Metaheuristic Firefly Algorithm Research Articles

Energy Efficient Data Aggregation in Wireless Sensor Networks Using Meta Heuristic Based Feed Forward Back Propagation Neural Network Approach

Sensor nodes are low-cost, low-power, tiny devices that make up the majority of WSNs, or distributed, self-organizing systems. These sensor nodes are able to exchange, perceive, and interpret data. The sensor nodes are equipped with a wide variety of sensors, such as chemical, touch, motion, temperature, and weather sensors. Because of its adaptability, sensors are used in a variety of applications such as automation, tracking, monitoring, and surveillance. Despite the enormous number of sensor applications, WSNs continue to suffer from common challenges like as low memory, slow processing speed, and short network lifetime. The feed forward back propagation neural network mode (FFBPNN) based on meta heuristics aims to create many paths for effective data aggregation in wireless sensor networks. This model handled the process of identifying and selecting the optimum route path. The distributed sensor nodes are utilized to create the various route paths. In this research paper, data aggregation is done using meta-heuristic firefly algorithm that helped in identifying an optimal route from among the found routes. After selecting the operative ideal route choice, the data aggregation procedure practices a rank-based approach to accomplish lower latency and a better packet delivery ratio(PDR). In addition to throughput, simulation was done to improve and measure performance in terms of packet delivery ratio, energy consumption, and end-to-end latency.

A unified framework of response surface methodology and coalescing of Firefly with random forest algorithm for enhancing nano-phytoremediation efficiency of chromium via in vitro regenerated aquatic macrophyte coontail (Ceratophyllum demersum L.)

Nano-phytoremediation is a novel green technique to remove toxic pollutants from the environment. In vitro regenerated Ceratophyllum demersum (L.) plants were exposed to different concentrations of chromium (Cr) and exposure times in the presence of titania nanoparticles (TiO2NPs). Response surface methodology was used for multiple statistical analyses like regression analysis and optimizing plots. The supplementation of NPs significantly impacted Cr in water and Cr removal (%), whereas NP × exposure time (T) statistically regulated all output parameters. The Firefly metaheuristic algorithm and the random forest (Firefly-RF) machine learning algorithms were coalesced to optimize hyperparameters, aiming to achieve the highest level of accuracy in predicted models. The R2 scores were recorded as 0.956 for Cr in water, 0.987 for Cr in the plant, 0.992 for bioconcentration factor (BCF), and 0.957 for Cr removal through the Firefly-RF model. The findings illustrated superior prediction performance from the random forest models when compared to the response surface methodology. The conclusion is drawn that metal-based nanoparticles (NPs) can effectively be utilized for nano-phytoremediation of heavy metals. This study has uncovered a promising outlook for the utilization of nanoparticles in nano-phytoremediation. This study is expected to pave the way for future research on the topic, facilitating further exploration of various nanoparticles and a thorough evaluation of their potential in aquatic ecosystems. Graphical

Application of optimization techniques for generating trajectories and adjusting the controller gains of a hydraulic servo-positioner using the Firefly metaheuristic algorithm

Application of optimization techniques for generating trajectories and adjusting the controller gains of a hydraulic servo-positioner using the Firefly metaheuristic algorithm

Predicting the Fracture Characteristics of Concrete Using Ensemble and Meta-heuristic Algorithms

Fracture properties are crucial for the design and application of concrete materials. The fracture energy which represents the fracture performance of concrete always suffers the influence of various factors, making it difficult to accurately predict the fracture energy of concrete with traditional methods. Thus, the artificial intelligence (AI) approaches are employed to establish the predictive models for the concrete fracture energy. Additionally, the firefly meta-heuristic algorithm (FA) is used to search for the optimum model hyper-parameters. The FA algorithm is proved to be efficient in tuning the hyper-parameters of the three models, and optimum values are obtained within ten iterations. Compared with the single classification and regression tree algorithm (CART), the ensemble algorithm appears to be more accurate and generalizable. Moreover, through the importance evaluation of different features in the optimum predictive model, the aggregate characteristics (aggregate size distribution and maximal aggregate size) and specimen size have been proven to be dominant factors for the predictive models, which should be carefully considered in predictive work regarding concrete fracture properties.

