Showing papers on "Firefly algorithm published in 2020"
TL;DR: It is shown that EFA-ANN provides a faster and more accurate prediction of HL and CL, and can assist civil engineers and construction managers in the early designs of energy-efficient buildings.
106 citations
TL;DR: A new hybrid algorithm is proposed that hybridizes the concept of dragon fly and firefly algorithm algorithms, termed fire fly replaced position update in dragonfly, to develop a new clustering model with optimal cluster head selection by considering four major criteria like energy, delay, distance, and security.
Abstract: Energy efficiency has become a primary issue in wireless sensor networks (WSN). The sensor networks are powered by battery and thus they turn out to be dead after a particular interval. Hence, enhancing the data dissipation in energy efficient manner remains to be more challenging for increasing the life span of sensor devices. It has been already proved that the clustering method could improve or enhance the life span of WSNs. In the clustering model, the selection of cluster head (CH) in each cluster regards as the capable method for energy efficient routing, which minimizes the transmission delay in WSN. However, the main problem dealt with the selection of optimal CH that makes the network service prompt. Till now, more research works have been processing on solving this issue by considering different constraints. Under this scenario, this paper attempts to develop a new clustering model with optimal cluster head selection by considering four major criteria like energy, delay, distance, and security. Further, for selecting the optimal CHs, this paper proposes a new hybrid algorithm that hybridizes the concept of dragon fly and firefly algorithm algorithms, termed fire fly replaced position update in dragonfly. Finally, the performance of the proposed work is carried out by comparing with other conventional models in terms of number of alive nodes, network energy, delay and risk probability.
105 citations
TL;DR: A Modified Binary GWO based on Stochastic Fractal Search (SFS) to identify the main features by achieving the exploration and exploitation balance and shows the superiority of the proposed algorithm compared to binary versions of the-state-of-the-art optimization techniques.
Abstract: Grey Wolf Optimizer (GWO) simulates the grey wolves’ nature in leadership and hunting manners. GWO showed a good performance in the literature as a meta-heuristic algorithm for feature selection problems, however, it shows low precision and slow convergence. This paper proposes a Modified Binary GWO (MbGWO) based on Stochastic Fractal Search (SFS) to identify the main features by achieving the exploration and exploitation balance. First, the modified GWO is developed by applying an exponential form for the number of iterations of the original GWO to increase the search space accordingly exploitation and the crossover/mutation operations to increase the diversity of the population to enhance exploitation capability. Then, the diffusion procedure of SFS is applied for the best solution of the modified GWO by using the Gaussian distribution method for random walk in a growth process. The continuous values of the proposed algorithm are then converted into binary values so that it can be used for the problem of feature selection. To ensure the stability and robustness of the proposed MbGWO-SFS algorithm, nineteen datasets from the UCI machine learning repository are tested. The K-Nearest Neighbor (KNN) is used for classification tasks to measure the quality of the selected subset of features. The results, compared to binary versions of the-state-of-the-art optimization techniques such as the original GWO, SFS, Particle Swarm Optimization (PSO), hybrid of PSO and GWO, Satin Bowerbird Optimizer (SBO), Whale Optimization Algorithm (WOA), Multiverse Optimization (MVO), Firefly Algorithm (FA), and Genetic Algorithm (GA), show the superiority of the proposed algorithm. The statistical analysis by Wilcoxon’s rank-sum test is done at the 0.05 significance level to verify that the proposed algorithm can work significantly better than its competitors in a statistical way.
97 citations
TL;DR: An adaptive logarithmic spiral-Levy FA (AD-IFA) that strengthens the firefly algorithm's local exploitation and accelerates its convergence and consistently outperforms the standard FA and LF-FA for 29 test functions and 6 real cases of global optimization problems in terms of both computation speed and derived optimum.
