Firefly Algorithm Algorithm Research Articles

Performance Comparison of FA, PSO and CS application in SINR Optimisation for LCMV Beamforming Technique

A beamforming technique called Linearly Constraint Minimum Variance (LCMV) allows directing a radiation beam towards the desired direction to minimise interference of the signal through weight vectors that are computed by LCMV. Generally, to achieve a favourable beam shape, LCMV’s weights are not exactly steered towards the user’s direction. In addition, traditional methods are not equipped well to seamlessly improve the weights of LCMV. This paper employs Particle Swarm Optimisation (PSO), Firefly Algorithm (FA) and Cuckoo Search (CS) to optimise the weights of LCMV. The key anticipated goal in LCMV optimisation is the power reduction on the interferences’ side to achieve a favourable beam shape and better SINR output. A common metaheuristic algorithm is Particle Swarm Optimisation (PSO), which deals with the social behaviour of creatures such as bird flocking. A population and attraction-based algorithm is employed in Firefly algorithm; the flashing characteristics of fireflies are the inspiration of the swarm intelligence metaheuristic algorithm. Also, a novel equation-based nature inspired algorithm is Cuckoo Search (CS), which is based on the brood parasitism of a few cuckoo species combined with the so-called Levy flights. Based on simulation results, FA showed enhanced ability to precisely determine power allocation’s optimal direction when compared with CS and PSO. Thus, better SINR results could be achieved with FA. For SINR optimisation using the LCMV technique, the effectiveness of CS in comparison with FA and PSO algorithms was simulated employing MATLAB®.

Synthesis of Linear Antenna Arrays Using Enhanced Firefly Algorithm

Nature inspired algorithms are finding extensive applications in real-world applications. Firefly algorithm (FA) is one such swarm intelligent algorithm introduced in the recent past. This algorithm has proved its competitiveness over standard benchmark and real-world applications, but suffers from the problem of slow convergence speed. So, in order to overcome this problem, a modified FA approach called enhanced firefly algorithm (EFA) is proposed. The performance of the proposed EFA with respect to FA and other algorithms has been evaluated for eleven benchmark functions. The numerical results show that the novel method consistently provides better solution at a faster rate. Moreover, as a real-world application, EFA has been used for synthesis of linear antenna array for both equally and unequally spaced arrays. The results demonstrate that EFA provides reduced sidelobe level and faster convergence in comparison with algorithms like FA, biogeography-based optimization, cuckoo search, differential evolution, genetic algorithm, particle swarm optimization, tabu search and Taguchi method.

Synergism of Firefly Algorithm and Q-Learning for Robot Arm Path Planning

Abstract Over the past few decades, Firefly Algorithm (FA) has attracted the attention of many researchers by virtue of its capability of solving complex real-world optimization problems. The only factor restricting the efficiency of this FA algorithm is the need of having balanced exploration and exploitation while searching for the global optima in the search-space. This balance can be established by tuning the two inherent control parameters of FA. One is the randomization parameter and another is light absorption coefficient, over iterations, either experimentally or by an automatic adaptive strategy. This paper aims at the later by proposing an improvised FA which involves the Q-learning framework within itself. In this proposed Q-learning induced FA (QFA), the optimal parameter values for each firefly of a population are learnt by the Q-learning strategy during the learning phase and applied thereafter during execution. The proposed algorithm has been simulated on fifteen benchmark functions suggested in the CEC 2015 competition. In addition, the proposed algorithm's superiority is tested by conducting the Friedman test, Iman–Davenport and Bonferroni Dunn test. Moreover, its suitability for application in real-world constrained environments has been examined by employing the algorithm in the path planning of a robotic manipulator amidst various obstacles. To avoid obstacles one mechanism is designed for the robot-arm. The results, obtained from both simulation and real-world experiment, confirm the superiority of the proposed QFA over other contender algorithms in terms of solution quality as well as run-time complexity.

Surface roughness optimization in machining of AZ31 magnesium alloy using ABC algorithm

Magnesium alloys serve as excellent substitutes for materials traditionally used for engine block heads in automobiles and gear housings in aircraft industries. AZ31 is a magnesium alloy finds its applications in orthopedic implants and cardiovascular stents. Surface roughness is an important parameter in the present manufacturing sector. In this work optimization techniques namely firefly algorithm (FA), particle swarm optimization (PSO) and artificial bee colony algorithm (ABC) which are based on swarm intelligence techniques, have been implemented to optimize the machining parameters namely cutting speed, feed rate and depth of cut in order to achieve minimum surface roughness. The parameter Ra has been considered for evaluating the surface roughness. Comparing the performance of ABC algorithm with FA and PSO algorithm, which is a widely used optimization algorithm in machining studies, the results conclude that ABC produces better optimization when compared to FA and PSO for optimizing surface roughness of AZ 31.

