Anupriya Jain, Seema Sharma, Sachin Sharma et al. · 2021
Data science community has been in continuous search for inspiration from nature and humans to solve various real-life problems. Machine learning itself is based on the way humans learn. Meanwhile, nature-inspired algorithms have gained popularity to solve complex optimization problems. These are termed as biology/nature-inspired algorithms. Researchers have found swarm intelligence algorithms that work on the principle of a swarm of bees, ants, etc. One of the most popular algorithms that use the behavior of fireflies, their light flashing patterns in order to find a global optimal solution is Firefly Algorithm (FA). In this chapter, we present the working principle of FA in detail with the algorithm explained and its implementation ready for the reference. In recent years, there has been the introduction of variants of FA to accommodate new problems. The hybrid or modified models have tremendously improved the performance of a standard FA. These special cases and applications of this meta-heuristic problem are discussed in detail. We also determine the need for FA over swarm optimization and why does FA works so well.
Cited by 7
Eduardo Fávero Pacheco da Luz, José Carlos Becceneri, Haroldo Fraga de Campos Velho et al. · 2023
Cited by 2
Luz, Eduardo Fávero Pacheco da, Becceneri, José Carlos, Campos Velho, Haroldo Fraga de et al. · Biblioteca Digital da Memória Científica do INPE (National Institute for Space Research) · 2023
Cited by 0
thcttpth · Zenodo (CERN European Organization for Nuclear Research) · 2024
<pre><code>\n<p><strong>firefly algorithm pdf</strong><br></p>\n<p>Rating: 4.4 / 5 (2003 votes)<br></p>\n<p>Downloads: 49811<br><br></p>\n <p>= = = = = \n<strong><a href="https://tds11111.com/21Nr9y?keyword=firefly algorithm pdf" target="_blank">CLICK HERE TO DOWNLOAD</a></strong>\n = = = = = <br><br></p>\n<p><br><br><br><br></p>\n<p><br><br><br><br></p>\n<p><br><br>Modification in original FA or hybrid FA algorithms are required to solve diverse range of engineering problems. A new hybrid variant of discrete firefly algorithm, called HDFA, is proposed, to solve traveling salesman problem (TSP), by utilizing the local This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. In Download as PDF; Printable versionthe firefly algorithm is a metaheuristic proposed by Xin-She Yang and inspired by the flashing behavior of fireflies Download PDF Abstract: The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO) Matlab codes are Firefly algorithm (FA) was developed by Xin-She Yang in and it hasbecome an important tool for solving the hardest optimization problems in almostall areas of optimization as well as engineering practice. The particles in the particle filter are optimized using FA before resampling Xin-She Yang. In the work presented by Gao et al. For details, please read my book: Nature-Inspired Optimization Algorithms, Elsevier, (). Middlesex University London. The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as Download as PDF; Printable version; In mathematical optimization, the firefly algorithm is a metaheuristic proposed by Xin-She Yang and inspired by the flashing behavior of The firefly algorithm (FA) was developed by Xin-She Yang in (Yang,,, b) and is based on the flashing patterns and behavior of tropical fireflies. Experiments using binary firefly algorithms show that this algorithm could successfully be applied to those problems where binary representation is necessary Firefly Algorithm (FA) is one of the popular algorithm of Swarm Intelligence domain that can be used in most of the areas of optimization. Based on the flashing pattern of light and their intelligent behaviour, FA can solve problem in all fields of optimization and is considered very useful for solving complex mathematical and engineering problems The results of firefly algorithms depend on the best-found solution within a swarm. Nature-inspired algorithms are among the most powerful algorithms for optimization. (), an improved particle filter based on FA is proposed to solve a main handicap of the particle filter. Many problems from various areas have been successfully solved using the firefly algorithm and its variants The firefly algorithm (FA) is one of the latest swarm intelligence algorithms created by Yang. Firefly algorithm (FA) was developed by Xin-She Yang in and it hasbecome an important tool for solving the hardest optimization problems in almostall areas of optimization as well as engineering practice. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. Various FA variants have been developed to suitdifferent Firefly Algorithm (FA) is a meta-heuristic algorithm which is categorized as one of the fast-growing swarm intelligence algorithms. It is an effective and an easy to implement DOI: /JISE Corpus IDImproved Speciation-Based Firefly Algorithm in Dynamic and Uncertain Environments @article{NasiriImprovedSF, title={Improved Speciation-Based Firefly Algorithm in @inproceedings{RamsheAFA, title={A Firefly Algorithm for Portfolio Optimization Problem with Cardinality Constraint}, author={Manije Ramshe and Mohsen Gharahkhani and Ali Feiz and Seyed Jafar Sadjadi}, year={ Simulation results show that mutation-incorporated fireflies algorithm gives better result with larger computational time, whereas versions of firefly algorithm with opposition-based learning and elitist movement for the brighter