Abstract: Global continuous optimization is populated by its implementation in many real-world applications. Such optimization problems are often solved by nature-inspired and meta-heuristic algorithms, including the firefly algorithm (FA), which offers fast exploration and exploitation. To further strengthen FA's search for global optimum, a Levy-flight FA (LF-FA) has been developed through sampling from a Levy distribution instead of the traditional uniform one. However, due to its poor exploitation in local areas, the LF-FA does not guarantee fast convergence. To address this problem, this paper provides an adaptive logarithmic spiral-Levy FA (AD-IFA) that strengthens the LF-FA's local exploitation and accelerates its convergence. Our AD-IFA is integrated with logarithmic-spiral guidance to its fireflies’ paths, and adaptive switching between exploration and exploitation modes during the search process. Experimental results show that the AD-IFA presented in this paper consistently outperforms the standard FA and LF-FA for 29 test functions and 6 real cases of global optimization problems in terms of both computation speed and derived optimum.
95 citations
TL;DR: A new firefly algorithm based feature selection method is proposed to deal with Arabic Text Classification which is not intensively studied due to the complexity of Arabic language and achieves a precision value equals to 0.994.
90 citations
TL;DR: The results indicated that the proposed FFA-ANN model was the most dominant model in comparison with other models (i.e., CART, SVM, KNN), and demonstrated that the FFA has a vital role in optimizing the ANN model in predicting blast-induced ground vibration.
Abstract: The primary purpose of this study was to develop a novel hybrid artificial intelligence model, with a robust performance, to predict ground vibration induced by bench blasting. An artificial neural network (ANN) was combined with the firefly algorithm (FFA), abbreviated as an FFA-ANN model, for this objective. To develop the FFA-ANN model, an ANN model (i.e., ANN 5-16-20-1) was established first; its weights and biases were then optimized by the FFA. A classification and regression tree (CART), a k-nearest neighbor (KNN), and a support vector machine (SVM) were also developed to confirm the power of the proposed FFA-ANN model. Eighty-three blasting events at a quarry mine in Vietnam were investigated to assess the danger of ground vibration through the developed models. The quality of the developed models was assessed through root-mean-squared error, mean absolute error, coefficient of correlation (R2), and variance account for. A simple ranking method and color gradient technique were also applied to evaluate the performance of the models. The results of this study indicated that the proposed FFA-ANN model was the most dominant model in comparison with other models (i.e., CART, SVM, KNN). The results also demonstrated that the FFA has a vital role in optimizing the ANN model in predicting blast-induced ground vibration.
87 citations
TL;DR: A quantum behaved particle swarm algorithm has been used for inverse kinematic solution of a 7-degree-of-freedom serial manipulator and the results have been compared with other swarm techniques such as firefly algorithm, particle swarm optimization (PSO) and artificial bee colony (ABC).
Abstract: In this study, a quantum behaved particle swarm algorithm has used for inverse kinematic solution of a 7-degree-of-freedom serial manipulator and the results have been compared with other swarm techniques such as firefly algorithm (FA), particle swarm optimization (PSO) and artificial bee colony (ABC). Firstly, the DH parameters of the robot manipulator are created and transformation matrices are revealed. Afterward, the position equations are derived from these matrices. The position of the end effector of the robotic manipulator in the work space is estimated using Quantum PSO and other swarm algorithms. For this reason, a fitness function which name is Euclidian has been determined. This function calculates the difference between the actual position and the estimated position of the manipulator end effector. In this study, the algorithms have tested with two different scenarios. In the first scenario, values for a single position were obtained while values for a hundred different positions were obtained in the second scenario. In fact, the second scenario confirms the quality of the QPSO in the inverse kinematic solution by verifying the first scenario. According to the results obtained; Quantum behaved PSO has yielded results that are much more efficient than standard PSO, ABC and FA. The advantages of the improved algorithm are the short computation time, fewer iterations and the number of particles.
81 citations
TL;DR: A Hybrid approach of Firefly Algorithm with Particle Swarm Optimization (HFAPSO) is proposed for finding the optimal cluster head selection in the LEACH-C algorithm and it has been found that the proposed methodology has achieved better throughput and residual energy.
