Showing papers on "Firefly algorithm published in 2011"
TL;DR: In this paper, a multi-objective bat algorithm (MOBA) is proposed to solve multiobjective design problems such as welded beam design, and validated against a subset of test functions.
Abstract: Engineering optimisation is typically multi-objective and multidisciplinary with complex constraints, and the solution of such complex problems requires efficient optimisation algorithms. Recently, Xin-She Yang proposed a bat-inspired algorithm for solving non-linear, global optimisation problems. In this paper, we extend this algorithm to solve multi-objective optimisation problems. The proposed multi-objective bat algorithm (MOBA) is first validated against a subset of test functions, and then applied to solve multi-objective design problems such as welded beam design. Simulation results suggest that the proposed algorithm works efficiently.
767 citations
TL;DR: A recently developed metaheuristic optimization algorithm, the Firefly Algorithm, which mimics the social behavior of fireflies based on their flashing characteristics is used for solving mixed continuous/discrete structural optimization problems.
720 citations
TL;DR: It is concluded that the FA can be efficiently used for clustering and compared with other two nature inspired techniques — Artificial Bee Colony, Particle Swarm Optimization and other nine methods used in the literature.
Abstract: A Firefly Algorithm (FA) is a recent nature inspired optimization algorithm, that simulates the flash pattern and characteristics of fireflies. Clustering is a popular data analysis technique to identify homogeneous groups of objects based on the values of their attributes. In this paper, the FA is used for clustering on benchmark problems and the performance of the FA is compared with other two nature inspired techniques — Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and other nine methods used in the literature. Thirteen typical benchmark data sets from the UCI machine learning repository are used to demonstrate the results of the techniques. From the results obtained, we compare the performance of the FA algorithm and conclude that the FA can be efficiently used for clustering.
437 citations
TL;DR: A solution to this famous problem of economic emission load dispatch is described using a new metaheuristic nature-inspired algorithm, called firefly algorithm, which was developed by Dr. Xin-She Yang at Cambridge University in 2007.
Abstract: Efficient and reliable power production is necessary to meet both the profitability of power systems operations and the electricity demand, taking also into account the environmental concerns about the emissions produced by fossil-fuelled power plants. The economic emission load dispatch problem has been defined and applied in order to deal with the optimization of these two conflicting objectives, that is, the minimization of both fuel cost and emission of generating units. This paper introduces and describes a solution to this famous problem using a new metaheuristic nature-inspired algorithm, called firefly algorithm, which was developed by Dr. Xin-She Yang at Cambridge University in 2007. A general formulation of this algorithm is presented together with an analytical mathematical modeling to solve this problem by a single equivalent objective function. The results are compared with those obtained by alternative techniques proposed by the literature in order to show that it is capable of yielding good optimal solutions with proper selection of control parameters.
317 citations
TL;DR: An attempt is made to review the hybrid optimization techniques in which one main algorithm is a well known metaheuristic; particle swarm optimization or PSO and three hybrid PSO algorithms are compared on a test suite of nine conventional benchmark problems.
246 citations
TL;DR: The experimental results show that the proposed FF-based MCET algorithm can efficiently search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method when the number of thresholds is less than 5.
Abstract: The minimum cross entropy thresholding (MCET) has been widely applied in image thresholding. The search mechanism of firefly algorithm inspired by the social behavior of the swarms of firefly and the phenomenon of bioluminescent communication, is used to search for multilevel thresholds for image segmentation in this paper. This new multilevel thresholding algorithm is called the firefly-based minimum cross entropy thresholding (FF-based MCET) algorithm. Four different methods that are the exhaustive search, the particle swarm optimization (PSO), the quantum particle swarm optimization (QPSO) and honey bee mating optimization (HBMO) methods are implemented for comparison with the results of the proposed method. The experimental results show that the proposed FF-based MCET algorithm can efficiently search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method when the number of thresholds is less than 5. The need of computation time of using the FF-based MCET algorithm is the least, meanwhile, the results using the FF-based MCET algorithm is superior to the ones of PSO-based and QPSO-based MCET algorithms but is not significantly different to the HBMO-based MCET algorithm.