Hybrid neural network-based metaheuristics for prediction of financial markets: a case study on global gold market

Abstract Technical analysis indicators are popular tools in financial markets. These tools help investors to identify buy and sell signals with relatively large errors. The main goal of this study is to develop new practical methods to identify fake signals obtained from technical analysis indicators in the precious metals market. In this paper, we analyze these indicators in different ways based on the recorded signals for 10 months. The main novelty of this research is to propose hybrid neural network-based metaheuristic algorithms for analyzing them accurately while increasing the performance of the signals obtained from technical analysis indicators. We combine a convolutional neural network and a bidirectional gated recurrent unit whose hyperparameters are optimized using the firefly metaheuristic algorithm. To determine and select the most influential variables on the target variable, we use another successful recently developed metaheuristic, namely, the moth-flame optimization algorithm. Finally, we compare the performance of the proposed models with other state-of-the-art single and hybrid deep learning and machine learning methods from the literature. Finally, the main finding is that the proposed neural network-based metaheuristics can be useful as a decision support tool for investors to address and control the enormous uncertainties in the financial and precious metals markets.

A new robust hybrid model based on support vector machine and firefly meta-heuristic algorithm to predict pistachio yields and select effective soil variables

Pistachio production is an economically important crop that grows in arid environments. To predict yield and sustainably manage the use of natural resources such as soil and water, we modelled the effect of soil properties by classification and regression tree, k-nearest neighbors, support vector machines and developed a new hybrid model of support vector machines and the firefly meta-heuristic algorithm. We sampled soils from 124 pistachio orchards in Iran and analyzed them for a range of parameters. Available phosphorus and potassium, exchangeable sodium percentage, soil salinity, gypsum, calcium carbonate and gravel were selected as predictors in the subsequent model based on correlation coefficients, sensitivity analysis and ANOVA hypothesis testing. For modeling, the optimized values for the Kernel function parameters in the hybrid model of ζ, ε and γ were 8.76, 0.001 and 0.99, respectively, while the ideal numerical combinations for p and k parameters in the k-nearest neighbors model were 0.3 and 5, respectively. We checked the difference between the models using paired t-tests which showed that improvements were significant. According to the results, k-nearest neighbors, classification and regression tree and support vector machines algorithms could explain 83, 84 and 88% of the variation of pistachio yield, respectively, but improved to 94% in the hybrid model because it was more able to efficiently capture non-linear relationships. Soil available phosphorus was the most important determinant of pistachio yield, with soil salinity, exchangeable sodium percentage, potassium, gypsum, calcium carbonate and gravel ranked in order of decreasing importance. These outputs can help planners and farmers to better manage soil properties to increase pistachio yield and sustainable production.

A firefly metaheuristic algorithm for structural size and shape optimization with dynamic constraint

A firefly metaheuristic algorithm for structural size and shape optimization with dynamic constraint

Implementation of the Bio-Inspired Metaheuristic Firefly Algorithm (FA) Applied to Maximum Power Point Tracking of Photovoltaic Systems

In this paper, an algorithm for the maximum extraction of energy generated by photovoltaic (PV) systems was presented. The tracking of the global maximum point of the system is complex due to the non-linearity of the current-voltage (I-V) characteristic curve of the photovoltaic modules, as they vary according to the temperature and solar irradiation in the module. To obtain the best energy efficiency in these systems, it is important that the generation is delivering the maximum power available through the arrangement. In order to solve such problems, in this work an efficient MPPT-FA method was proposed, which showed good traceability when compared to traditional methods. Most traditional MPPT techniques are not able to find the global maximum point to extract the maximum power provided by the PV system. Finally, the Firefly Metaheuristic MPPT method proved to be robust against several partial shading scenarios. Simulations were presented to demonstrate the effectiveness of the proposal when compared to the traditional MPPT-PO method.