A new metaheuristic algorithm: car tracking optimization algorithm

Over the last decade, several metaheuristic algorithms have emerged to solve numerical function optimization problems. Since the performance of these algorithms presents a suboptimal behavior, a large number of studies have been carried out to find new and better algorithms. Therefore, this paper proposes a new metaheuristic algorithm, namely the car tracking optimization algorithm; it is inspired by observing the programming methods of other metaheuristic algorithms. And the proposed algorithm has been tested over 55 benchmark functions, and the results have been compared with firefly algorithm (FA), cuckoo searching algorithm (CS), and vortex search algorithm (VS). The results indicate that the performance of the proposed algorithm surpasses FA, CS, and VS algorithm.

High Performance Maximum Power Point Tracking on Wind Energy Conversion System

<span>This paper presents the maximum power point tracking (MPPT) to extract the power of wind energy conversion system (WECS) using the Firefly Algorithm (FA) algorithm. This paper aims to present the FA as one of the accurate algorithms in MPPT techniques. Recently, researchers tend to apply the MPPT digital technique with the P n O algorithm to track MPP. On the other hand, this Paper implements the FA included in the digital classification to improve the performance of the MPPT technique. Therefore, the FA tracking results are verified with P n O to show the accuracy of the MPPT algorithm. The results obtained show that performance is higher when using the FA algorithm</span>

Dynamic measurement errors prediction for sensors based on firefly algorithm optimize support vector machine

The accuracy of dynamic measurement error predicting has the great significant influence on the precision and stability of the sensors. In order to solve the problem of the low accuracy of the model in traditional dynamic measurement error prediction, support vector machine (SVM) is applied to predicting the dynamic measurement error of sensors. However, in prediction tasks, a proper set of design parameters are essential for the performance and efficiency of SVM model. Hence, parameters selection for SVM by firefly algorithm (FA) is proposed in this study to avoid the local minimum value which occurs in the traditional method of parameter optimization. Two sensors of dynamic measurement error data are considered for modeling. Root mean squared error (RMSE) and mean absolute percentage error (MAPE) are employed to evaluate the performances of the models. These results are also compared with the results obtained from the grid search SVM (GS-SVM)and the particle swarm optimization SVM (PSO-SVM). Experimental results show that the model of SVM based on FA algorithm predicts more accurately and effectively on dynamic measurement errors prediction for sensors than the other models.

Firefly algorithm with dynamic attractiveness model and its application on wireless sensor networks

Firefly algorithm (FA) is a new population-based meta-heuristic algorithm which has outstanding performance on many optimisation problems. However, in standard FA, the attractiveness quickly approaches a constant in the middle period of the iterations. It may be very detrimental to the search ability of the algorithm. So we propose a new variant FA (DFA) with a dynamic attractiveness model which can linearly adjust the rate of change of attractiveness as the number of iterations grows. Thirteen well-known benchmark functions are used to verify the performance of our proposed method; the computational results show that DFA is more efficient than many other FA algorithms. We also successfully used DFA to solve the wireless sensor network node distribution optimisation problem; the results of the coverage statistics further validate the effectiveness of the proposed algorithm.

Firefly algorithm with dynamic attractiveness model and its application on wireless sensor networks

Firefly algorithm (FA) is a new population-based meta-heuristic algorithm which has outstanding performance on many optimisation problems. However, in standard FA, the attractiveness quickly approaches a constant in the middle period of the iterations. It may be very detrimental to the search ability of the algorithm. So we propose a new variant FA (DFA) with a dynamic attractiveness model which can linearly adjust the rate of change of attractiveness as the number of iterations grows. Thirteen well-known benchmark functions are used to verify the performance of our proposed method; the computational results show that DFA is more efficient than many other FA algorithms. We also successfully used DFA to solve the wireless sensor network node distribution optimisation problem; the results of the coverage statistics further validate the effectiveness of the proposed algorithm.

A dual population based firefly algorithm and its application on wireless sensor network coverage optimisation

Firefly algorithm (FA) has shown good performance on many engineering optimisation problems. Recent study has pointed out that FA suffers from slow convergence. To enhance the performance of FA, this paper presents a dual population based FA (called DPFA). In DPFA, the entire population consists of two sub-populations. A memetic FA (MFA) and the standard differential evolution are used to generate new solutions in different sub-populations. To verify the performance of DPFA, we test it on nine benchmark functions. Simulation results show that DPFA outperforms MFA and other improved FA algorithms. Finally, we use the proposed DPFA to solve wireless sensor network coverage optimisation problems. Results show that DPFA can also achieve promising solutions.