195 citations
06 Sep 2011
TL;DR: Computer simulation shows that the simple form of FA without combination with other methods performs very well to solve some TSP instances, but it can be trapped into local optimum solutions for some other instances.
Abstract: This paper addresses how to apply firefly algorithm (FA) for travelling salesman problem (TSP). Two schemes are studied, i.e. discrete distance between two fireflies and the movement scheme. Computer simulation shows that the simple form of FA without combination with other methods performs very well to solve some TSP instances, but it can be trapped into local optimum solutions for some other instances.
184 citations
TL;DR: To stabilize firefly's movement, a new behavior to direct fireflies movement to global best if there was no any better solution around them is proposed, and in addition to increase convergence speed it is proposed to use Gaussian distribution to move all fireflies to globalbest in each iteration.
Abstract: Firefly algorithm is one of the evolutionary optimization algorithms, and is inspired by fireflies behavior in nature. Each firefly movement is based on absorption of the other one. In this paper to stabilize firefly's movement, it is proposed a new behavior to direct fireflies movement to global best if there was no any better solution around them. In addition to increase convergence speed it is proposed to use Gaussian distribution to move all fireflies to global best in each iteration. Proposed algorithm was tested on five standard functions that have ever used for testing the static optimization algorithms. Experimental results show better performance and more accuracy than standard Firefly algorithm.
145 citations
05 Jun 2011
TL;DR: A modified FA approach combined with chaotic sequences (FAC) applied to reliability-redundancy optimization is introduced and was found to outperform the previously best-known solutions available.
Abstract: The reliability-redundancy allocation problem can be approached as a mixed-integer programming problem. It has been solved by using optimization techniques such as dynamic programming, integer programming, and mixed-integer nonlinear programming. On the other hand, a broad class of meta-heuristics has been developed for reliability-redundancy optimization. Recently, a new meta-heuristics called firefly algorithm (FA) algorithm has emerged. The FA is a stochastic metaheuristic approach based on the idealized behavior of the flashing characteristics of fireflies. In FA, the flashing light can be formulated in such a way that it is associated with the objective function to be optimized, which makes it possible to formulate the firefly algorithm. This paper introduces a modified FA approach combined with chaotic sequences (FAC) applied to reliability-redundancy optimization. In this context, an example of mixed integer programming in reliability-redundancy design of an overspeed protection system for a gas turbine is evaluated. In this application domain, FAC was found to outperform the previously best-known solutions available.
121 citations
TL;DR: The author extends the standard firefly algorithm further to introduce chaos-enhanced fireflies algorithm with automatic parameter tuning, which results in two more variants of FA, which is used to solve a benchmark design problem in engineering.
Abstract: Many metaheuristic algorithms are nature-inspired, and most are population-based. Particle swarm optimization is a good example as an efficient metaheuristic algorithm. Inspired by PSO, many new algorithms have been developed in recent years. For example, firefly algorithm was inspired by the flashing behaviour of fireflies. In this paper, the author extends the standard firefly algorithm further to introduce chaos-enhanced firefly algorithm with automatic parameter tuning, which results in two more variants of FA. The author first compares the performance of these algorithms, and then uses them to solve a benchmark design problem in engineering. Results obtained by other methods will be compared and analyzed.
85 citations
10 Nov 2011
TL;DR: Experimental results show that binary firefly algorithm is capable of finding correct results more efficiently than GA, and compared with the results shown by Genetic Algorithm to discover the plaintext from the cipher text.
Abstract: This paper presents a binary Firefly Algorithm (FA), for cryptanalysis of knapsack cipher algorithm so as to deduce the meaning of an encrypted message (i.e. to determine a plaintext from the cipher text). The implemented algorithm has been characterized, in this paper, by a number of properties and operations that build up and evolve the fireflies' positions. These include light intensity, distances, attractiveness, and position updating, fitness evaluation. The results of the Firefly algorithm are compared with the results shown by Genetic Algorithm (GA), to discover the plaintext from the cipher text. Experimental results show that binary firefly algorithm is capable of finding correct results more efficiently than GA.