Abstract: Wireless Sensor Networks (WSN) are operated on battery source, and the sensor nodes are used for collecting the information from the environment and transmitting the same to the base station. The sensor nodes consume more energy for the process of data communication and also affect the network lifetime. Energy efficiency is one of the important features for designing the sensor networks. Clustering technique is mainly used to perform the energy-efficient data transmission that consumes the minimum energy and also prolongs the lifetime of the network. In this paper, a Hybrid approach of Firefly Algorithm with Particle Swarm Optimization (HFAPSO) is proposed for finding the optimal cluster head selection in the LEACH-C algorithm. The hybrid algorithm improves the global search behavior of fireflies by using PSO and achieves optimal positioning of the cluster heads. The performance of the proposed methodology is evaluated by using the number of alive nodes, residual energy and throughput. The results show the improvement in network lifetime, thus increasing the alive nodes and reducing the energy utilization. While making a comparison with the firefly algorithm, it has been found that the proposed methodology has achieved better throughput and residual energy.
81 citations
TL;DR: Experimental results confirmed that the proposed FFA algorithm offers high accuracy and efficiency with rapid tracking speed and the proposed SPP process is capable of significantly reducing the sampling events not only when integrating it with the FFA, but also with other conventional FA algorithms.
Abstract: An improved maximum power point tracking (MPPT) algorithm based on the fusion firefly algorithm (FFA) with a novel simplified propagation process (SPP) for photovoltaic (PV) systems under partial shading conditions (PSCs) is proposed in this study. By integrating the neighborhood attraction firefly algorithm (NaFA) and simplified firefly algorithm (SFA), the proposed FFA is capable of tracking the global maximum power points (GMPPs) with high accuracy. In addition, the proposed SPP process reduces the sampling events by omitting redundant propagations, thereby accelerating the tracking speed and reducing the energy loss and oscillations during the sampling process. The performance of the proposed FFA and the speed improvement using the SPP process were simulated using MATLAB software and verified with a hardware evaluation system. Experimental results confirmed that the proposed FFA algorithm offers high accuracy and efficiency with rapid tracking speed. In addition, the proposed SPP process is capable of significantly reducing the sampling events not only when integrating it with the FFA, but also with other conventional FA algorithms.
79 citations
TL;DR: A Yin-Yang firefly algorithm (YYFA) based on dimensionally Cauchy mutation is proposed for performance improvement of FA and demonstrates that YYFA provides highly competitive performance in terms of the tested algorithms.
Abstract: Firefly algorithm (FA) is a classical and efficient swarm intelligence optimization method and has a natural capability to address multimodal optimization. However, it suffers from premature convergence and low stability in the solution quality. In this paper, a Yin-Yang firefly algorithm (YYFA) based on dimensionally Cauchy mutation is proposed for performance improvement of FA. An initial position of fireflies is specified by the good nodes set (GNS) strategy to ensure the spatial representativeness of the firefly population. A designed random attraction model is then used in the proposed work to reduce the time complexity of the algorithm. Besides, a key self-learning procedure on the brightest firefly is undertaken to strike a balance between exploration and exploitation. The performance of the proposed algorithm is verified by a set of CEC 2013 benchmark functions used for the single objective real parameter algorithm competition. Experimental results are compared with those of other the state-of-the-art variants of FA. Nonparametric statistical tests on the results demonstrate that YYFA provides highly competitive performance in terms of the tested algorithms. In addition, the application in constrained engineering optimization problems shows the practicability of YYFA algorithm.
78 citations
TL;DR: Improved versions of the tree growth and firefly algorithms that improve the original implementations are proposed that establish higher performance than the other existing techniques in terms of classification accuracy and the use of computational resources.