Tourist group itinerary design: When the firefly algorithm meets the n-person Battle of Sexes

The presented research focuses on generating tourist trip itineraries for a group with different individual preferences on various points of interest. A set of walking routes among selected points of interest is provided by the Prize-Collecting Vehicle Routing Problem solution, considering several imposed constraints. However, the members of a tourist group request to stay together during their trip, despite their different preferences. Thus, to accommodate preferential heterogeneity in group itinerary design, the n-person Prize-Collecting Vehicle Routing Problem is proposed in this research, along with a novel, combined game theory and metaheuristic approach to solve it. Notably, the n-person Battle of Sexes game is utilized to configure a priori the set of locations to be visited, which results in three distinctive sets, depending on whether their visit is prohibited, mandatory, or optional. Consecutively, the metaheuristic firefly algorithm is employed to determine the tourist routes, enhanced with the coordinates-related encoding/decoding process. This process enables the original algorithm to solve discrete optimization problems without altering or hybridizing the original algorithmic framework. Compared with other metaheuristic algorithms, the proposed approach generates efficient and satisfactory tourist trip itineraries for heterogeneous groups.

Effect of process parameters on sliding wear performance of red brick dust-filled glass–epoxy composites

This research is focused on the study of the tribological properties of epoxy composites reinforced with red brick dust and glass fiber. Wear tests are conducted on a pin-on-disk apparatus. The wear rate and coefficient of friction are measured after tribological tests. The relative effect of process parameters such as sliding velocity, normal load, filler content on specific wear rate, and coefficient of friction is also examined using the Taguchi model and analysis of variance technique. Furthermore, an innovative optimization approach is adopted by combining grey relational analysis with the metaheuristic firefly algorithm to obtain the desired response values. A nonlinear regression model is generated to cater to the relationship between the grey relational grade and the process parameters. This model is further employed in the firefly algorithm to move the firefly to the neighboring brighter and attractive firefly. The most influencing factors for wear rate and friction coefficient are sliding velocity and normal load, respectively. This work not only opens up an opportunity for value-added utilization of waste material such as red brick dust but also the proposed metaheuristic approach can be customized and applied for any multi-response optimization problem.

Meta-Heuristic Firefly Algorithm Based Optimal Design of Passive Harmonic Filter for Harmonic Mitigation

In present years, the increasing use of power electronic based nonlinear devices in various power system applications causes harmonic distortions in source side voltages and currents. The unwanted harmonic distortion can introduce detrimental effects on electrical equipment and temperature increases in neutral conductors and distribution transformers. In this paper, a new optimization method of Meta-heuristic firefly algorithm is proposed for the optimal design of passive harmonic filters to mitigate the harmonics existing in the power system environment. The performance of the proposed firefly algorithm based passive filter evidences better result compared with the other algorithm based methods.

Multi-Response Optimization of Magnetic Abrasive Flow Finishing Process on Aluminum (6061-T6) Using Utility Concept Embedded Firefly’s Algorithm

Surface finish is the most desired properties of any sophisticated machinery parts for its proper functioning and long endurance. The surface finish must be in the order of micrometer to nanometer for most of the machinery parts. The nontraditional surface finish processes prepare these parts; in these processes, the uses of electromagnet play a vital role in the surface finishing mechanism. Magnetic abrasive flow finishing (MAFF) is such a hybrid process, which gives a combined effect of abrasive flow finishing (AFF) and magnetic abrasive finishing (MAF). In this method, a pair of electromagnets are attached to the AFF setup. By using electromagnet in the AFF process, it enhanced material removal and surface finishing. The main process parameters selected in the MAFF process were magnetic flux density, number of cycles, percentage abrasive content, piston speed, and corresponding responses selected were material removal, percentage improvement in surface finish. In this research paper, the responses were optimized by a combination of utility theory and meta-heuristic firefly’s algorithm. The utility theory based-firefly algorithm’s predicted global optimum parameters set, which was more suitable for reducing the finishing time and required surface finish. The confirmatory test validated this optimized parameter set and it was revealed that the meta-heuristic firefly algorithm embedded with utility theory had given optimized results in the MAFF process.