Journal Article•
TL;DR: In this paper, an optimum design of truss structures with both sizing and geometry design variables is carried out using the firefly algorithm and modifications in the movement stage of artificial fireflies are proposed to improve the efficiency of the algorithm.
Abstract: Nature-inspired search algorithms have proved to be successful in solving real-world optimization problems. Firefly algorithm is a novel meta-heuristic algorithm which simulates the natural behavior of fireflies. In the present study, optimum design of truss structures with both sizing and geometry design variables is carried out using the firefly algorithm. Additionally, to improve the efficiency of the algorithm, modifications in the movement stage of artificial fireflies are proposed. In order to evaluate the performance of the proposed algorithm, optimum designs found are compared to the previously reported designs in the literature. Numerical results indicate the efficiency and robustness of the proposed approach.
01 Jan 2011
26 Jul 2011
TL;DR: In this paper, a maximum variance intra-cluster based on Firefly algorithm is proposed to optimize the runtimes and segmentation accuracy of Otsu's method for image segmentation, which can search for optimal multiple thresholds, which are very efficient for segmentation.
Abstract: Segmentation is a low level operation that can segment the image in discrete and homogenous regions. Otsu's method for image segmentation selects an optimum threshold by maximizing the variance Intra-clusters in a gray level image. However, with increasing the number of classes, the total runtimes also increase exponentially. Due to the fact, that a large number of iterations are required for computing the mean of intra-cluster variance. In this paper, Firefly algorithm is used to optimize the runtimes and segmentation accuracy. Firefly algorithm has some characteristics that make it suitable for solving optimization problem, like higher converging speed and less computation rate. Here Firefly algorithm is proposed to optimize Otsu's method. This method is called maximum variance Intra-cluster based on Firefly algorithm. The proposed method is compared to Otsu's method and recursive Otsu. The experimental results show that the proposed method is far more efficient to Otsu's method and recursive Otsu. The proposed method can search for optimal multiple thresholds, which are very efficient for segmentation. Numbers of thresholds' values have greatly less effect on total runtimes. For evaluation of segmentation result we use peak signal to noise ratio method (PSNR).
01 Dec 2011
TL;DR: This paper discusses the integration of the operators of mutation and crossover commonly used in Genetic Algorithms with the Firefly Algorithm for cryptanalysis of the monoalphabetic substitution cipher.
Abstract: The monoalphabetic substitution cipher encrypts a given text by replacing every letter in the text with a different letter according to some predefined scheme. The cryptanalysis of this cipher involves the identification of this scheme using known language statistical data. The firefly algorithm (FA) is a metaheuristic algorithm, inspired by the flashing behavior of fireflies. This paper discusses the integration of the operators of mutation and crossover commonly used in Genetic Algorithms with the Firefly Algorithm for cryptanalysis of the monoalphabetic substitution cipher.
19 Dec 2011
TL;DR: An adaptive local enhancement algorithm based on Firefly Algorithm is proposed, which represents a new approach for optimization and offers better performance than existing methods.
Abstract: The principal objective of enhancement is to improve the contrast and detail an image so, that the result is more suitable than the original image for a specific application. The enhancement process is a non-linear optimization problem with several constraints. In this paper, an adaptive local enhancement algorithm based on Firefly Algorithm (FA) is proposed. FA represents a new approach for optimization. The FA is used to search the optimal parameters for the best enhancement. In the proposed method, the evaluation criterion is defined by edge numbers, edge intensity and the entropy. The proposed method is demonstrated and compared with Linear Contrast Stretching (LCS), Histogram Equalization (HE), Genetic Algorithm based image Enhancement (GAIE), and the Particle Swarm Optimization based image enhancement (PSOIE) methods. Experimental results presented that proposed technique offers better performance.
01 Jan 2011
TL;DR: Combination of learning automata and fireflies in order to improve the performance of firefly algorithm in dynamic environments has been proposed and it is shown that the new algorithm optimizer significantly outperforms previous approaches.