Abstract: Computer vision is one of the most frontier technologies in computer science. It is used to build artificial systems to extract valuable information from images and has a broad range of applications in various areas such as agriculture, business, and healthcare. Convolutional neural networks represent the key algorithms in computer vision, and in recent years, they have attained notable advances in many real-world problems. The accuracy of the network for a particular task profoundly relies on the hyperparameters’ configuration. Obtaining the right set of hyperparameters is a time-consuming process and requires expertise. To approach this concern, we propose an automatic method for hyperparameters’ optimization and structure design by implementing enhanced metaheuristic algorithms. The aim of this paper is twofold. First, we propose enhanced versions of the tree growth and firefly algorithms that improve the original implementations. Second, we adopt the proposed enhanced algorithms for hyperparameters’ optimization. First, the modified metaheuristics are evaluated on standard unconstrained benchmark functions and compared to the original algorithms. Afterward, the improved algorithms are employed for the network design. The experiments are carried out on the famous image classification benchmark dataset, the MNIST dataset, and comparative analysis with other outstanding approaches that were tested on the same problem is conducted. The experimental results show that both proposed improved methods establish higher performance than the other existing techniques in terms of classification accuracy and the use of computational resources.
15 Jun 2020
TL;DR: An improved version of the firefly algorithm has been applied to improve the network lifetime maximization and conducted simulations have proven that the proposed metaheuristic achieves better and more consistent performance than other algorithms.
Abstract: We have recently witnessed the rapid development of several emerging technologies, including the internet of things, which lead to a high interest in wireless sensor networks. Tiny sensor nodes are now important parts of a large number of complex systems, with numerous applications including military, environment monitoring, surveillance and body area sensor networks. One of the biggest challenges each wireless sensor network has to handle is the network lifetime maximization. To achieve this, numerous clustering algorithms have been created, with the goal to improve energy consumption throughout the network by balancing the energy consumption overall nodes. All clustering algorithms incorporate load balancing to achieve energy efficiency. One of the basic and most important algorithms in use is LEACH. Swarm intelligence metaheuristics have already been applied in solving numerous problems of wireless sensor networks, including lifetime optimization, localization and many other NP hard problems with promising results, as can be seen in the literature overview. In the research proposed in this paper, an improved version of the firefly algorithm has been applied to improve the network lifetime. The firefly algorithm was used to help in forming the clusters and selection of the cluster head. Additionally, we have evaluated the performance of the improved firefly algorithm by comparing it to the LEACH, basic firefly algorithm and particle swarm optimization, that were all tested on the same network infrastructure model. Conducted simulations have proven that our proposed metaheuristic achieves better and more consistent performance than other algorithms.
TL;DR: The firefly algorithm conveys a leading task for the SLMLI topology for solar-photovoltaic applications and generates low distortion output and consumes the harmonic band of the fast Fourier transform framework by the employment of the proposed algorithm.
Abstract: The super-lift technique is an exceptional contribution to DC-DC conversion technology. A replacement approach of symmetrical super-lift multilevel inverter (SLMLI) DC/AC technology is proposed with a reduced number of elements compared with the traditional multilevel inverter. In this method, the firefly algorithm conveys a leading task for the SLMLI topology for solar-photovoltaic applications. It generates low distortion output and consumes the harmonic band of the fast Fourier transform framework by the employment of the proposed algorithm. The simulation circuit for 15 levels output uses single switch super-lift inverter feed with different kinds of load (R, RL and RLE) conditions. The power quality is improved in SLMLI with minimised harmonics underneath the various modulation indices while varied from 0.1 up to 0.8. The circuit is designed in a field-programmable gate array, which includes the firefly rule to help the multilevel output, to reduce the lower order harmonics and to find the best switching angle. As a result, the minimum total harmonic distortion from the simulation and hardware circuit is achieved. Due to the absence of bulky switches, inductor and filter elements expose the effectiveness of the proposed system.
TL;DR: In this article, a Chaotic Sine Cosine Firefly (CSCF) algorithm with numerous variants is proposed to solve optimization problems, where the chaotic form of two algorithms namely the sine cosine algorithm (SCA) and the Firefly (FF) algorithms are integrated to improve the convergence speed and efficiency.
