firefly algorithm

The unbalanced load distribution in the electrical distribution network caused crucial power losses. This condition occurs in one of the electrical distribution networks, 20 kV Tarahan Substation, Province of Bandar Lampung, Indonesia. This condition can be maintained using optimal reconfiguration with the integration of Distributed Generation (DG) based on Renewable Energy (RE). This study demonstrates the optimal reconfiguration of the 20 kV Tarahan Substation with the integration of the Photovoltaic (PV) and Battery Energy Storage System (BESS). The reconfiguration process is optimized by using the Firefly Algorithm (FA). This process is conducted in the 24-hour simulation with various load profiles. The optimal reconfiguration is investigated in two scenarios based on without and with DG integration. The optimal configuration with more balanced load distribution conducted by FA reduces the power losses by up to 31.39% and 32.38% in without and with DG integration, respectively. Besides that, the DG integration improves the lowest voltage bus in the electrical distribution network from 0.95 p.u to 0.97 p.u.

The financial time series data is a highly nonlinear signal and hence difficult to predict precisely. The prediction accuracy can be improved by linearizing the signal. In this paper the nonlinear data sample is linearized by decomposing it into several IMFs. A hybrid multi-layer decomposition technique is developed. The decomposition proposed in this paper is the combination of both EMD and VMD methods. As a new contribution to the previous literature in this study the VMD is used to further decompose the higher frequency signals obtained from the EMD based decomposed signal. In the result analysis it is observed that the double decomposition improves the prediction accuracy. This is a new introduction in the field of stock market prediction. The prediction accuracy of the proposed model is performed by applying it to three different stock markets for predicting the closing price. Historical data (closing price) is implemented to obtain 1 day ahead predicted closing price. Comparative analysis of different previously implemented methods like BPNN, SVM, ANN and ELM, along with the proposed method is performed. GA is implemented for optimizing the kernel factors. It is observed that the proposed hybrid model outperformed the other methods.

In the last decade, online shopping has played a vital role in customers' approach to purchasing different products, providing convenience to shop and many benefits for the economy. E-commerce is widely used for digital media products such as movies, images, and software. So, recommendation systems are of great importance, especially in today's hectic world, which search for content that would be interesting to an individual. In this research, a new two-steps recommender system is proposed based on demographic data and user ratings on the public MovieLens datasets. In the first step, clustering on the training dataset is performed based on demographic data, grouping customers in homogeneous clusters. The clustering includes a hybrid Firefly Algorithm (FA) and K-means approach. Due to the FA's ability to avoid trapping into local optima, which resolves K-means' main pitfall, the combination of these two techniques leads to much better performance. In the next step, for each cluster, two recommender systems are proposed based on K-Nearest Neighbor (KNN) and Naïve Bayesian Classification. The results are evaluated based on many internal and external measures like the Davies-Bouldin index, precision, accuracy, recall, and F-measure. The results showed the effectiveness of the K-means/FA/KNN compared with other extant models.

Power transformers play an important role in the transmission and distribution of electrical energy. Power transformers increase or decrease the voltage level without changing the power and frequency of alternating current (AC) electricity. Power transformers are divided into oil type and dry type transformers. Both two types have disadvantages of high cost and isolation problems etc. These problems are reduced by the optimization of transformer design parameters. In this study, the most optimal design dimension is determined by using the firefly algorithm, which is a new heuristic approach in calculating the volume of oil type power transformers at least time. At the same time, the performance comparison is made with the most preferred genetic algorithm, which is one of the other intuitive methods, and the advantages of the firefly algorithm are revealed. This work will provide both cost and time benefits for transformer manufacturers.

 Detection of attacks and anomalies is one of the new challenges in promoting e-commerce technologies. Detecting anomalies of a network and the process of detecting destructive activities in e-commerce can be executed by analyzing the behavior of network traffic. Data mining systems/techniques are used extensively in intrusion detection systems (IDS) in order to detect anomalies. Reducing the size/dimensions of features plays an important role in intrusion detection since detecting anomalies, which are features of network traffic with high dimensions, is a time-consuming process. Choosing suitable and accurate features influences the speed of the proposed task/work analysis, resulting in an improved speed of detection. The present papers utilize a neural network for deep learning to detect e-commerce attacks and anomalies of e-commerce systems. Overfitting is a common event in multi-layer neural networks. In this paper, features are reduced by the firefly algorithm (FA) to avoid this effect. Simulation results illustrate that a neural network system performs with high accuracy using feature reduction. Ultimately, the neural network structure is optimized by using particle swarm optimization (PSO) to increase the accuracy of attack detection capability.

To achieve high-quality software, different tasks such as testing should be performed. Testing is known as a complex and time-consuming task. Efficient test suite generation (TSG) methods are required to suggest the best data for test designers to obtain better coverage in terms of testing criteria. In recent years, researchers to generate test data in time-efficient ways have presented different types of methods. Evolutionary and swarm-based methods are among them. This work is aimed to study the applicability of swarm-based methods for efficient test data generation in EvoSuite. The Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Teaching Learning Based Optimization (TLBO), and Imperialist Competitive Algorithm (ICA) are used here. These methods are added to the EvoSuite. The methods are adapted to work in a discrete search space of test data generation problem. Also, a movement pattern is presented for generating new solutions. The performances of the presented methods are compared over 103 java classes with two built-in genetic-based methods in EvoSuite. The results show that swarm-based methods are successful in solving this problem and competitive results are obtained in comparison with the evolutionary methods.