Abstract: Many real world problems are mostly time varying optimization problems, which require special mechanisms to detect changes in environment and then response to them. In this paper, combination of learning automata and firefly algorithm in order to improve the performance of firefly algorithm in dynamic environments has been proposed. In the algorithm, the firefly algorithm has been equipped with three learning automatons and velocity parameter, so they can increase diversity in response the dynamic environments. The main idea is based to split the population of fireflies into a set of interacting swarms. This Algorithm evaluated on a variety of instances of the multimodal dynamic moving peaks benchmark. Results are also compared with alternative approaches from the literature. They show that the new algorithm optimizer significantly outperforms previous approaches.
10 Nov 2011
TL;DR: Proposed algorithm has been compared with existing algorithm like Genetic Algorithm, Particle swarm Optimization on benchmark functions and experiments prove that proposed algorithm is better in many cases.
Abstract: Although a number of nature inspired algorithms exist in literature to solve optimization problems, yet there is always a need of new algorithm which can search for optimum solution in minimum time. This paper proposes a new optimization algorithm for solving optimization problems. Proposed algorithm has been compared with existing algorithm like Genetic Algorithm, Particle swarm Optimization on benchmark functions and experiments prove that proposed algorithm is better in many cases.
01 Jan 2011
TL;DR: In this article, an algorithm for obtaining a black-box-type solution which maintains a high production rate and the high quality of the products is described, based on a numerical model of 2D temperature field designed for the real caster geometry.
Abstract: The ambition to increase both the productivity and the product quality in the continuous casting process, led us to study new, effective mathematical approaches. The quality of the steel produced with the continuous casting process is influenced by the controlled factors, such as the casting speed or cooling rates. The appropriate setting of these factors is usually obtained with expert estimates and expensive experimental runs. This paper describes an algorithm for obtaining a black-box-type solution which maintains a high production rate and the high quality of the products. The core of the algorithm is our original numerical model of 2D temperature field designed for the real caster geometry. The mathematical model contains Fourier-Kirchhoff equation and includes boundary conditions. Phase and structural changes are modeled by the enthalpy computed from the chemical composition of the steel. The optimization part is performed with a recently created heuristic method, the so-called Firefly algorithm, in which the principles of searching for optimal values are inspired by the biological behavior of fireflies. Combining the numerical model and heuristic optimization we are able to set the controlled values and to obtain high-quality steel that satisfies the constraints for the prescribed metallurgical length, core and surface temperatures. This approach can be easily utilized for an arbitrary class of steel only by changing its chemical composition in the numerical model. The results of the simulations can be validated with real historical data in order to compare the relationship between the temperature field and the final product quality. Ambicije za pove~anje produktivnosti in kakovosti kon~nega proizvoda pri kontinuirnem ulivanju sta nas pripeljala do {tudija novih u~inkovitih matemati~nih prijemov. Na kakovost jekla, proizvedenega s kontinuirnim ulivanjem, vplivajo {tevilni nadzorovani dejavniki, kot sta npr. hitrost ulivanja in ohlajanja. Ustrezno dolo~anje teh dejavnikov je navadno povezano s strokovnimi ocenami in dragimi poizkusi. Prispevek opisuje algoritem za vrsto re{itev za ohranjanje visoke stopnje proizvodnje in visoke kakovosti izdelkov. Jedro algoritma je na{ prvotni numeri~ni model 2D-polja temperature, namenjen ulivalni geometriji. Ta matemati~ni model vsebuje Fourier-Kirchhoffovo ena~bo in tudi robne pogoje. Fazne in strukturne spremembe so bile modelirane z entalpijo, izra~unano iz kemijske sestave jekla. Optimizacijski del je bil izveden z nedavno narejeno hevristi~no metodo, s tako imenovanim algoritmom Firefly, kjer na~ela iskanja optimalnih vrednosti temeljijo na biolo{kem
TL;DR: The performance of PSWWV algorithm and the solution quality prove that PSWV is highly competitive and can be considered as a viable alternative to solve optimization problems.
Journal Article•
TL;DR: Simulations and results indicate that the new bioinspired algorithm has better feasibility and validity for continuous space optimization and discrete space optimization.