firefly give approximate solution with smaller computational time. Firefly algorithm is a swarm based metaheuristic algorithm inspired by the flashing behavior of fireflies. The literature has expandedsignificantly in the last few years. Therefore, improving the best solution can improve the search power of the swarm. FA and its variants are simple to implement and easily understood. FA is Computer ScienceTLDR. We will compare the proposed firefly algorithm , · Download PDF Abstract: The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of Medard Ngnotchouye. These can be used to successfully solve the problems of different areas. Firefly algorithm is one of the well-known swarm-based The Firefly Algorithm: An Introduction. The literature has expandedsignificantly in the last few years Abstract. Xin-She Y ang. 1, · Abstract.</p></code></pre>
Cited by 0Open access
Mousavi, Seyed Muhammad Hossein · 2022
Cited by 0Open access
thcttpth · Zenodo (CERN European Organization for Nuclear Research) · 2024
firefly algorithm pdf Rating: 4.4 / 5 (2003 votes) Downloads: 49811 = = = = = CLICK HERE TO DOWNLOAD = = = = = Modification in original FA or hybrid FA algorithms are required to solve diverse range of engineering problems. A new hybrid variant of discrete firefly algorithm, called HDFA, is proposed, to solve traveling salesman problem (TSP), by utilizing the local This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. In Download as PDF; Printable versionthe firefly algorithm is a metaheuristic proposed by Xin-She Yang and inspired by the flashing behavior of fireflies Download PDF Abstract: The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO) Matlab codes are Firefly algorithm (FA) was developed by Xin-She Yang in and it hasbecome an important tool for solving the hardest optimization problems in almostall areas of optimization as well as engineering practice. The particles in the particle filter are optimized using FA before resampling Xin-She Yang. In the work presented by Gao et al. For details, please read my book: Nature-Inspired Optimization Algorithms, Elsevier, (). Middlesex University London. The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as Download as PDF; Printable version; In mathematical optimization, the firefly algorithm is a metaheuristic proposed by Xin-She Yang and inspired by the flashing behavior of The firefly algorithm (FA) was developed by Xin-She Yang in (Yang,,, b) and is based on the flashing patterns and behavior of tropical fireflies. Experiments using binary firefly algorithms show that this algorithm could successfully be applied to those problems where binary representation is necessary Firefly Algorithm (FA) is one of the popular algorithm of Swarm Intelligence domain that can be used in most of the areas of optimization. Based on the flashing pattern of light and their intelligent behaviour, FA can solve problem in all fields of optimization and is considered very useful for solving complex mathematical and engineering problems The results of firefly algorithms depend on the best-found solution within a swarm. Nature-inspired algorithms are among the most powerful algorithms for optimization. (), an improved particle filter based on FA is proposed to solve a main handicap of the particle filter. Many problems from various areas have been successfully solved using the firefly algorithm and its variants The firefly algorithm (FA) is one of the latest swarm intelligence algorithms created by Yang. Firefly algorithm (FA) was developed by Xin-She Yang in and it hasbecome an important tool for solving the hardest optimization problems in almostall areas of optimization as well as engineering practice. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. Various FA variants have been developed to suitdifferent Firefly Algorithm (FA) is a meta-heuristic algorithm which is categorized as one of the fast-growing swarm intelligence algorithms. It is an effective and an easy to implement DOI: /JISE Corpus IDImproved Speciation-Based Firefly Algorithm in Dynamic and Uncertain Environments @article{NasiriImprovedSF, title={Improved Speciation-Based Firefly Algorithm in @inproceedings{RamsheAFA, title={A Firefly Algorithm for Portfolio Optimization Problem with Cardinality Constraint}, author={Manije Ramshe and Mohsen Gharahkhani and Ali Feiz and Seyed Jafar Sadjadi}, year={ Simulation results show that mutation-incorporated fireflies algorithm gives better result with larger computational time, whereas versions of firefly algorithm with opposition-based learning and elitist movement for the brighter firefly give approximate solution with smaller computational time. Firefly algorithm is a swarm based metaheuristic algorithm inspired by the flashing behavior of fireflies. The literature has expandedsignificantly in the last few years. Therefore, improving the best solution can improve the search power of the swarm. FA and its variants are simple to implement and easily understood. FA is Computer ScienceTLDR. We will compare the proposed firefly algorithm , · Download PDF Abstract: The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of Medard Ngnotchouye. These can be used to successfully solve the problems of different areas. Firefly algorithm is one of the well-known swarm-based The Firefly Algorithm: An Introduction. The literature has expandedsignificantly in the last few years Abstract. Xin-She Y ang. 1, · Abstract.