Hybrid of Meta Heuristic Firefly and Genetic Algorithm for Optimization Approach in the Cloud Environment

Hybrid of Meta Heuristic Firefly and Genetic Algorithm for Optimization Approach in the Cloud Environment

A hybrid computing model to predict rock strength index properties using support vector regression

The uniaxial compressive strength (UCS) and elasticity modulus (E) are two of the most quoted rock strength parameters in engineering application. Due to approved technical difficulties indirect measurements, the tendency for determining these parameters through predictive models using simpler and cheaper tests in practical oriented applications have widely been highlighted. In this paper, a new hybridized multi-objective support vector regression (MSVR) model integrated with the firefly metaheuristic algorithm (FMA) was developed to touch upon a computational method in rock engineering purposes. The optimum internal parameters were adjusted through parametric investigation using 222 physical and mechanical rock properties corresponding to a variety of quarried stones from all over Iran. The accuracy and robustness of models were evaluated using different error indices, the area under curve for receiver operation characteristics (AUCROC) and F1-score criteria. Comparing to MSVR, the predictability level of UCS and E showed 8.35% and 5.47% improvement in hybrid MSVR-FMA. The superior and more promising results imply that hybrid MSVR-FMA as a flexible alternative can be applied for rock strength prediction in designing of construction projects. Using tow sensitivity analyses, the point load index and P-wave velocity were distinguished as the main effective factors on predicted UCS and E.

Optimization of feedback bits using firefly algorithm for interference reduction in LTE femtocell networks

Femtocells are the feasible solutions to extend the network coverage of indoor users and to enhance the network capacity in long-term evolution advanced (LTE-A)-based 5G networks. However, the femtocell base station shares the same frequency spectrum of microcell base station in unplanned manner. Hence, interference mitigation is a crucial problem in densely deployed femtocell environment and it is more severe with the deployment of femtocells in LTE-A network. In this paper, a modified dirty paper coding is proposed for interference mitigation along with the optimization of feedback bits using natural inspired meta-heuristic firefly algorithm. The proposed meta-heuristic algorithm reduces the interference by periodically unicasting the channel state information. Since the bandwidth of feedback system is limited, it is optimized in such a way that it does not affect the performance of the system. As compared to the conventional zero-forcing pre-coding, the proposed modified dirty paper coding along with firefly algorithm scheme offers improved sum rate of 70% and 64% with increase in the number of feedback bits and number of users, respectively.

A new redefined model of Firefly Algorithm with application to strategic bidding problem in Power Sector

Real-life optimization problems require an effective mechanism that completely utilizes the search space to obtain optimal solutions. A designer has always an opportunity to propose a new effective solution technique to address this issue. In this order, this paper presents a new upgraded redefined model of nature-inspired state of art meta-heuristic firefly algorithm (FA). FA is centered on swarm intelligence, which is motivated by the flashing pattern and behavior of fireflies. However, for a few instances, FA has a tendency to trap in local optima and it exhibits slow convergence. The proposed model is enabled through Time-varying Inertia Weight (TIW), Opposition Based Learning (OBL) and hybridized with sine cosine operators to get updated positions of search agents. A set of twenty-two classical benchmark function problems with numerous range and features are employed to prove the efficacy of the proposed model. Also, the effects of the above-mentioned modifications during the whole optimization routine are analyzed through different statistical and numerical analyses. The result analysis proves that the proposed modifications make FA more compatible. The proposed model is also tested on the strategic bidding problem of the power market of two different test systems with single and multi-trading hours. All the reported results confirm the supremacy of the proposed redefined model of FA.

Modelling and Parameters Extraction of Flexible Amorphous Silicon Solar Cell a-Si:H

