Showing papers on "Firefly algorithm published in 2014"

Nature-Inspired Optimization Algorithms

Abstract: Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference.Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm

A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load Forecasting

Abstract: Precise forecast of the electrical load plays a highly significant role in the electricity industry and market. It provides economic operations and effective future plans for the utilities and power system operators. Due to the intermittent and uncertain characteristic of the electrical load, many research studies have been directed to nonlinear prediction methods. In this paper, a hybrid prediction algorithm comprised of Support Vector Regression (SVR) and Modified Firefly Algorithm (MFA) is proposed to provide the short term electrical load forecast. The SVR models utilize the nonlinear mapping feature to deal with nonlinear regressions. However, such models suffer from a methodical algorithm for obtaining the appropriate model parameters. Therefore, in the proposed method the MFA is employed to obtain the SVR parameters accurately and effectively. In order to evaluate the efficiency of the proposed methodology, it is applied to the electrical load demand in Fars, Iran. The obtained results are compared with those obtained from the ARMA model, ANN, SVR-GA, SVR-HBMO, SVR-PSO and SVR-FA. The experimental results affirm that the proposed algorithm outperforms other techniques.

MPPT of PV Systems Under Partial Shaded Conditions Through a Colony of Flashing Fireflies

Abstract: This paper reports the development of a maximum power-point tracking (MPPT) method for photovoltaic (PV) systems under partially shaded conditions using firefly algorithm. The major advantages of the proposed method are simple computational steps, faster convergence, and its implementation on a low-cost microcontroller. The proposed scheme is studied for two different configurations of PV arrays under partial shaded conditions and its tracking performance is compared with traditional perturb and observe (P&O) method and particle swarm optimization (PSO) method under identical conditions. The improved performance of the algorithm in terms of tracking efficiency and tracking speed is validated through simulation and experimental studies.

Optimized gray-scale image watermarking using DWT-SVD and Firefly Algorithm

Abstract: This paper presents an optimized watermarking scheme based on the discrete wavelet transform (DWT) and singular value decomposition (SVD). The singular values of a binary watermark are embedded in singular values of the LL3 sub-band coefficients of the host image by making use of multiple scaling factors (MSFs). The MSFs are optimized using a newly proposed Firefly Algorithm having an objective function which is a linear combination of imperceptibility and robustness. The PSNR values indicate that the visual quality of the signed and attacked images is good. The embedding algorithm is robust against common image processing operations. It is concluded that the embedding and extraction of the proposed algorithm is well optimized, robust and show an improvement over other similar reported methods.

A Discrete Firefly Algorithm for the Multi-Objective Hybrid Flowshop Scheduling Problems

Abstract: Hybrid flowshop scheduling problems include the generalization of flowshops with parallel machines in some stages. Hybrid flowshop scheduling problems are known to be NP-hard. Hence, researchers have proposed many heuristics and metaheuristic algorithms to tackle such challenging tasks. In this letter, a recently developed discrete firefly algorithm is extended to solve hybrid flowshop scheduling problems with two objectives. Makespan and mean flow time are the objective functions considered. Computational experiments are carried out to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm outperforms many other metaheuristics in the literature.

Application of Firefly Algorithm for Load Frequency Control of Multi-area Interconnected Power System

Abstract: —In this article, a firefly algorithm is proposed for load frequency control of multi-area power systems. Initially a two equal area non-reheat thermal system is considered and the optimum gains of the proportional integral/proportional integral derivative controller are optimized employing the firefly algorithm technique. The superiority of the proposed approach is demonstrated by comparing the results with some recently published techniques such as genetic algorithm, bacteria foraging optimization algorithm, differential evolution, particle swarm optimization, hybrid bacteria foraging optimization algorithm-particle swarm optimization, and Ziegler–Nichols-based controllers for the same interconnected power system. Further, the proposed approach is extended to a three-unequal-area thermal system considering generation rate constraint and governor dead-band. Investigations reveal on comparison that proportional integral derivative controller provides much better response compared to integral and p...

Swarm intelligence based algorithms: a critical analysis

Abstract: Many optimization algorithms have been developed by drawing inspiration from swarm intelligence (SI). These SI-based algorithms can have some advantages over traditional algorithms. In this paper, we carry out a critical analysis of these SI-based algorithms by analyzing their ways to mimic evolutionary operators. We also analyze the ways of achieving exploration and exploitation in algorithms by using mutation, crossover and selection. In addition, we also look at algorithms using dynamic systems, self-organization and Markov chain framework. Finally, we provide some discussions and topics for further research.