Cited by 0Open access
Sankalap Arora, Sarbjeet Singh, Satvir Singh et al. · 2014
In the standard firefly algorithm, every firefly has same parameter settings and its value changes from iteration to iteration. The solutions keeps on changing as the optima are approaching which results that it may fall into local optimum. Furthermore, the underlying strength of the algorithm lies in the attractiveness of less brighter firefly towards the brighter firefly which has an impact on the convergence speed and precision. So to avoid the algorithm to fall into local optimum and reduce the impact of maximum of iteration, a mutated firefly algorithm is proposed in this paper. The proposed algorithm is based on monitoring the movement of fireflies by using different probability for each firefly and then perform mutation on each firefly according to its probability. Simulations are performed to show the performance of proposed algorithm with standard firefly algorithm, based on ten standard benchmark functions. The results reveals that proposed algorithm improves the convergence speed, accurateness and prevent the premature convergence.
Cited by 25
Swarm Intelligence · 2015
Cited by 0
Mahdi Bidar, Hamidreza Rashidy Kanan · 2013
Firefly algorithm is one of the metaheuristic algorithms which are used to solve optimization problems. Firefly algorithm is such a population-based algorithm so that their group behavior and interaction result in a swarm Intelligence that is used to find the best and fittest firefly. Since this algorithm is based on the search agents, it utilizes different agents with different qualities in searching the problem space. In order to increase the quality of firefly society to strengthen it to search the problem space efficiently and find the optimal solution, we have proposed a new algorithm based on the firefly algorithm. This algorithm with the aid of a Status Table, records behavior of the fireflies in details and identifies weak agents. Then the proposed algorithm enables weak ones to jump to new positions in order to attain a high probability of finding the qualified solutions. Modification of the search agents individually, leads to modification of the whole population and subsequently causes to increase the performance of algorithm in finding the optimal solutions.
Cited by 18
Lauro C. M. de Paula (672575), Anderson S. Soares (672576), Telma W. de Lima (672577) et al. · Figshare · 2015
Cited by 0Open access
Ariel O. Gamao, Bobby D. Gerardo, Ruji P. Medina · 2019 IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS) · 2019
The strength of the Firefly algorithm depends on the attractiveness of fireflies with lesser intensity towards the superior firefly with brighter intensity. The mutated firefly algorithm (MFA) explores the search space by enhancing the features of less glowing fireflies from more luminous fireflies. The MFA converges faster than the standard FA using the 40:40 formula. With this, only the top 40 percent and bottom 40 percent of the fireflies join in the mutation process. Consequently, there has to be an added sorting algorithm to achieve such a preferred method. However, the sorting algorithm entails processing time depending on its applied technique, size of data, and CPU. A Modified Mutated Firefly Algorithm (MMFA) was developed to optimize the convergence time, employing a stochastic approach with increased convergence time. The results of the simulations show a significant improvement in terms of convergence time.
Cited by 3
Qi XB(亓祥波), Zhu, Sihan, Zhang H(张浩) · SIA OpenIR (Chinese Academy of Sciences) · 2017
Firefly Algorithm (FA) is a bio-inspired algorithm simulating the flashing behavior of fireflies. In original algorithm, all the fireflies are unisex and only the flashing behavior is simulated. This paper presents a novel hybrid firefly algorithm (HFA) adding mating behavior in original firefly algorithm. The new algorithm is compared with FA and other three well-known bio-inspired algorithms on fifteen benchmark functions. The experimental results show HFA has a good optimizing ability on most benchmark functions and is proved to have significant improvement over canonical FA and several other comparison algorithms.
