Showing papers on "Firefly algorithm published in 2010"

A New Metaheuristic Bat-Inspired Algorithm

Abstract: Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed.

A New Metaheuristic Bat-Inspired Algorithm

Abstract: Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed.

Firefly algorithm, stochastic test functions and design optimisation

Abstract: Modern optimisation algorithms are often metaheuristic, and they are very promising in solving NP-hard optimisation problems. In this paper, we show how to use the recently developed firefly algorithm to solve non-linear design problems. For the standard pressure vessel design optimisation, the optimal solution found by FA is far better than the best solution obtained previously in the literature. In addition, we also propose a few new test functions with either singularity or stochastic components but with known global optimality and thus they can be used to validate new optimisation algorithms. Possible topics for further research are also discussed.

Firefly Algorithm, Stochastic Test Functions and Design Optimisation

Abstract: Modern optimisation algorithms are often metaheuristic, and they are very promising in solving NP-hard optimization problems. In this paper, we show how to use the recently developed Firefly Algorithm to solve nonlinear design problems. For the standard pressure vessel design optimisation, the optimal solution found by FA is far better than the best solution obtained previously in literature. In addition, we also propose a few new test functions with either singularity or stochastic components but with known global optimality, and thus they can be used to validate new optimisation algorithms. Possible topics for further research are also discussed.

Firefly Algorithm, Lévy Flights and Global Optimization

Abstract: Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Levy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Levy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed.

Analysis of Particle Swarm Optimization Algorithm

Abstract: Particle swarm optimization is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. The algorithm is widely used and rapidly developed for its easy implementation and few particles required to be tuned. The main idea of the principle of PSO is presented; the advantages and the shortcomings are summarized. At last this paper presents some kinds of improved versions of PSO and research situation, and the future research issues are also given.

Nature-Inspired Metaheuristic Algorithms: Second Edition

Abstract: Modern metaheuristic algorithms such as particle swarm optimization and cuckoo search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms for global optimization, including ant and bee algorithms, bat algorithm, cuckoo search, differential evolution, firefly algorithm, genetic algorithms, harmony search, particle swarm optimization, simulated annealing and support vector machines. In this revised edition, we also include how to deal with nonlinear constraints. Worked examples with implementation have been used to show how each algorithm works. This book is thus an ideal textbook for an undergraduate and/or graduate course as well as for self study. As some of the algorithms such as the cuckoo search and firefly algorithms are at the forefront of current research, this book can also serve as a reference for researchers.

Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic Optimization

Abstract: Most global optimization problems are nonlinear and thus difficult to solve, and they become even more challenging when uncertainties are present in objective functions and constraints. This paper provides a new two-stage hybrid search method, called Eagle Strategy, for stochastic optimization. This strategy intends to combine the random search using Levy walk with the firefly algorithm in an iterative manner. Numerical studies and results suggest that the proposed Eagle Strategy is very efficient for stochastic optimization. Finally practical implications and potential topics for further research will be discussed.

Constrained particle swarm optimization using a bi-objective formulation

Abstract: This paper introduces an approach for dealing with constraints when using particle swarm optimization. The constrained, single objective optimization problem is converted into an unconstrained, bi-objective optimization problem that is solved using a multi-objective implementation of the particle swarm optimization algorithm. A specialized bi-objective particle swarm optimization algorithm is presented and an engineering example problem is used to illustrate the performance of the algorithm. An additional set of 13 test problems from the literature is used to further validate the performance of the newly proposed algorithm. For the example problems considered here, the proposed algorithm produced promising results, indicating that it is an approach that deserves further consideration. The newly proposed algorithm provides performance similar to that of a tuned penalty function approach, without having to tune any penalty parameters.

Collaborative particle swarm optimization with a data mining technique for manufacturing cell design

Abstract: In recent years, different metaheuristic methods have been used to solve clustering problems. This paper addresses the problem of manufacturing cell formation using a modified particle swarm optimization (PSO) algorithm. The main modification that this work made to the original PSO algorithm consists in not using the vector of velocities that the standard PSO algorithm does. The proposed algorithm uses the concept of proportional likelihood with modifications, a technique that is used in data mining applications. Some simulation results are presented and compared with results from literature. The criterion used to group the machines into cells is based on the minimization of intercell movements. The computational results show that the PSO algorithm is able to find the optimal solutions in almost all instances, and its use in machine grouping problems is feasible.

Eagle Strategy Using L\'evy Walk and Firefly Algorithms For Stochastic Optimization

Abstract: Most global optimization problems are nonlinear and thus difficult to solve, and they become even more challenging when uncertainties are present in objective functions and constraints. This paper provides a new two-stage hybrid search method, called Eagle Strategy, for stochastic optimization. This strategy intends to combine the random search using L\'evy walk with the firefly algorithm in an iterative manner. Numerical studies and results suggest that the proposed Eagle Strategy is very efficient for stochastic optimization. Finally practical implications and potential topics for further research will be discussed.