Abstract: Inspired by social behavior of fireflies and the phenomenon of bioluminescent communication,firefly algorithm(FA) is developed as a novel bionic swarm intelligence optimization method.This paper analyzed the bionic principle of firefly algorithm and defined the mechanism of optimization by mathematics.Tested the FA by benchmark functions and combinatorial optimization instances.Simulations and results indicate that the new bioinspired algorithm has better feasibility and validity for continuous space optimization and discrete space optimization.
16 Jun 2011
TL;DR: This paper makes a comparison of the effectiveness of these three methods on a specific optimisation problem, specifically tuning the parameters of a PID controller.
Abstract: Evolutionary Computation (EC) is a recent and lively area of study. Some of the recent approaches within EC are particle Swarm Optimisation (PSO) and Differential Evolution (DE), while one of the latest to be developed is Firefly Algorithm (FA): all of which can be used in optimisation problems. This paper makes a comparison of the effectiveness of these three methods on a specific optimisation problem, specifically tuning the parameters of a PID controller.
11 Aug 2011
TL;DR: Stem Cells Algorithm is introduced, which is based on behavior of stem cells in reproducing themselves, which has high speed of convergence, low level of complexity with easy implementation process and also avoid the local minimums in an intelligent manner.
Abstract: Optimization algorithms have been proved to be good solutions for many practical applications. They were mainly inspired by natural evolutions. However, they are still faced to some problems such as trapping in local minimums, having low speed of convergence, and also having high order of complexity for implementation. In this paper, we introduce a new optimization algorithm, we called it Stem Cells Algorithm (SCA), which is based on behavior of stem cells in reproducing themselves. SCA has high speed of convergence, low level of complexity with easy implementation process. It also avoid the local minimums in an intelligent manner. The comparative results on a series of benchmark functions using the proposed algorithm related to other well-known optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) algorithm, ant colony optimization (ACO) algorithm and artificial bee colony (ABC) algorithm demonstrate the superior performance of the new optimization algorithm.
TL;DR: Experimental results show that this method proposed by this paper has stronger optimal ability and better global searching capability than PSO.
Abstract: Based on the problem of traditional particle swarm optimization (PSO) easily trapping into local optima, quantum theory is introduced into PSO to strengthen particles’ diversities and avoid the premature convergence effectively. Experimental results show that this method proposed by this paper has stronger optimal ability and better global searching capability than PSO.
Journal Article•
TL;DR: A Flexible Neural Tree (FNT) model for microarray data is constructed using Nature-inspired algorithms and is superior to the existing metaheuristic algorithm and solves multimodal optimization problems.
Abstract: Cancer classification based on microarray data is an important problem. Prediction models are used for classification which helps the diagnosis procedures to improve and aid in the physician’s effort. A hybrid swarm model for microarray data is proposed for performance evaluation based on Nature-inspired metaheuristic algorithms. Firefly Algorithm (FA) is the most powerful algorithms for optimization used for multimodal applications. In this paper a Flexible Neural Tree (FNT) model for microarray data is constructed using Nature-inspired algorithms. The FNT structure is developed using the Ant Colony Optimization (ACO) and the parameters embedded in the neural tree are optimized by Firefly Algorithm (FA). FA is superior to the existing metaheuristic algorithm and solves multimodal optimization problems. In this research, comparisons are done with the proposed model for evaluating its performance to find the appropriate model in terms of accuracy and error rate.
TL;DR: The functional similarities between Meta-heuristics and the aspects of the science of life (biology) are shown andTabu Search Algorithm (Classical Conditioning on alive beings), Simulated Annealing algorithm (temperature control of spiders), Particle Swarm Optimization algorithm (social behavior and movement dynamics of birds and fish) and Artificial Immune System (immunological mechanism of the vertebrates).