Abstract: Recently, numerous meta-heuristic based approaches are deliberated to reduce the computational complexities of several existing approaches that include tricky derivations, very large memory space requirement, initial value sensitivity etc. However, several optimization algorithms namely firefly algorithm, sine cosine algorithm, particle swarm optimization algorithm have few drawbacks such as computational complexity, convergence speed etc. So to overcome such shortcomings, this paper aims in developing a novel Chaotic Sine Cosine Firefly (CSCF) algorithm with numerous variants to solve optimization problems. Here, the chaotic form of two algorithms namely the sine cosine algorithm (SCA) and the Firefly (FF) algorithms are integrated to improve the convergence speed and efficiency thus minimizing several complexity issues. Moreover, the proposed CSCF approach is operated under various chaotic phases and the optimal chaotic variants containing the best chaotic mapping is selected. Then numerous chaotic benchmark functions are utilized to examine the system performance of the CSCF algorithm. Finally, the simulation results for the problems based on engineering design are demonstrated to prove the efficiency, robustness and effectiveness of the proposed algorithm.
TL;DR: The data presented in this data article is related to the research article entitles ‘Operational Framework for Recent Advances in Backtracking Search Optimisation Algorithm: A Systematic Review and Performance Evaluation’.
TL;DR: This study proposes a hybrid intelligent system based on artificial intelligence (AI) and metaheuristic algorithms for designing optimal mixtures of RAC and shows that kNN-based semi-supervised cotraining can effectively exploit unlabeled data to improve the regression estimates.
TL;DR: HLPSO combines diverse search mechanisms including modified Firefly Algorithm operations, a new spiral research action, probability distributions, crossover, and mutation procedures to diversify and improve the original PSO algorithm, and is used in conjunction with the K-Means clustering algorithm to enhance lesion segmentation.
Abstract: In this research, we propose a variant of the Particle Swarm Optimization (PSO) algorithm, namely hybrid learning PSO (HLPSO), for skin lesion segmentation and classification. HLPSO combines diverse search mechanisms including modified Firefly Algorithm (FA) operations, a new spiral research action, probability distributions, crossover, and mutation procedures to diversify and improve the original PSO algorithm. It is used in conjunction with the K-Means clustering algorithm to enhance lesion segmentation. Its cost function takes both intra-class and inter-class variations into account to increase scalability. Two lesion classification systems are formulated based on HLPSO. In the first system, HLPSO is used to devise evolving convolutional neural networks (CNN) with optimized topologies and hyper-parameters for lesion classification. In the second system, shape and colour features, as well as texture features extracted using the Kirsch operator and Shift Local Binary Patterns are used to produce an initial discriminative lesion representation. HLPSO is then used to identify the most significant components of each feature vector for ensemble lesion classification. Evaluated using several skin lesion data sets, both systems depict superior capabilities in lesion segmentation, deep CNN architecture generation, and discriminative feature selection for ensemble lesion classification, and outperform a number of advanced PSO and FA variants, classical search methods, as well as other related models on skin lesion classification significantly. HLPSO also yields better performances over other classical and advanced search methods in solving a number of benchmark tasks related to mathematical landscapes and those in the complex CEC 2014 test suite.
TL;DR: This survey covers SWEVO-based IDS approaches such as Genetic Algorithm, Ant Colony Optimization, Particle Swarmoptimization, and Flower Pollination Algorithm along with challenges and possible future directions.
Abstract: The growth of data and categories of attacks, demand the use of Intrusion Detection System(IDS) effectively using Machine Learning(ML) and Deep Learning(DL) techniques. Apart from the ML and DL techniques, Swarm and Evolutionary (SWEVO) Algorithms have also shown significant performance to improve the efficiency of the IDS models. This survey covers SWEVO-based IDS approaches such as Genetic Algorithm(GA), Ant Colony Optimization(ACO), Particle Swarm Optimization(PSO), Artificial Bee Colony Optimization(ABC), Firefly Algorithm(FA), Bat Algorithm(BA), and Flower Pollination Algorithm(FPA). The paper also discusses applications of the SWEVO in the field of IDS along with challenges and possible future directions.
TL;DR: The motivation of this research is proposing an intelligent meta-heuristic algorithm based on the combination of ICA and FA to get the mentioned required result and the obtained result showed dramatic improvements in makespan, CPU time, load balancing, stability and planning speed.