A dual population based firefly algorithm and its application on wireless sensor network coverage optimisation

Firefly algorithm (FA) has shown good performance on many engineering optimisation problems. Recent study has pointed out that FA suffers from slow convergence. To enhance the performance of FA, this paper presents a dual population based FA (called DPFA). In DPFA, the entire population consists of two sub-populations. A memetic FA (MFA) and the standard differential evolution are used to generate new solutions in different sub-populations. To verify the performance of DPFA, we test it on nine benchmark functions. Simulation results show that DPFA outperforms MFA and other improved FA algorithms. Finally, we use the proposed DPFA to solve wireless sensor network coverage optimisation problems. Results show that DPFA can also achieve promising solutions.

Optimization studies in high speed turning of Ti-6Al-4V

Surface roughness is a major concern to the present manufacturing sector without the wastage of material. Hence, in order to achieve good surface roughness and reduce production time, optimization is necessary. In this study optimization techniques based on swarm intelligence (SI) namely firefly algorithm (FA), particle swarm optimization (PSO) and a newly introduced metaheuristic algorithm namely bat algorithm (BA) has been implemented for optimizing machining parameters namely cutting speed, feed rate, depth of cut and tool flank wear and cutting tool vibrations in order to achieve minimum surface roughness. Two parameters Ra and Rt have been considered for evaluating the surface roughness. The performance of BA algorithm has been compared with FA algorithm and PSO, which is a commonly and widely used optimization algorithm in machining. The results conclude that BA produces better optimization, when compared to FA and PSO. Based on the literature review carried out, this work is a first attempt at using a metaheuristic algorithm namely BA in machining applications.

Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm

The main objective of blasting operations is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as flyrock. Flyrock is the source of most of the injuries and property damage in a majority of blasting accidents in surface mines. Therefore, proper prediction and subsequently optimization of flyrock distance may reduce the possible damages. The first objective of this study is to develop a new predictive model based on gene expression programming (GEP) for predicting flyrock distance. To achieve this aim, three granite quarry sites in Malaysia were investigated and a database composed of blasting data of 76 operations was prepared for modelling. Considering changeable GEP parameters, several GEP models were constructed and the best one among them was selected. Coefficient of determination values of 0.920 and 0.924 for training and testing datasets, respectively, demonstrate that GEP predictive equation is capable enough of predicting flyrock. The second objective of this study is to optimize blasting data for minimization purpose of flyrock. To do this, a new non-traditional optimization algorithm namely firefly algorithm (FA) was selected and used. For optimization purposes, a series of analyses were performed on the FA parameters. As a result, implementing FA algorithm, a reduction of about 34 % in results of flyrock distance (from 60 to 39.793 m) was observed. The obtained results of this study are useful to minimize possible damages caused by flyrock.

Optimization of 5.5-GHz CMOS LNA parameters using firefly algorithm

This paper presents an optimal design of low noise amplifier (LNA) using an efficient swarm-based optimizer called firefly algorithm (FA). Many researchers have used firefly algorithm to solve various nonlinear engineering problems and reported outstanding results. In view of this, FA is implemented for the first time in this paper to optimize the parameters of LNA like gain and noise figure (NF). Two case studies have been performed which includes the minimization of NF and maximization of gain. Optimization of these two parameters has been carried out by considering each parameter as a single objective function. Penalty factor method is considered for handling the constraints. Other parameters of LNA like power consumption, linearity, and stability are also discussed for both the cases. The designed LNA has a cascode structure with inductive source degeneration topology and is implemented in UMC 0.18-μm CMOS technology using CADENCE software. LNA is designed for 5.5-GHz frequency. The performance of FA in optimizing the parameters of LNA is also compared with the performance of other similar contemporary algorithms like particle swarm optimization (PSO), human behavior PSO (HB-PSO), backtracking search algorithm, and cuckoo search algorithm (CSA). The optimized value of LNA parameter using FA and other algorithms when simulated in MATLAB environment is compared with the simulated result of CADENCE. Statistical analysis is also performed for each case study, and the results are compared with the above-mentioned optimization algorithms. Simulation results, comparative study, and statistical analysis confirm the superiority of FA over other methods in terms of its computational efficiency, consistency, and robustness.

Hybrid Clustering using Firefly Optimization Technique and K-Means Algorithm

In the past few decades, cluster analysis has contributed a vital role in a diversity of fields in engineering. Clustering and classification are the two basic tasks in data mining to retrieve the useful information from databases. 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. The k-means clustering algorithm suffers from local convergence problem and it is sensitive to initial seed points selection. To overcome the problems in the k-means and to enhance the FA algorithm, the hybrid K-Firefly algorithm is developed in this paper by incorporating K-means operator in the end of each iteration of the FA algorithm. This proposed K-Firefly algorithm combines the goodness of the two algorithms and improves the clustering accuracy. In this research work, the Hybrid K-Firefly algorithm is implemented and experimentally tested for various performance measures under six different benchmark datasets chosen from UCI Machine learning repository. From the experimental results, it is observed that the Hybrid K-Firefly algorithm significantly improves the intra-cluster distance when compared with the existing algorithms like K-means, FCM and FA algorithm.