A New Particle Swarm Optimization Algorithm for Dynamic Environments

Abstract: Many real world optimization problems are dynamic in which global optimum and local optima change over time. Particle swarm optimization has performed well to find and track optima in dynamic environments. In this paper, we propose a new particle swarm optimization algorithm for dynamic environments. The proposed algorithm utilizes a parent swarm to explore the search space and some child swarms to exploit promising areas found by the parent swarm. To improve the search performance, when the search areas of two child swarms overlap, the worse child swarms will be removed. Moreover, in order to quickly track the changes in the environment, all particles in a child swarm perform a random local search around the best position found by the child swarm after a change in the environment is detected. Experimental results on different dynamic environments modelled by moving peaks benchmark show that the proposed algorithm outperforms other PSO algorithms, including FMSO, a similar particle swarm algorithm for dynamic environments, for all tested environments.

Multilevel Image Thresholding Selection Based on the Firefly Algorithm

Abstract: The multilevel thresholding is an important technique for image processing and pattern recognition. The maximum entropy thresholding has been widely applied in the literature. In this paper, a new multilevel MET algorithm based on the technology of the firefly algorithm is proposed. This proposed method is called the maximum entropy based firefly thresholding method. Four different methods are implemented for comparing to this proposed method: the exhaustive search, the particle swarm optimization, the hybrid cooperative-comprehensive learning based PSO algorithm and the honey bee mating optimization. The experimental results demonstrated that the proposed MEFFT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. Compared to the PSO and HCOCLPSO, the segmentation results of using the MEFFT algorithm is significantly improved and the computation time of the proposed MEFFT algorithm is shortest.

PSOLVER: A new hybrid particle swarm optimization algorithm for solving continuous optimization problems

Abstract: This study deals with a new hybrid global-local optimization algorithm named PSOLVER that combines particle swarm optimization (PSO) and a spreadsheet ''Solver'' to solve continuous optimization problems. In the hybrid PSOLVER algorithm, PSO and Solver are used as the global and local optimizers, respectively. Thus, PSO and Solver work mutually by feeding each other in terms of initial and sub-initial solution points to produce fine initial solutions and avoid from local optima. A comparative study has been carried out to show the effectiveness of the PSOLVER over standard PSO algorithm. Then, six constrained and three engineering design problems have been solved and obtained results are compared with other heuristic and non-heuristic solution algorithms. Identified results demonstrate that, the hybrid PSOLVER algorithm requires less iterations and gives more effective results than other heuristic and non-heuristic solution algorithms.

Unit commitment in deregulated power system using Lagrangian firefly algorithm

Abstract: Profit Unit commitment in deregulated power system has a different objective than that of traditional unit commitment PBUC has been performed to emphasize the importance of the profit. The distinct feature of PBUC is that all market information is reflected in the market price. This paper presents a solution for profit based unit commitment by the use of Lagrangian firefly algorithm. The profit based unit commitment (PBUC) solution is developed to determine the unit ON/OFF schedule, power, spinning and non-spinning reserve generations ofGENCOs taking part in the competition. The resultant schedule evidently maximizes the profit and the proposed algorithm is tested for a small unit test system with 3 unit 4 hour data and the simulations are carried out to show the performance of proposed methodology using MATLAB. The proposed algorithm can be extended to 'n' number of generating units.

Experimental study on a hybrid nature-inspired algorithm for financial portfolio optimization

Abstract: Hybrid intelligent schemes have proven their efficiency in solving NP-hard optimization problems Portfolio optimization refers to the problem of finding the optimal combination of assets and their corresponding weights which satisfies a specific investment goal and various constraints In this study, a hybrid intelligent metaheuristic, which combines the Ant Colony Optimization algorithm and the Firefly algorithm, is proposed in tackling a complex formulation of the portfolio management problem The objective function under consideration is the maximization of a financial ratio which combines factors of risk and return At the same time, a hard constraint, which refers to the tracking ability of the constructed portfolio towards a benchmark stock index, is imposed The aim of this computational study is twofold Firstly, the efficiency of the hybrid scheme is highlighted Secondly, comparison results between alternative mechanisms, which are incorporated in the main function of the hybrid scheme, are presented.