Abstract: Cloud computing is an Internet-based approach in which all applications and files are hosted in a cloud consisting of thousands of computers that are linked in complex ways. The major challenge of cloud data centers is to show how the millions of requests of final users are correctly and effectively being investigated and serviced. Load-balancing techniques are needed to increase the flexibility and scalability of cloud data centers. Load-balancing technique is one of the most significant issues in the distributed computing system. Since there are large-scale resources and a lot of user demands in cloud computing load-balancing problem, it could be the main reason that many researchers considered and addressed that as an NP-hard problem. Therefore, some heuristics algorithms such as imperialist competitive algorithm (ICA) and firefly algorithm (FA) had been proposed by previous researchers to solve the mentioned problem. Although ICA and FA could get an approximate satisfying result in solving the cloud computing load-balancing problem, obtaining the better result means to make improvements in makespan, CPU time, load balancing, stability and planning speed. The motivation of this research is proposing an intelligent meta-heuristic algorithm based on the combination of ICA and FA to get the mentioned required result. Local search ability of FA can reinforce ICA algorithm. The obtained result of this research showed dramatic improvements in makespan, CPU time, load balancing, stability and planning speed.
TL;DR: The proposed algorithm has been named as enhanced moth flame optimization (E-MFO) and to validate the applicability of the algorithm, it has been applied to twenty benchmark functions and experimental analysis shows the superior performance of E-M FO over other algorithms in terms of convergence rate and solution quality.
Abstract: Moth flame optimization (MFO) is a recent nature-inspired algorithm, motivated from the transverse orientation of moths in nature. The transverse orientation is a special kind of navigation method, which demonstrates the movement of moths toward moon in a straight path. This algorithm has been successfully applied on various optimization problems. But, MFO suffers from the problem of poor exploration. So, in order to enhance the performance of MFO, some modifications are proposed. A Cauchy distribution function is added to enhance the exploration capability, influence of best flame has been added to improve the exploitation and adaptive step size and division of iterations is followed to maintain a balance between the exploration and exploitation. The proposed algorithm has been named as enhanced moth flame optimization (E-MFO) and to validate the applicability of E-MFO, and it has been applied to twenty benchmark functions. Also, comprehensive comparison of E-MFO with other meta-heuristic algorithms like bat algorithm, bat flower pollination, differential evolution, firefly algorithm, genetic algorithm, particle swarm optimization and flower pollination algorithm has been done. Further, the effect of population and dimension size on the performance of MFO and E-MFO has been discussed. The experimental analysis shows the superior performance of E-MFO over other algorithms in terms of convergence rate and solution quality. Also, statistical testing of E-MFO has been done to prove its significance.
TL;DR: Experimental results indicate that the FA and OBL can significantly boost the core exploratory and exploitative trends of GWO in dealing with the optimization tasks.
Abstract: Understanding the green consumption behaviors of college students is highly demanded to update the public and educational policies of universities. For this purpose, this research is devoted to advance an efficient model for identifying prominent features and predicting the green consumption behaviors of college students. The proposed prediction model is based on the K-Nearest Neighbor (KNN) with an effective swarm intelligence method, which is called OBLFA_GWO. The optimization core takes advantage of the firefly algorithm (FA) and opposition-based learning (OBL) to mitigate the immature convergence of the grey wolf algorithm (GWO). In the proposed prediction framework, OBLFA_GWO is utilized to identify influential features. Then, the enhanced KNN model is used to identify the importance and interrelationships of features in samples and construct an effective and stable predictive model for decision support. Five other well-known algorithms are employed to validate the effectiveness of the proposed OBLFA_GWO strategy using 13 benchmark test problems. Also, the non-parametric statistical Wilcoxon sign rank and Friedman tests are conducted to validate the significance of the proposed OBLFA_GWO against other peers. Experimental results indicate that the FA and OBL can significantly boost the core exploratory and exploitative trends of GWO in dealing with the optimization tasks. Also, the OBLFA_GWO-based KNN (OBLFA_GWO-KNN) model is compared with four classical classifiers, such as kernel extreme learning machine (KELM), backpropagation neural network method (BPNN), and random forest (RF) and five advanced feature selection methods in terms of four standard evaluation indexes. The experimental results show that the classification accuracy of the proposed OBLFA_GWO-KNN can reach to 96.334 % on the real-life dataset collected from nine universities. Also, the proposed binary OBLFA_GWO algorithm has improved the classification performance of KNN compared to the other peers. Hopefully, the established adaptive OBLFA_GWO-KNN model can be considered as a useful tool for predicting students’ behavior of green consumption.