This paper presents a method which is capable of satisfying the optimal protection coordination of relays in Microgrids (MGs) in both islanded and grid-connected modes. While the tripping times are minimized, the requirement of having multiple setting groups for relays is alleviated. Non-linear constrained programming is formulated in firefly algorithm (FA) and static penalties are considered for constraints handling. The goal is to obtain the optimal coordination between the directional overcurrent relays (OCRs). The formulation includes a framework to satisfy coordination constraints for both connectivity modes of MG operation and yield the least tripping times, while maintaining an appropriate time interval between the primary and the backup relays. A 9-bus IEEE test system is simulated as the MG in DIgSILENT software and the achieved results are compared with a similar study where the genetic algorithm has been applied for optimization. The comparative results verify the capability of the current method and its superiority.

In Software Defined Network (SDN), controller plane is separated from the data plane simplifying management. In these networks, data forwarding cannot be conducted just one controller. Therefore, it is needed to use multiple controllers in control plane. Since, switch-controller propagation delays and inter-controller latencies affect the performance, the problem of determining appropriate number of controllers as well as their suitable locations are two main challenges, which are known as NP-Hard. In this paper, a new clustering method based on K-means, K-Harmonics means and firefly algorithm named CPP-KKF is proposed for controller placement in SDN. Result obtained by CPP- KKF algorithm is benefitted by the advantages of all techniques. The proposed algorithm is evaluated on four topologies of TopologyZoo with different scales, that include Aarnet, Colt, Cognet, and DFN and the conducted simulations demonstrate that the proposed solution outperforms K-means, K-means++, Firefly and GSO algorithms in terms of aforementioned performance issues.

In this paper, a hybrid system based on wind turbines, solar arrays and fuel cells is designed optimally in the view of economical and technical aspects. The objective of the hybrid system optimization is to minimize the system net peresent cost(NPC) while considering the reliability as a constraint. The economical designing aspect is defined as equivalent loss factor (ELF) of reliability. The NPC consist of capital, operation and maintenance, replacement and, especially, loss of load costs. The data of load, solar radiation and definitive wind speed are from the North West of Iran. It is assumed that between the system components, i.e., wind turbine, photovoltaic array and inverter, there is a forced outage probability. The cuckoo optimization (COA) and firefly algorithms(FA) are applied to optimize the hybrid system components and the results are compared with the last studies. The results show that the COA method is superior to the FA and the last studies, with respect to the economical and technical aspects and convergence speed. They also show that complete consideration of the components availability and the availability of inverter increases the generation costs of the system, but improves the system reliability indices, too.

In this study, a new hybrid method based on Firefly Algorithm (FA) and Harmony Search (HS) techniques, is presented for solving the least-cost design problem of water distribution systems (WDS). This algorithm is designed to improve the performance of the FA as a recently developed meta-heuristic that mimics the natural behaviour of fireflies. The use of such a nature–inspired optimization method to solve the optimal design problem of WDS needs particular modifications to produce high quality solutions. Therefore, a modification is proposed to the movement stage of artificial fireflies and based on the HS strategy a memory is utilized to save a number of the best solutions. Another improvement in this algorithm contains the addition of pitch adjustment operation in the FA as a mutation operator.The presented method is applied to optimal design of some well-known benchmark problems taken from literature, and the results confirm its validity. In addition, a sensitivity analysis is performed on the parameters of the algorithm.

The Economic Load Dispatch (ELD) problems in power generation systems are to reduce the fuel cost by reducing the total cost for the generation of electric power. This paper presents an efficient Modified Firefly Algorithm (MFA), for solving ELD Problem. The main objective of the problems is to minimize the total fuel cost of the generating units having quadratic cost functions subjected to limits on generator true power output and transmission losses. The MFA is a stochastic, Meta heuristic approach based on the idealized behaviour of the flashing characteristics of fireflies. This paper presents an application of MFA to ELD for six generator test case system. MFA is applied to ELD problem and compared its solution quality and computation efficiency to Genetic algorithm (GA), Differential Evolution (DE), Particle swarm optimization (PSO), Artificial Bee Colony optimization (ABC), Biogeography-Based Optimization (BBO), Bacterial Foraging optimization (BFO), Firefly Algorithm (FA) techniques. The simulation result shows that the proposed algorithm outperforms previous optimization methods.

An accelerated firefly algorithm (AFA) for fast size optimization of truss structures is proposed in this paper. Metaheuristic firefly algorithm has been recently developed and its effectiveness in solving practical problems such as sizing optimization of truss structures has not been thoroughly explored. The numerical experiments show that although the standard firefly algorithm (FA) is a powerful approach for truss optimization, it suffers from slow rate of convergence, and hence it should be modified to solve real-life problems. The proposed AFA imposes some improvements on the searching procedure by both reduction of randomness and scaling the random term in fireflies'' motion. The effectiveness and robustness of the algorithm are investigated by solving some benchmark problems. The results revealed that the proposed AFA remarkably enhances the rate of convergence and stability of standard firefly algorithm.