The precise of solar cell model parameters being the prerequisite for realizing accurate photovoltaic models. Hence, the parameters identification techniques have attracted immense interest over the years among the researchers. This paper proposes a modelling and prediction of electrical intrinsic parameter extraction method of flexible hydrogenated amorphous silicon a-Si:H solar cell, based on the meta-heuristic firefly algorithm (FA). The characteristics of solar cells are non-linear, multivariable and multi-modal and difficult to identifies the electrical intrinsic parameters by conventional and analytical methods with high accuracy. Recently, the firefly algorithm has attracted the attention to optimize the non-linear and complex systems, based on the flashing patterns and behaviour of firefly’s swarm. Besides, the proposed constrained objective function is derived from the current–voltage curve. It is the absolute errors between the experimental and calculated current and voltage values. Furthermore, the obtained results of the proposed algorithm are compared with the results obtained by quasi-Newton method (Q-N) and self-organizing migrating algorithm (SOMA). Indeed, to validate the performance of the algorithm, the statistical analyses are carried out to measure the accuracy of the estimated parameters. In the end, the theoretical results of the firefly algorithm show an excellent agreement with experimental data and more accurate compared to other compared techniques.

Firefly-inspired stochastic resonance for spectrum sensing in CR-based IoT communications

The exponential increase in the number of the Internet of Things (IoT) necessitates dynamic and shared spectrum access at the edge of network. In cognitive radio (CR)-based IoT communications, spectrum sensing (SS) plays a pivotal role, if designed carefully, to enable a coexistence between licensed users (LUs) and unlicensed IoT devices for efficient and dynamic spectrum utilization. Though several SS techniques have been proposed in the literature, energy detection (ED) is renowned for its time and resource efficiency. Despite its suitability for IoT devices owing to its low hardware complexity and absence of a priori LU information, the detection performance of ED is poor at very low signal-to-noise ratio (SNR) channel conditions. While cooperative sensing can alleviate the performance problem of ED sensing in IoT network, significant detection cannot be achieved under adverse channel environments using non-cooperative IoT applications. Recently, stochastic resonance (SR) has been employed in CRs to boost the performance of SS in weak signal detection. In this paper, we propose a metaheuristic firefly algorithm (FFA) to determine the SR parameters through an objective function defined by the output SNR of a dynamic IoT system. In particular, we use an optimization scheme to optimally compute a noise level to achieve the best SR effect. Hence, the proposed FFA-based optimization problem significantly improves the sensing time and utilization of IoT communication channels in the weak heterogeneous IoT application introductions into the market. Our proposed system achieves a better detection probability of 80% compared to the 50% obtained through previous SR-based ED research works taking into account of SNR value of − 20 dB and a 10% false alarm probability ( $$Q_{\rm FA}$$ ). Moreover, for SNR value of − 20 dB, the sensing error probability of our proposed technique (20%) is 30% less than the previous SR-based ED considering $$Q_{\rm FA}$$ = 5%.

Workflow Scheduling in Cloud Environment Using Firefly Optimization Algorithm

One of the issues in cloud computing is workflow scheduling. A workflow models the process of executing an application comprising a set of steps and its objective is to simplify the complexity of application management. Workflow scheduling maps each task to a proper resource and sorts tasks on each resource to meet some efficiency measures such as processing and transmission costs, load balancing, quality of service, and etc. Task scheduling is an NP-Complete problem. In this study, meta-heuristic firefly algorithm (FA) is used to present a workflow scheduling algorithm. The purpose of the proposed scheduling algorithm is to explore optimal schedules such that the cost of processing and transmission of the whole workflow are minimized while there will be load balancing among the processing stations. The proposed algorithm is implemented in MATLAB and its efficiency is compared with cat swarm optimization (CSO) algorithm. The evaluations show that the proposed algorithm outperforms CSO in finding better solutions.

Hybrid Model for Movie Recommendation System Using Fireflies and Fuzzy C-Means

In the era of Big Data, extremely complicated data is delivered from the system, of which it is impossible to collect the correct information with an online platform. In this research work, it provides a hybrid model for a movie-based recommender system; based on meta-heuristic firefly algorithm and fuzzy c-means (FCM) clustering technique to evaluate rating of a movie for a specific user based on the similarity of users and historical data. The firefly algorithm was employed in the movie lens dataset to get the initial cluster and also to initialize the position of clusters. FCM is used to classify the similarity of the user ratings. The proposed collaborative recommender system performed well regarding accuracy and precision. Various metrics are used in a movie lens dataset like mean absolute error (MAE), precision, and recall. The experimental result delivered by the system provides more efficient performance compared to the existing system in term of mean absolute error (MAE).