TL;DR: An efficient krill herd (EKH) algorithm is proposed to search optimal thresholding values at different level for color images and Kapur’s entropy is found to be more accurate and robust for color image multilevel thresholding segmentation.
TL;DR: In this paper, a hybrid approach is proposed based on mobile sink, firefly algorithm based on leach, and Hopfield neural network (WSN-FAHN), which is applied to both improve energy consumption and increase network lifetime.
Abstract: Wireless sensor networks (WSNs) contain numerous nodes that their main goals are to monitor and control environments. Also, sensor nodes distribute based on network usage. One of the most significant issues in this type of network is the energy consumption of sensor nodes. In fixed-sink networks, nodes which are near the sink act as an interface to transfer data of other nodes to sink. This causes the energy consumption of sensors reduces rapidly. Therefore, the lifetime of the network declines. Sensor nodes owing to their weaknesses are susceptible to several threats, one of which is denial-of-sleep attack (DoSA) threatening WSN. Hence, the DoSA refers to the energy loss in these nodes by maintaining the nodes from entering energy-saving and sleep mode. In this paper, a hybrid approach is proposed based on mobile sink, firefly algorithm based on leach, and Hopfield neural network (WSN-FAHN). Thus, mobile sink is applied to both improve energy consumption and increase network lifetime. Firefly algorithm is proposed to cluster nodes and authenticate in two levels to prevent from DoSA. In addition, Hopfield neural network detects the direction route of the sink movement to send data of CH. Furthermore, here WSN-FAHN technique is assessed through wide simulations performed in the NS-2 environment. The WSN-FAHN procedure superiority is demonstrated by simulation outcomes in comparison with contemporary schemes based on performance metrics like packet delivery ratio (PDR), average throughput, detection ratio, and network lifetime while decreasing the average residual energy.
TL;DR: The obtained R 2 and RMSE values show that FS-FA model has high prediction level in the modeling of blast-induced AOp, which clearly demonstrate the merits of the proposed FS- FA model.
Abstract: Air overpressure (AOp) produced by blasting is one of the environmental hazards of mining operations. Accordingly, the accurate prediction of AOp is very important, and this issue requires the application of appropriate prediction models. With this in view, this paper aims to propose a new data-driven model in the prediction of AOp using a hybrid model of fuzzy system (FS) and firefly algorithm (FA). This combination is abbreviated as FS-FA model. The used data-sets in the proposed FS-FA model were arranged in a format of three input parameters. In total, 86 sets of the mentioned parameters were prepared. To avoid over-fitting, the data-sets were divided into two parts of training (80%) and test sets (20%). Three quantitative standard statistical performance evaluation measures, variance account for (VAF), coefficient correlation (R2) and root mean squared error (RMSE), were used to check the accuracy of the FS-FA model. According to the results, the R2 and RMSE values obtained from the proposed FS-FA model were equal to 0.977 and 1.241 (for testing phase), respectively, which clearly demonstrate the merits of the proposed FS-FA model. In other words, the obtained R2 and RMSE show that FS-FA model has high prediction level in the modeling of blast-induced AOp.
TL;DR: The obtained results indicated that the proposed method is highly efficient in multilevel image thresholding in terms of objective function value, peak signal to noise, structural similarity index, feature similarityindex, and the curse of dimensionality.
19 Jul 2020
TL;DR: A new adaptive Lévy flight mutation operator is introduced here and called the levy/1/bin, which propitiates the emergence of these two mutation approaches at different rates and times in COLSHADE.
