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Polymers-Based Self-Healing Cementitious Materials
Published in Ghasan Fahim Huseien, Iman Faridmehr, Mohammad Hajmohammadian Baghban, Self-Healing Cementitious Materials, 2022
Ghasan Fahim Huseien, Iman Faridmehr, Mohammad Hajmohammadian Baghban
Fireflies, also known as lightning bugs, are nocturnal, luminous beetles. Several researchers have studied the behavior of this creature in nature [46]. The nature-inspired firefly algorithm is a metaheuristic algorithm proposed by Fister et al. [47] and stimulated by the insect’s flashing behavior. In the classic firefly optimization algorithm, two fundamental aspects need to be clarified: the source light and attractiveness. The intensity of light I is referred to as an absolute measure of emitted light by the firefly, while the attractiveness β is the measure of light seen by the other fireflies. The intensity of light is defined using following equation.
An Optimized False Positive Free Video Watermarking System in Dual Transform Domain
Published in D. P. Acharjya, V. Santhi, Bio-Inspired Computing for Image and Video Processing, 2018
The firefly algorithm (FA) is a nature-inspired metaheuristic optimization algorithm inspired by the fireflies’ flashing behavior. The crucial purpose for a firefly’s flash is to act as a signal system to attract other fireflies. Xin-She Yang [42] formulated this firefly algorithm using the following rules. The two important points in the firefly algorithm are: the variation in the light intensity and formulation of the attractiveness. In general, the attractiveness of a firefly is determined by its brightness, which in turn is proportional to the encoded objective function.All fireflies are unisexual, so that any individual firefly will be attracted to all other fireflies.The brightness of a firefly is determined by the encoded objective function.Attractiveness is proportional to their brightness, and for any two flashing fireflies, the less bright one will be attracted by the brighter one; however, the intensity decreases as their mutual distance increases. If there are no fireflies brighter than a given firefly, it will move randomly.Now the general firefly algorithm can be stated [12] as below.
Firefly Algorithm
Published in Kaushik Kumar, Divya Zindani, J. Paulo Davim, Optimizing Engineering Problems through Heuristic Techniques, 2020
Kaushik Kumar, Divya Zindani, J. Paulo Davim
Cellular learning automata was hybridized with the classical firefly algorithm in a study conducted by Hassanzadeh and Meybodi (2012). The cellular learning automata was responsible for diversified solutions in the population of firefly and firefly algorithm was incorporated to improve these solution. Five well-known benchmark functions were used to adjudge the performance of the proposed algorithm. The results revealed that the algorithm was able to find global optima and enhance the exploration rate of standard firefly algorithm.
Metaheuristic Algorithms in Smart Farming: An Analytical Survey
Published in IETE Technical Review, 2023
Ant colony optimization is inspired by the behavior of ants, who communicate with each other through pheromone trails to find the shortest path between their nest and food source. In this algorithm, artificial ants deposit pheromones on a graph, and the pheromone concentration determines the likelihood of choosing a particular path [20]. Firefly algorithm is inspired by the flashing behavior of fireflies. In this algorithm, fireflies move towards each other and their brightness determines their attractiveness. The algorithm involves creating a population of fireflies that move towards the brighter ones to find the optimal solution [21]. Artificial bee colony algorithm is inspired by the behavior of bees. In this algorithm, bees search for food sources, and the quality of the food source determines the likelihood of other bees visiting that source. The algorithm involves creating a population of artificial bees that search for the best solution to a problem [22].
An Enhanced Firefly Algorithm for Time Shared Grid Task Scheduling
Published in Applied Artificial Intelligence, 2021
Firefly Algorithm (FA) is a metaheuristic algorithm inspired by the flashing behavior of fireflies (YANG 2010). The FA is a population-based technique that finds the optimal global solution based on swarm intelligence, investigating the foraging behavior of fireflies (Senthilnath, OMKAR, and Mani 2011). Similar to other metaheuristics optimization methods, the FA generates a random initial population of feasible candidate solutions (Dey et al. 2020; RAJAGOPALAN, MODALE, and SENTHILKUMAR 2020). This paper introduces an enhanced time-shared metaheuristic scheduling mechanism for the computational grid. The paper proposes an enhanced time shared metaheuristics mechanism based on Firefly Algorithm to improve the grid job scheduling process. The proposed mechanism utilizes the Smallest Position Value (SPV) technique to handle the scheduling problem as permutations. The details of the approach, the flowcharts as well as the pseudo-codes of the algorithms used are described. We evaluated the proposed time-shared metaheuristic scheduling mechanism using simulation and real workload data. Furthermore, the details of the simulation model, including its parameter, experimentations design and simulation results, are also elaborated in this paper. This paper contains six sections. Section 2 reviews the related works. Section 3 illustrates the standard firefly algorithm. Sections 4 and 5 present the proposed mechanism and the evaluation process, respectively. We the paper is concluded in Section 6.
Classification Framework for Clinical Datasets Using Synergistic Firefly Optimization
Published in IETE Journal of Research, 2021
V. R. Elgin Christo, H. Khanna Nehemiah, S. Keerthana Sankari, Shiney Jeyaraj, A. Kannan
In this work, the missing values are imputed using the k-NN technique. Min–max normalization is used to normalize the data and the normalized data are split into a training set and a testing set using ten-fold cross-validation. Feature selection is done by the wrapper approach using the Synergistic firefly algorithm and the Levenberg-Marquardt back-propagation algorithm is used as the classifier. The Synergistic firefly algorithm proposed in this work is an extension to the basic Firefly algorithm, inspired based on the flashing characteristics of fireflies, developed by Yang [15] for multimodal optimization and is used in various optimization problems. However, the major drawback of the basic firefly algorithm is getting trapped in local optima or having slow convergence speed when solving complex problems. The proposed synergistic firefly algorithm performs both global and local searches, unlike the basic firefly algorithm [15]. The global search helps overcome the slow convergence, whereas using both local and global searches helps jump out of local optima. The optimal features selected using this approach have been used to train an ensemble classifier. The ensemble classifier is built using the Back Propagation Neural Network, Extreme Learning Machine and Naive Bayes classifier.