Showing papers on "Firefly algorithm published in 2018"

Multi-level image thresholding using Otsu and chaotic bat algorithm

Abstract: Multi-level thresholding is a helpful tool for several image segmentation applications Evaluating the optimal thresholds can be applied using a widely adopted extensive scheme called Otsu's thresholding In the current work, bi-level and multi-level threshold procedures are proposed based on their histogram using Otsu's between-class variance and a novel chaotic bat algorithm (CBA) Maximization of between-class variance function in Otsu technique is used as the objective function to obtain the optimum thresholds for the considered grayscale images The proposed procedure is applied on a standard test images set of sizes (512 × 512) and (481 × 321) Further, the proposed approach performance is compared with heuristic procedures, such as particle swarm optimization, bacterial foraging optimization, firefly algorithm and bat algorithm The evaluation assessment between the proposed and existing algorithms is conceded using evaluation metrics, namely root-mean-square error, peak signal to noise ratio, structural similarity index, objective function, and CPU time/iteration number of the optimization-based search The results established that the proposed CBA provided better outcome for maximum number cases compared to its alternatives Therefore, it can be applied in complex image processing such as automatic target recognition

Load frequency controller design of a two-area system composing of PV grid and thermal generator via firefly algorithm

Abstract: In this paper, firefly algorithm (FA) for optimal tuning of PI controllers for load frequency control of hybrid system composing of photovoltaic (PV) system and thermal generator is introduced. Also, maximum power point tracking of PV is considered in the design process. The block diagram of the hybrid system is performed. To robustly tune the parameters of controllers, a time-domain-based objective function is established which is solved by the FA. Simulation results are presented to show the improved performance of the suggested FA-based controllers compared with genetic algorithm (GA). These results show that the proposed controllers present better performance over GA in terms of settling times and different indices.

Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors

Abstract: The factors affecting the shear strength of the angle shear connectors in the steel-concrete composite beams can play an important role to estimate the efficacy of a composite beam. Therefore, the current study has aimed to verify the output of shear capacity of angle shear connector according to the input provided by Support Vector Machine (SVM) coupled with Firefly Algorithm (FFA). SVM parameters have been optimized through the use of FFA, while genetic programming (GP) and artificial neural networks (ANN) have been applied to estimate and predict the SVM-FFA models\' results. Following these results, GP and ANN have been applied to develop the prediction accuracy and generalization capability of SVM-FFA. Therefore, SVM-FFA could be performed as a novel model with predictive strategy in the shear capacity estimation of angle shear connectors. According to the results, the Firefly algorithm has produced a generalized performance and be learnt faster than the conventional learning algorithms.

Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization

Abstract: . Groundwater is one of the most valuable natural resources in the world (Jha et al., 2007). However, it is not an unlimited resource; therefore understanding groundwater potential is crucial to ensure its sustainable use. The aim of the current study is to propose and verify new artificial intelligence methods for the spatial prediction of groundwater spring potential mapping at the Koohdasht–Nourabad plain, Lorestan province, Iran. These methods are new hybrids of an adaptive neuro-fuzzy inference system (ANFIS) and five metaheuristic algorithms, namely invasive weed optimization (IWO), differential evolution (DE), firefly algorithm (FA), particle swarm optimization (PSO), and the bees algorithm (BA). A total of 2463 spring locations were identified and collected, and then divided randomly into two subsets: 70 % (1725 locations) were used for training models and the remaining 30 % (738 spring locations) were utilized for evaluating the models. A total of 13 groundwater conditioning factors were prepared for modeling, namely the slope degree, slope aspect, altitude, plan curvature, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), distance from fault, distance from river, land use/land cover, rainfall, soil order, and lithology. In the next step, the step-wise assessment ratio analysis (SWARA) method was applied to quantify the degree of relevance of these groundwater conditioning factors. The global performance of these derived models was assessed using the area under the curve (AUC). In addition, the Friedman and Wilcoxon signed-rank tests were carried out to check and confirm the best model to use in this study. The result showed that all models have a high prediction performance; however, the ANFIS–DE model has the highest prediction capability (AUC = 0.875), followed by the ANFIS–IWO model, the ANFIS–FA model (0.873), the ANFIS–PSO model (0.865), and the ANFIS–BA model (0.839). The results of this research can be useful for decision makers responsible for the sustainable management of groundwater resources.

