ck-based firefly Bayes algorithm and probabilistic clustering for identifying the presence of noisy pixel in the input image. The Ck-based Firefly Bayes algorithm is newly proposed by integrating the cuckoo search optimization, firefly optimization, and Bayes Classifier and it is based on the maximum posterior probability objective function. The proposed algorithm provides the best solution for the formulation of the binary matrix using the Bayes Classifier, which is subjected to fuzzy-based image denoising. The paper uses two standard images for experimentation, and the comparative analysis is performed in order to determine the superiority of the proposed method. The PSNR, SSIM, and SDME obtained for the proposed method are greater when compared with the existing methods, and the proposed method attained a maximum PSNR, SSIM, and SDME of 45.1696 dB, 0.8260, and 59.9684 dB.

Fuzzy Bio-Inspired Hybrid Techniques for Server Consolidation and Virtual Machine Placement in Cloud Environment

Abstract Cloud computing technology has transformed the information and communication technology industry by authorizing on-demand resource delivery to the cloud users. Datacenters are the major resource storage places from where the resources are disseminated to the requesters. When several requests are received by datacenters, the available resources are to be handled in an optimized way; otherwise the datacenters suffer from resource wastage. Virtualization is the technology that helps the cloud providers to handle several requests in an optimized way. In this regard, virtual machine placement, i.e., the process of mapping virtual machines to physical machines is considered to be the major research issue. In this paper, we propose to apply fuzzy hybrid bio-inspired meta-heuristic techniques for solving the virtual machine placement problem. The cuckoo search technique is hybridized with the fuzzy ant colony optimization and fuzzy firefly colony optimization technique. The experimental results obtained show competing performance of the proposed algorithms.

Effective Loss Minimization and Allocation of Unbalanced Distribution Network

An efficient distribution network must be able to supply power with good voltage profile. The main objective of the proposed work is to allocate losses of the unbalanced distribution network by the firefly algorithm in regulated and deregulated environments before and after loss minimization. Reconfiguration is one of the methods for loss reduction of unbalanced distribution network. Further, optimal placement of distributed generation and capacitor in the reconfigured unbalanced distribution network can further reduce the loss. The results of reconfigured unbalanced distribution network in regulated environment have already been reported. In this paper reconfiguration of an unbalanced distribution network in a deregulated environment is also carried out using an established Fuzzy Firefly algorithm. Loss sensitivity factor of unbalanced distribution networks is used to get the appropriate location of distributed generation and capacitor to be placed in the unbalanced distribution network. Their ratings have been found out by using bacteria foraging optimization algorithm (BFOA). The suggested loss allocation method using Firefly algorithm is implemented at first on 13 node unbalanced distribution network to check the performance of the proposed loss allocation method when compared to other available method. Finally the proposed method has been implemented on 25 node unbalanced distribution network. Both of the implementations are carried out under MATLAB environment.

A New Fuzzy Firefly Algorithm with Adaptive Parameters

Firefly algorithm is a swarm based algorithm that can be used for solving optimization problems. This paper proposed an improved fuzzy adaptive firefly algorithm (FAFA). In the proposed FAFA, a fuzzy system is used to adapt Firefly Algorithm’s parameters in order to improve its ability in global and local searches. Also, we used different fireflies initializing intervals and different iteration numbers to show the algorithm capability to find global optima. Results focus on the two case study categories of function optimization (seven benchmark functions) and presented a novel optimal multilevel thresholding approach for histogram-based image segmentation by using proposed FAFA and Otsu method. Evidence indicates that the optimization results of proposed FAFA approach are so better than the standard FA.

Fuzzy firefly clustering for tumour and cancer analysis

Fuzzy firefly clustering for tumour and cancer analysis

Fuzzy firefly clustering for tumour and cancer analysis

Swarm intelligence represents a meta-heuristic approach to solve a wide variety of problems. Searching for similar patterns of genes is becoming very essential to predict the expression of genes under various conditions. Firefly clustering inspired by the behaviour of fireflies helps in grouping genes that behave alike. Contrasting hard clustering methodology, fuzzy clustering assigns membership values for every gene and predicts the possibility of belonging to every cluster. To distinguish highly expressed and suppressed genes, the research in this paper proposes an efficient fuzzy-firefly clustering by integrating the merits of firefly and fuzzy clustering. The proposed method is compared with other swarm optimisation based clustering algorithms. It is applied on five gene expression datasets. The clusters resulting from the proposed algorithm provide interpretations of different gene expression patterns present in the cancer datasets. Experimental results show the excellent performance of fuzzy-firefly clustering to separate co-expressed and co-regulated genes.

Fuzzy Logic and Firefly Algorithm based Hybrid System for Energy Efficient Operation of Three Phase Induction Motor Drives

Background/Objectives: Three phase induction motors have been the workforce for several industrial, manufacturing, propulsion and transportation applications for the past several years. Energy efficient operation of induction motors is the important task in industries.The objective of the present paper is to provide energy efficient operation of induction motors using soft computing techniques. Methods/Statistical analysis: A fuzzy logic and firefly algorithm based hybrid technique is used for performance improvement of a.c.voltage controller fed three phase induction motor drives. Simulation is done by using matlab software. Findings: It is shown that the conventional search method for reaching the best efficiency point has got many drawbacks. These demerits are overcomed using the proposed softcomputing techniques. Application/ Improvements: Improvement in motor efficiency and power factor is achieved and the motor always operates in the energy efficient region.

A Firefly Colony and Its Fuzzy Approach for Server Consolidation and Virtual Machine Placement in Cloud Datacenters

Managing cloud datacenters is the most prevailing challenging task ahead for the IT industries. The data centers are considered to be the main source for resource provisioning to the cloud users. Managing these resources to handle large number of virtual machine requests has created the need for heuristic optimization algorithms to provide the optimal placement strategies satisfying the objectives and constraints formulated. In this paper, we propose to apply firefly colony and fuzzy firefly colony optimization algorithms to solve two key issues of datacenters, namely, server consolidation and multiobjective virtual machine placement problem. The server consolidation aims to minimize the count of physical machines used and the virtual machine placement problem is to obtain optimal placement strategy with both minimum power consumption and resource wastage. The proposed techniques exhibit better performance than the heuristics and metaheuristic approaches considered in terms of server consolidation and finding optimal placement strategy.