Cuckoo Search and Firefly Algorithm

Abstract: Firefly algorithm (FA) was developed by Xin-She Yang in 2008, while cuckoo search (CS) was developed by Xin-She Yang and Suash Deb in 2009. Both algorithms have been found to be very efficient in solving global optimization problems. This chapter provides an overview of both cuckoo search and firefly algorithm as well as their latest developments and applications. We analyze these algorithms and gain insight into their search mechanisms and find out why they are efficient. We also discuss the essence of algorithms and its link to self-organizing systems. In addition, we also discuss important issues such as parameter tuning and parameter control, and provide some topics for further research.

Otsu based optimal multilevel image thresholding using firefly algorithm

Abstract: Histogram based multilevel thresholding approach is proposed using Brownian distribution (BD) guided firefly algorithm (FA). A bounded search technique is also presented to improve the optimization accuracy with lesser search iterations. Otsu's between-class variance function is maximized to obtain optimal threshold level for gray scale images. The performances of the proposed algorithm are demonstrated by considering twelve benchmark images and are compared with the existing FA algorithms such as Levy flight (LF) guided FA and random operator guided FA. The performance assessment comparison between the proposed and existing firefly algorithms is carried using prevailing parameters such as objective function, standard deviation, peak-to-signal ratio (PSNR), structural similarity (SSIM) index, and search time of CPU. The results show that BD guided FA provides better objective function, PSNR, and SSIM, whereas LF based FA provides faster convergence with relatively lower CPU time.

An improved firefly algorithm for solving dynamic multidimensional knapsack problems

Abstract: There is a wide range of publications reported in the literature, considering optimization problems where the entire problem related data remains stationary throughout optimization. However, most of the real-life problems have indeed a dynamic nature arising from the uncertainty of future events. Optimization in dynamic environments is a relatively new and hot research area and has attracted notable attention of the researchers in the past decade. Firefly Algorithm (FA), Genetic Algorithm (GA) and Differential Evolution (DE) have been widely used for static optimization problems, but the applications of those algorithms in dynamic environments are relatively lacking. In the present study, an effective FA introducing diversity with partial random restarts and with an adaptive move procedure is developed and proposed for solving dynamic multidimensional knapsack problems. To the best of our knowledge this paper constitutes the first study on the performance of FA on a dynamic combinatorial problem. In order to evaluate the performance of the proposed algorithm the same problem is also modeled and solved by GA, DE and original FA. Based on the computational results and convergence capabilities we concluded that improved FA is a very powerful algorithm for solving the multidimensional knapsack problems for both static and dynamic environments.

Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint.

Abstract: Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.

Cuckoo Search and Firefly Algorithm: Overview and Analysis

Abstract: Firefly algorithm (FA) was developed by Xin-She Yang in 2008, while cuckoo search (CS) was developed by Xin-She Yang and Suash Deb in 2009. Both algorithms have been found to be very efficient in solving global optimization problems. This chapter provides an overview of both cuckoo search and firefly algorithm as well as their latest developments and applications. We analyze these algorithms and gain insight into their search mechanisms and find out why they are efficient. We also discuss the essence of algorithms and its link to self-organizing systems. In addition, we also discuss important issues such as parameter tuning and parameter control, and provide some topics for further research.

Artificial Bee Colony Algorithm Hybridized with Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Selection Problem

Abstract: Portfolio selection (optimization) problem is a very important and widely rese arched problem in the areas of finance and economy. Literature review shows that many methods and heuristics were applied to this hard optimization problem, however, there are only few implementations of swarm intelligence metaheuristics. This paper presents artificial bee colony (ABC) algorithm applied to the cardinality constrained mean-variance (CCMV) portfolio optimization model. By analyzing ABC metaheuristic, some deficiencies such as slow convergence to the optimal region, were noticed. In this paper ABC algorithm improved by hybridization with the firefly algorithm (FA) is presented. FA's search procedure was incorporate d into the ABC algorithm to enhance the process of exploitation. We tested our proposed algorithm on standard test data used in the literature. Comparison with other state-of-the-art optimization metaheuristics including genetic algorithms, simulated annealing, tabu searc h and particle swarm optimization (PSO) shows that our approach is superior considering quality of the portfolio optimization results , especially mean Euclidean distance from the standard efficiency frontier.