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.

A Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Global Optimization

In this paper, a new and an effective combination of two metaheuristic algorithms, namely Firefly Algorithm and the Differential evolution, has been proposed. This hybridization called as HFADE, consists of two phases of Differential Evolution (DE) and Firefly Algorithm (FA). Firefly algorithm is the nature- inspired algorithm which has its roots in the light intensity attraction process of firefly in the nature. Differential evolution is an Evolutionary Algorithm that uses the evolutionary operators like selection, recombination and mutation. FA and DE together are effective and powerful algorithms but FA algorithm depends on random directions for search which led into retardation in finding the best solution and DE needs more iteration to find proper solution. As a result, this proposed method has been designed to cover each algorithm deficiencies so as to make them more suitable for optimization in real world domain. To obtain the required results, the experiment on a set of benchmark functions was performed and findings showed that HFADE is a more preferable and effective method in solving the high-dimensional functions

Hybrid firefly and Particle Swarm Optimization algorithm for the detection of Bundle Branch Block

Abnormal Cardiac beat identification is a key process in the detection of heart ailments. This work proposes a technique for the detection of Bundle Branch Block (BBB) using hybrid Firefly and Particle Swarm Optimization (FFPSO) technique in combination with Levenberg Marquardt Neural Network (LMNN) classifier. BBB is developed when there is a block along the electrical impulses travel to make heart to beat. ECG feature extraction is a key process in detecting heart ailments. Our present study comes up with a hybrid method combining the two meta-heuristic optimization methods, Firefly algorithm (FFA) and Particle Swarm Optimization (PSO), for feature optimization of ECG (BBB and normal) patterns. One of the major controlling forces is the light intensity attraction of FFA algorithm that models the optimum solution. The light intensity attraction process of the FFA algorithm depends on random directions for search, this may delay in achieving the global optimization solution. The hybrid technique FFPSO, integrates the concepts from FF algorithm and PSO and creates new individuals. In the FFPSO method the local search is performed through the modified light intensity attraction step with PSO operator. The FFPSO features are compared with the classical FF, PSO features. The FFPSO feature values are given as the input to the Levenberg Marquardt Neural Network (LM NN) classifier. It has been observed that the performance of the classifier is improved with the help of the optimized features.

Punching shear capacity estimation of FRP-reinforced concrete slabs using a hybrid machine learning approach

Fibre-reinforced polymer (FRP) provides an alternative reinforcement for concrete flat slabs. This research proposes a hybrid machine learning model for predicting the ultimate punching shear capacity of FRP-reinforced slabs. The model employs the least squares support vector machine (LS-SVM) to discover the mapping between the influencing factors and the slab punching capacity. Furthermore, the firefly algorithm (FA), a population-based metaheuristic, is utilised to facilitate the LS-SVM training. A data-set which contains actual tests of FRP-reinforced concrete slabs is utilised to construct and verify the proposed approach. The contribution of this research is to establish a hybrid machine method, based on the LS-SVM and FA algorithms, for meliorating the prediction accuracy of FRP-reinforced slabs’ ultimate punching shear capacity. Experimental results demonstrate that the new model has achieved roughly 55 and 15% reductions in terms of Root Mean Squared Error compared with the formula-based and Artificial Neural Network methods, respectively.

Development of firefly algorithm via chaotic sequence and population diversity to enhance the image contrast

Nature-inspired algorithms have been applied in the optimization field including digital image processing like image enhancement or segmentation. Firefly algorithm (FA) is one of the most powerful of them. In this paper two different implementation of FA has been taken into consideration. One of them is FA via levy flight where step length of levy flight has been taken from chaotic sequence. Chaotic sequence shows ergodicity property which helps in better searching. But in the second implementation chaotic sequence replaces levy flight to enhance the capability of FA. Population of individuals has been created in every generation using the information of population diversity. As an affect FA does not converges prematurely. These two modified FA algorithms have been applied to optimize parameters of parameterized contrast stretching function. Entropy, contrast and energy of the image have been used as objective criterion for measuring goodness of image enhancement. Fitness criterion has been maximized in order to get enhanced image with better contrast. From the experimental results it has been shown that FA with chaotic sequence and population diversity information outperforms the Particle swarm optimization and FA via levy flight.