Abstract: In this paper we present the COLSHADE algorithm for real parameter constrained optimization problems. COLSHADE evolved from the basic L-SHADE algorithm by introducing significant features such as adaptive Levy flights and dynamic tolerance (included in the constraint handling technique). Levy flights mainly perform the exploration phase in algorithms such as the Firefly algorithm and Cuckoo search; in COLSHADE, however, the goal of the Levy flights is to administer the selection pressure exerted over the population as to find the feasible region and keeping diversity. Thus, a new adaptive Levy flight mutation operator is introduced here and called the levy/1/bin. In many problems the levy/1/bin excels during the exploration phase whilst the exploitation phase is performed by current-to-pbest mutation. However, the adaptive strategy propitiates the emergence of these two mutation approaches at different rates and times. The proposed method is tested on 57 constrained optimization functions of the benchmark provided for the CEC 2020 realworld single-objective constrained optimization competition.
TL;DR: A new methodology has been proposed for optimal allocation and optimal sizing of a lithium-ion battery energy storage system (BESS) and the results showed that using two BESS can reduce the total error of the distribution system.
Abstract: In this study, a new methodology has been proposed for optimal allocation and optimal sizing of a lithium-ion battery energy storage system (BESS). The main purpose is to minimize the total loss reduction in the distribution system. The optimization process is applied using a newly developed type of Cayote Optimization Algorithm (COA). The proposed technique includes two different approaches. In the first approach, the optimization for allocation and the sizing are performed one by one and in the second approach, the optimization has been done simultaneously. To analyze the proposed system, four different scenarios have been analyzed which include different conditions without/with PVs and also using single/two BESS. The results showed that using two BESS can reduce the total error of the distribution system. the results also showed that using PVs can also decrease the total losses. Finally, the proposed approach based on ICOA is compared with Firefly Algorithm (FA), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO) to show the proposed method's prominence efficiency.
TL;DR: A new hybrid machine learning algorithm that incorporates the adaptive neuro-fuzzy inference system model with a new version of the fireflies algorithm denominated as the gender-difference firefly algorithm is proposed, demonstrating the robustness and the accuracy of the proposed algorithm when compared to the traditional adaptive Neuro-f fuzzy inference system models and also to the different predictive techniques implemented in several pieces of literature.
TL;DR: Simulation results show that the introduction of Levy flight and improved sine cosine operator in LSC-SSA significantly improves optimization accuracy and convergence speed compared with other swarm optimization algorithms and can effectively solve high-dimensional large-scale optimization problems.
Abstract: The salp swarm algorithm (SSA) is a swarm intelligence optimization algorithm that simulates the chain movement behavior of salp populations in the sea. Aiming at the shortcomings of the SSA, such as low precision, low optimization dimension and slow convergence speed, an improved salp swarm algorithm based on Levy flight and sine cosine operator (LSC-SSA) was proposed. The Levy flight mechanism uses the route of short walks combined with long jumps to search the solution space, which can effectively improve the global exploration capability of the algorithm. Improved sine cosine operator use sine search for global exploration and cosine search for local exploitation. At the same time, an adaptively switching between the two function search methods can achieve a smooth transition between global exploration and local exploitation. In the simulation experiment, salp swarm algorithm (SSA), whale optimization algorithm (WOA), particle swarm algorithm (PSO), sine cosine algorithm (SCA), firefly algorithm (FA) and LSC-SSA were adopted for solving function optimization problems. Then, the feasibility of the improved algorithm for solving high-dimensional large-scale optimization problems and the effectiveness of the improvement strategy are evaluated. Finally, LSC-SSA was applied to train muti-layer perceptron neural network. Simulation results show that the introduction of Levy flight and improved sine cosine operator in LSC-SSA significantly improves optimization accuracy and convergence speed compared with other swarm optimization algorithms. In addition, the improved algorithm can effectively solve high-dimensional large-scale optimization problems. In the application of training muti-layer perceptron NN, the improved algorithm can avoid falling into the local optimal value and obtain the ideal classification accuracy.
TL;DR: The modified firefly algorithm, an in-house optimization tool, is employed to design the adaptive facade system, which can adapt its thermal and visible transmittance for dynamically varying climatic conditions and can reduce the energy consumption by 14.9–29.3% compared to the static facades.