New cuckoo search algorithms with enhanced exploration and exploitation properties

Abstract: Cuckoo Search (CS) algorithm is nature inspired global optimization algorithm based on the brood parasitic behavior of cuckoos. It has proved to be an efficient algorithm as it has been successfully applied to solve a large number of problems of different areas. CS employs Levy flights to generate step size and to search the solution space effectively. The local search is carried out using switch probability in which certain percentages of solutions are removed. Though CS is an effective algorithm, still its performance can be improved by incorporating the exploration and exploitation during the search process. In this work, three modified versions of CS are proposed to improve the properties of exploration and exploitation. All these versions employ Cauchy operator to generate the step size instead of Levy flights to efficiently explore the search space. Moreover, two new concepts, division of population and division of generations, are also introduced in CS so as to balance the exploration and exploitation. The proposed versions of CS are tested on 24 standard benchmark problems with different dimension sizes and varying population sizes and the effect of probability switch has been studied. Apart from this, the best of the proposed versions is also tested on CEC 2015 benchmark suite. The modified algorithms have been statistically tested in comparison to the state-of-the-art algorithms, namely grey wolf optimization (GWO), differential evolution (DE), firefly algorithm (FA), flower pollination algorithm (FPA) and bat algorithm (BA). The numerical and statistical results prove the superiority of the proposed versions with respect to other popular algorithms available in the literature.

Automatic generation control of two-area electrical power systems via optimal fuzzy classical controller

Abstract: The interconnected large-scale power systems are liable to performance degradation under the presence of sudden small load demands, parameter ambiguity and structural changes. Due to this, to supply reliable electric power with good quality, robust and intelligent control strategies are extremely requisite in automatic generation control (AGC) of power systems. Hence, this paper presents an output scaling factor (SF) based fuzzy classical controller to enrich AGC conduct of two-area electrical power systems. An implementation of imperialist competitive algorithm (ICA) is made to optimize the output SF of fuzzy proportional integral (FPI) controller employing integral of squared error criterion. Initially the study is conducted on a well accepted two-area non-reheat thermal system with and without considering the appropriate generation rate constraint (GRC). The advantage of the proposed controller is illustrated by comparing the results with fuzzy controller and bacterial foraging optimization algorithm (BFOA)/genetic algorithm (GA)/particle swarm optimization (PSO)/hybrid BFOA-PSO algorithm/firefly algorithm (FA)/hybrid FA-pattern search (hFA-PS) optimized PI/PID controller prevalent in the literature. The proposed approach is further extended to a newly emerged two-area reheat thermal-PV system. The superiority of the method is depicted by contrasting the results of GA/FA tuned PI controller. The proposed control approach is also implemented on a multi-unit multi-source hydrothermal power system and its advantage is established by Correlating its results with GA/hFA-PS tuned PI, hFA-PS/grey wolf optimization (GWO) tuned PID and BFOA tuned FPI controllers. Finally, a sensitivity analysis is performed to demonstrate the robustness of the proposed method to broad changes in the system parameters and size and/or location of step load perturbation.

An Intelligent Parkinson’s Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach

Abstract: Parkinson's disease (PD) is a common neurodegenerative disease, which has attracted more and more attention. Many artificial intelligence methods have been used for the diagnosis of PD. In this study, an enhanced fuzzy k-nearest neighbor (FKNN) method for the early detection of PD based upon vocal measurements was developed. The proposed method, an evolutionary instance-based learning approach termed CBFO-FKNN, was developed by coupling the chaotic bacterial foraging optimization with Gauss mutation (CBFO) approach with FKNN. The integration of the CBFO technique efficiently resolved the parameter tuning issues of the FKNN. The effectiveness of the proposed CBFO-FKNN was rigorously compared to those of the PD datasets in terms of classification accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic curve). The simulation results indicated the proposed approach outperformed the other five FKNN models based on BFO, particle swarm optimization, Genetic algorithms, fruit fly optimization, and firefly algorithm, as well as three advanced machine learning methods including support vector machine (SVM), SVM with local learning-based feature selection, and kernel extreme learning machine in a 10-fold cross-validation scheme. The method presented in this paper has a very good prospect, which will bring great convenience to the clinicians to make a better decision in the clinical diagnosis.

Feature selection using firefly optimization for classification and regression models

Abstract: In this research, we propose a variant of the Firefly Algorithm (FA) for discriminative feature selection in classification and regression models for supporting decision making processes using data-based learning methods. The FA variant employs Simulated Annealing (SA)-enhanced local and global promising solutions, chaotic-accelerated attractiveness parameters and diversion mechanisms of weak solutions to escape from the local optimum trap and mitigate the premature convergence problem in the original FA algorithm. A total of 29 classification and 11 regression benchmark data sets have been used to evaluate the efficiency of the proposed FA model. It shows statistically significant improvements over other state-of-the-art FA variants and classical search methods for diverse feature selection problems. In short, the proposed FA variant offers an effective method to identify optimal feature subsets in classification and regression models for supporting data-based decision making processes.