Eagle strategy using Lévy walk and firefly algorithm for stochastic optimization

Abstract: Most global optimization problems are nonlinear and thus difficult to solve, and they become even more challenging when uncertainties are present in objective functions and constraints. This paper provides a new two-stage hybrid search method, called Eagle Strategy, for stochastic optimization. This strategy intends to combine the random search using L´evy walk with the firefly algorithm in an iterative manner. Numerical studies and results suggest that the proposed Eagle Strategy is very efficient for stochas- tic optimization. Finally practical implications and potential topics for further research will be discussed.

The codebook design of image vector quantization based on the firefly algorithm

Abstract: The vector quantization (VQ) was a powerful technique in the applications of digital image compression. The traditionally widely used method such as the Linde-Buzo-Gray (LBG) algorithm always generated local optimal codebook. This paper proposed a new method based on the firefly algorithm to construct the codebook of vector quantization. The proposed method uses LBG method as the initial of firefly algorithm to develop the VQ algorithm. This method is called FF-LBG algorithm. The FF-LBG algorithm is compared with the other three methods that are LBG, PSO-LBG and HBMO-LBG algorithms. Experimental results showed that the computation of this proposed FF-LBG algorithm is faster than the PSO-LBG, and the HBMO-LBG algorithms. Furthermore, the reconstructured images get higher quality than those generated from the LBG and PSO-LBG algorithms, but there are not significantly different to the HBMO-LBG algorithm.

Firefly Algorithms for Multimodal Optimization

Abstract: Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms. Finally we will discuss its applications and implications for further research.

Firefly Algorithm, Levy Flights and Global Optimization

Abstract: Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Levy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Levy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed.

Research on Particle Swarm Optimization Algorithm

Abstract: Particle swarm optimization algorithm is put forward according to the simulation of migration of bird flight their food-searching and the group model,and is a novel stochastic optimization algorithm which can use to solve optimization problems. The models of bird flocking and swarm actions are firstly introduced,and the fundamentals characteristics and the working mechanisms of PSO algorithm are also analyzed. Then this paper introduces the optimization strategy of particle swarm optimization including improve the convergence rate,discrete algorithms,improve overall group diversity. Finally,some suggestion on future trends and existing problems related to PSO algorithm are discussed and concluded.

Fault-tolerant averaging for self-organizing synchronization in wireless ad hoc networks

Abstract: This paper presents a self-organizing robust clock synchronization algorithm based on the Reachback Firefly Algorithm, which is tailored for the use in wireless networks. We adapt a fault-tolerant algorithm from wired networks to cope with nodes deliberately feeding faulty clock readings into the system. The presented algorithm achieves a tight alignment of the firing phases of the non-faulty nodes, which supports duty cycling, communication scheduling, and time synchronization. Results show that the algorithm can cope with up to 1/5 non-silent faulty nodes.

An Improved Particle Swarm Optimization Algorithm

Abstract: Based on the research results published in existing relevant references,the basic principles of the standard particle swarm optimization(PSO) algorithm are elaborated and analyzed.To the shortcomings of the standard particle swarm optimization algorithm such as the slow speed of selecting the initial particle populations and the local optimum in the optimization process,an improved PSO algorithm is presented.The comparison between the improved PSO algorithm and the standard PSO algorithm through the experimental analysis show that,the improved PSO algorithm is apparently better than the standard PSO algorithm both in the convergence speed and convergence precision.

Firefly Algorithms for Multimodal Optimization

Abstract: Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms. Finally we will discuss its applications and implications for further research.

Hybrid Optimization Algorithm of PSO and AFSA

Abstract: This paper proposes a hybrid algorithm of Particle Swarm Optimization(PSO) and Artificial Fish Swarm Algorithm(AFSA) by combining the advantages of PSO algorithm and AFSA.Hybrid algorithm divides the swarm into two sub-groups.In each iteration,one sub-group evolves using PSO algorithm,the other sub-group evolves using AFSA,and two algorithms share the information of groups extremum.Through comparing PSO-AFSA hybrid algorithm with standard PSO algorithm in evolving solution to five standard functions,results show that PSO-AFSA hybrid algorithm outperforms PSO algorithm.

Research of Particle Swarm Optimization algorithm based on Nelder-Mead simplex and its application on partial discharge parameter recognition

Abstract: Particle Swarm Optimization (PSO), firstly presented in 1995, is mainly used in high-dimension optimization. Despite its wide use, PSO have a disadvantage of prematurity. This paper put forward a new Particle Swarm Optimization algorithm based on Nelder-Mead simplex algorithm to overcome the PSO's nature of prematurity and precision problems. Nelder-Mead simplex algorithm is hybrid into the process of PSO. The test results of high dimensional Griewank function show that this novel algorithm is efficient to solve high-dimension optimization problem with a balance of convergence and precision. Finally, an example of partial discharge parameter recognition shows this novel algorithm have advantage to solve these type of problem.