Abstract: In this paper, we show the functional similarities between Meta-heuristics and the aspects of the science of life (biology): (a) Meta-heuristics based on gene transfer: Genetic algorithms (natural evolution of genes in an organic population), Transgenic Algorithm (transfers of genetic material to another cell that is not descending); (b) Meta-heuristics based on interactions among individual insects: Ant Colony Optimization (on interactions among individuals insects, Ant Colonies), Firefly algorithm (fireflies of the family Lampyridze), Marriage in honey bees Optimization algorithm (the process of reproduction of Honey Bees), Artificial Bee Colony algorithm (the process of recollection of Honey Bees); and (c) Meta-heuristics based on biological aspects of alive beings: Tabu Search Algorithm (Classical Conditioning on alive beings), Simulated Annealing algorithm (temperature control of spiders), Particle Swarm Optimization algorithm (social behavior and movement dynamics of birds and fish) and Artificial Immune System (immunological mechanism of the vertebrates).
Journal Article•
TL;DR: A modified hybrid PSO algorithm that combines some principles of Particle Swarm Optimization and Crossover operation of the Genetic Algorithm is presented to prove the effectiveness of the proposed algorithm in dealing with NP-hard and combinatorial optimization problems.
Abstract: paper, a modified hybrid Particle Swarm Optimization (MHPSO) algorithm that combines some principles of Particle Swarm Optimization (PSO) and Crossover operation of the Genetic Algorithm (GA) is presented. Our contribution has a twofold aim: first, is to propose a new hybrid PSO algorithm. Second is to prove the effectiveness of the proposed algorithm in dealing with NP-hard and combinatorial optimization problems. In order to test and validate our algorithm, we have used it for solving the Multidimensional Knapsack Problem (MKP) which is a NP-hard combinatorial optimization problem. The experimental results based on some benchmarks from OR-Library, show a good and promise solution quality obtained by the proposed algorithm.
01 Jan 2011
TL;DR: In this paper, a Binary real coded firefly (BRCFF) algorithm for solving Security constrained unit commitment (SCUC) problem for the hybrid power system incorporating reliability constraints is presented.
Abstract: The strategies involved in scheduling generators and reliability management of power system is changing rapidly due to restructuring of power system. This paper critically reviews the reliability impacts of solar integrated thermal power plants and DR management. DR refers to the actions taken by the Independent system operator (ISO) to respond to a shortage of supply for a short duration of time, thereby maintaining the system reliability by scheduling spinning reserves. This paper presents a Binary real coded firefly (BRCFF) algorithm for solving Security constrained unit commitment (SCUC) problem for the hybrid power system incorporating reliability constraints. The Loss of load probability (LOLP) is the reliability index used for evaluating the system reliability level. The evaluation of the reliability index includes both unavailability of thermal generator units and unavailability of Solar cell generators (SCG). The proposed methodology is tested and validated on an IEEE RTS 24 bus system. (8 pages)
TL;DR: A hybrid particle swarm algorithm is proposed to minimize the makespan of job-shop scheduling problem which is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem.
Abstract: In this paper, a hybrid particle swarm algorithm is proposed to minimize the makespan of job-shop scheduling problem which is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. The new algorithm is based on the principle of particle swarm optimization (PSO). PSO as an evolutionary algorithm, it combines coarse global search capability (by neighboring experience) and local search ability. Simulated annealing (SA) as a neighborhood search algorithms, it has strong local search ability and can employ certain probability and can to avoid becoming trapped in a local optimum. Three neighborhood SA algorithms is designed and combined with PSO(called HPSO), for each best solution that particle find, SA is performed on it to find it’s best neighbor solution. The effectiveness and efficiency of HPSO are demonstrated by applying it to 43 benchmark job-shop scheduling problems. Comparison with other researcher’s results indicates that HPSO is a viable and effective approach for the job-shop scheduling problem.
12 Jun 2011
TL;DR: This paper proposes a hybrid algorithm to overcome the problem of diversity after environmental changes by applying a very interesting feature of the Fish School Search algorithm to the Particle Swarm Optimization algorithm, the collective volitive operator.
Abstract: Swarm Intelligence algorithms have been extensively applied to solve optimization problems. However, some of them, such as Particle Swarm Optimization, may not present the ability to generate diversity after environmental changes. In this paper we propose a hybrid algorithm to overcome this problem by applying a very interesting feature of the Fish School Search algorithm to the Particle Swarm Optimization algorithm, the collective volitive operator. We demonstrated that our proposal presents a better performance when compared to the FSS algorithm and some PSO variations in dynamic environments.