Biomedical Text Categorization Based on Ensemble Pruning and Optimized Topic Modelling

Abstract: Text mining is an important research direction, which involves several fields, such as information retrieval, information extraction, and text categorization In this paper, we propose an efficient multiple classifier approach to text categorization based on swarm-optimized topic modelling The Latent Dirichlet allocation (LDA) can overcome the high dimensionality problem of vector space model, but identifying appropriate parameter values is critical to performance of LDA Swarm-optimized approach estimates the parameters of LDA, including the number of topics and all the other parameters involved in LDA The hybrid ensemble pruning approach based on combined diversity measures and clustering aims to obtain a multiple classifier system with high predictive performance and better diversity In this scheme, four different diversity measures (namely, disagreement measure, Q-statistics, the correlation coefficient, and the double fault measure) among classifiers of the ensemble are combined Based on the combined diversity matrix, a swarm intelligence based clustering algorithm is employed to partition the classifiers into a number of disjoint groups and one classifier (with the highest predictive performance) from each cluster is selected to build the final multiple classifier system The experimental results based on five biomedical text benchmarks have been conducted In the swarm-optimized LDA, different metaheuristic algorithms (such as genetic algorithms, particle swarm optimization, firefly algorithm, cuckoo search algorithm, and bat algorithm) are considered In the ensemble pruning, five metaheuristic clustering algorithms are evaluated The experimental results on biomedical text benchmarks indicate that swarm-optimized LDA yields better predictive performance compared to the conventional LDA In addition, the proposed multiple classifier system outperforms the conventional classification algorithms, ensemble learning, and ensemble pruning methods

A Pareto firefly algorithm for multi-objective disassembly line balancing problems with hazard evaluation

Abstract: The safety hazards existing in the process of disassembling waste products pose potential harms to the physical and mental health of the workers. In this article, these hazards involved in the disa...

Metaheuristic Algorithm for Photovoltaic Parameters: Comparative Study and Prediction with a Firefly Algorithm

Abstract: In this paper, a Firefly algorithm is proposed for identification and comparative study of five, seven and eight parameters of a single and double diode solar cell and photovoltaic module under different solar irradiation and temperature. Further, a metaheuristic algorithm is proposed in order to predict the electrical parameters of three different solar cell technologies. The first is a commercial RTC mono-crystalline silicon solar cell with single and double diodes at 33 °C and 1000 W/m2. The second, is a flexible hydrogenated amorphous silicon a-Si:H solar cell single diode. The third is a commercial photovoltaic module (Photowatt-PWP 201) in which 36 polycrystalline silicon cells are connected in series, single diode, at 25 °C and 1000 W/m2 from experimental current-voltage. The proposed constrained objective function is adapted to minimize the absolute errors between experimental and predicted values of voltage and current in two zones. Finally, for performance validation, the parameters obtained through the Firefly algorithm are compared with recent research papers reporting metaheuristic optimization algorithms and analytical methods. The presented results confirm the validity and reliability of the Firefly algorithm in extracting the optimal parameters of the photovoltaic solar cell.

Fractional calculus-based firefly algorithm applied to parameter estimation of chaotic systems

Abstract: This paper deals with the parameters estimation problem of chaotic systems using firefly algorithm (FA). The main contribution of the present work is to introduce a modified version of FA by incorporating fractional calculus during the search process, namely fractional-order FA (FOFA). FOFA simulates the behavior of each firefly with more historical memory, leading to enhance the performance of the basic FA by controlling its convergence speed. A simple structure and straightforward to implement are the main aspects of the proposed FOFA. First, the capability of FOFA is evaluated on the well-known test functions adopted from CEC'2015. Then, FOFA is applied for parameter estimation of chaotic systems. Results reveal that the incorporation of a memory term into the FA is an extremely significant development in comparison with other types of FAs.

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.

Optimal Hyper-Parameter Tuning of SVM Classifiers With Application to Medical Diagnosis

Abstract: Proper tuning of hyper-parameters is essential to the successful application of SVM-classifiers. Several methods have been used for this problem: grid search, random search, estimation of distribution Algorithms (EDAs), bio-inspired metaheuristics, among others. The objective of this paper is to determine the optimal method among those that recently reported good results: Bat algorithm, Firefly algorithm, Fruit-fly optimization algorithm, particle Swarm optimization, Univariate Marginal Distribution Algorithm (UMDA), and Boltzmann-UMDA. The criteria for optimality include measures of effectiveness, generalization, efficiency, and complexity. Experimental results on 15 medical diagnosis problems reveal that EDAs are the optimal strategy under such criteria. Finally, a novel performance index to guide the optimization process, that improves the generalization of the solutions while maintaining their effectiveness, is presented.

A Firefly Algorithm Based Wrapper-Penalty Feature Selection Method for Cancer Diagnosis

Abstract: Advances in cancer diagnosis methods have led to the development of highly accurate, detailed and voluminous data. Unfortunately, high dimensional data often leads to poor accuracy and high processing time. Swarm intelligence based feature selection methods have been highly efficient in the biomedical domain, which motivates the exploration of more adaptive and newer wrapper based methods such as the Firefly algorithm. This paper explores the inclusion of a penalty function to the existing fitness function promoting the Binary Firefly Algorithm to drastically reduce the feature set to an optimal subset, and shows an increase in both classification accuracy as well as feature reduction using a Random Forest classifier for the diagnosis of Breast, Cervical and Hepatocellular Carcinoma - Liver Cancer by the proposed method in comparison to other contemporary methods such as those based on Deep Learning, Information Gain and others.