Cited by 0
Zhengyu Xu (8550660), Guofeng Zhao (2317765), Xian Liao (7500422) et al. · Figshare · 2025
<div> As a non-contact method, the transient electromagnetic (TEM) method has the characteristics of high efficiency, small impact of device, no limitation of site range, and high resolution, and is a hot topic in current research. However, the research on the refined data processing method of TEM is lag, which seriously restricts the application in superficial engineering investigation and is a key problem that needs to be solved urgently. The particle swarm optimization (PSO) algorithm and firefly algorithm (FA) were successful swarm intelligence algorithms inspired by nature. However, the accuracy and efficiency of the algorithm restrict its further development. In this paper, the particle moving velocity of FA algorithm is defined according to the concept of particle moving velocity in PSO algorithm, so as to improve the local fast convergence ability of FA algorithm. On this basis, the appropriate velocity of particle movement is improved, so that the improved algorithm can overcome the oscillation problem around the optimal solution and improve the computational efficiency. And finally, an improved PSO-IFA hybrid optimization algorithm (PSO-IFAH) was proposed in the paper. The proposed algorithm can exploit the strong points of both PSO and FA algorithm mechanisms. A typical layered model was established, and the PSO algorithm, FA algorithm, and PSO-IFAH algorithm were applied to inversion calculations. The results show that the PSO-IFAH algorithm improves calculation accuracy by more than 80% and efficiency by over 60% compared to the PSO and FA algorithms, respectively. The PSO-IFAH algorithm also exhibits high inversion accuracy and stability, with superior anti-noise properties compared to the other algorithms. When implemented in ground TEM measurement data processing, the PSO-IFAH algorithm enhances the resolution of anomalies and low-resistance details, aligning well with actual excavation results. This highlights the algorithm’s capability to depict underground electrical structures and karst developments accurately, thereby improving the precision of TEM data processing and interpretation. </div>
Cited by 0Open access
Rüştü Akay, Alper Baştürk · Journal of Intelligent Systems with Applications · 2018
In this study, the advantages of the parallel compution paradigms are utilized in a recent optimization algorithm, firefly algorithm. In the proposed implementation, the population is divided into subpopulations and each subpopulation is run on a different processing node. From the results on commonly used benchmark functions, the proposed model enhances the computation cost without comprosing on the solution quality.
Cited by 0Open access
Waleed Alomoush, Khairuddin Omar, Ayat Alrosan et al. · Journal of King Saud University - Computer and Information Sciences · 2018
Cited by 23Open access
Tomáš Kadavý, Roman Šenkeřík, Michal Pluháček et al. · Logic Journal of IGPL · 2020
Abstract The primary aim of this original work is to provide a more in-depth insight into the relations between control parameters adjustments, learning techniques, inner swarm dynamics and possible hybridization strategies for popular swarm metaheuristic Firefly Algorithm (FA). In this paper, a proven method, orthogonal learning, is fused with FA, specifically with its hybrid modification Firefly Particle Swarm Optimization (FFPSO). The parameters of the proposed Orthogonal Learning Firefly Algorithm are also initially thoroughly explored and tuned. The performance of the developed algorithm is examined and compared with canonical FA and above-mentioned FFPSO. Comparisons have been conducted on well-known CEC 2017 benchmark functions, and the results have been evaluated for statistical significance using the Friedman rank test.
Cited by 2
Mario H. A. C. Adaniya, Luiz F. Carvalho, Bruno Bogaz Zarpel�ão et al. · Elsevier eBooks · 2015
Cited by 3
Jani Dugonik, Iztok Fister · 2016
This paper proposes a meta-heuristic Multi-Population Fire-fly Algorithm (MPFA) for single-modal optimization using two multi-population models, i.e., one is based on the island model while the other on the mainland-island model. The unique characteristics of each sub-population is evolved in-dependently and the diversity of the entire population is ef-fectively increased. Sub-populations communicate with each other to exchange information in order to expand the search range of the entire population. In line with this, each sub-population explores a specific part of the search space and contributes its part for exploring the global search space. The main goal of this paper was to analyze the performance between MPFA and the original Firefly Algorithm (FA). Ex-periments were performed on a CEC 2014 benchmark suite consisting of 16 single-objective functions and the obtained results show improvements in most of them.
Cited by 0
Bingnan Pei, Hongyan Wang, Hao Zhang et al. · 2015
Regarding the relatively big errors with running the DV-Hop localization algorithm in a network topology environment, with which the nodes were randomly distributed, the firefly algorithm(FA) was used to correct the position estimated by DV-Hop, a firefly optimization localization algorithm based on DV-Hop was proposed. The mathematical model was built with the smallest error for all of unknown nodes, and the firefly algorithm was used to obtain the optimal solution of the model without additional devices and increasing the traffic. The simulation results show that the average localization error of the FDV-Hop algorithm is significantly improved compared with the original algorithm.
Cited by 18
Chuandong Song · Lecture notes in computer science · 2019
Cited by 2