A New Improved Firefly Algorithm for Global Numerical Optimization

Abstract: A novel robust meta-heuristic optimization algorithm, which can be considered as an improvement of the recently developed firefly algorithm, is proposed to solve global numerical optimization problems. The improvement includes the addition of information exchange between the top fireflies, or the optimal solutions during the process of the light intensity updating. The detailed implementation procedure for this improved meta-heuristic method is also described. Standard benchmarking functions are applied to verify the effects of these improvements and it is illustrated that, in most situations, the performance of this improved firefly algorithm (IFA) is superior to or at least highly competitive with the standard firefly algorithm, a differential evolution method, a particle swarm optimizer, and a biogeography-based optimizer. Especially, this new method can accelerate the global convergence speed to the true global optimum while preserving the main feature of the basic FA.

Optimum design of tower structures using Firefly Algorithm

Abstract: SUMMARY The Firefly Algorithm (FA) as a recent new meta-heuristic optimization algorithm is developed for determining optimum design of tower shaped structures. The FA mimics the social behavior of fireflies, which communicate, search for pray and find mates using bioluminescence with varied flashing patterns. In this paper, an adaptive FA is presented that utilizes the feasible-based method to handle constraints. This method is effective in improving the convergence and also suitable for expensive optimization tasks such as large-scale structures. Three tower structures are selected to evaluate the performance of the algorithm. The results are better than the other results proposed in the literature and confirm the validity of the proposed algorithm. Copyright © 2012 John Wiley & Sons, Ltd.

Cuckoo Search and Firefly Algorithm Applied to Multilevel Image Thresholding

Abstract: Multilevel image thresholding is a technique widely used in image processing, most often for segmentation. Exhaustive search is computationally prohibitively expensive since the number of possible thresholds to be examined grows exponentially with the number of desirable thresholds. Swarm intelligence metaheuristics have been used successfully for such hard optimization problems. In this chapter we investigate performance of two relatively new swarm intelligence algorithms, cuckoo search and firefly algorithm, applied to multilevel image thresholding. Particle swarm optimization and differential evolution algorithms have also been implemented for comparison. Two different objective functions, Kapur’s maximum entropy thresholding function and multi Otsu between-class variance, were used on standard benchmark images with known optima from exhaustive search (up to five threshold points). Results show that both, cuckoo search and firefly algorithm, exhibit superior performance and robustness.

Solution to automatic generation control problem using firefly algorithm optimized IλDµ controller

Abstract: Present work focused on automatic generation control (AGC) of a three unequal area thermal systems considering reheat turbines and appropriate generation rate constraints (GRC). A fractional order (FO) controller named as IλDµ controller based on crone approximation is proposed for the first time as an appropriate technique to solve the multi-area AGC problem in power systems. A recently developed metaheuristic algorithm known as firefly algorithm (FA) is used for the simultaneous optimization of the gains and other parameters such as order of integrator (λ) and differentiator (μ) of IλDµ controller and governor speed regulation parameters (R). The dynamic responses corresponding to optimized IλDµ controller gains, λ, μ, and R are compared with that of classical integer order (IO) controllers such as I, PI and PID controllers. Simulation results show that the proposed IλDµ controller provides more improved dynamic responses and outperforms the IO based classical controllers. Further, sensitivity analysis confirms the robustness of the so optimized IλDµ controller to wide changes in system loading conditions and size and position of SLP. Proposed controller is also found to have performed well as compared to IO based controllers when SLP takes place simultaneously in any two areas or all the areas. Robustness of the proposed IλDµ controller is also tested against system parameter variations.

Circular antenna array synthesis using firefly algorithm

Abstract: In this article, the design of circular antenna arrays CAAs and concentric circular antenna arrays CCAAs of isotropic radiators with optimum side lobe level SLL reduction is studied. The newly proposed global evolutionary optimization method; namely, the firefly algorithm FA is used to determine an optimum set of weights and positions for CAAs, and an optimum set of weights for CCAAs, that provides a radiation pattern with optimum SLL reduction with the constraint of a fixed major lobe beamwidth. The FA represents a new algorithm for optimization problems in electromagnetics. It is shown that the FA results provide a SLL reduction that is better than that obtained using well-known algorithms, like the particle swarm optimization, genetic algorithm GA, and evolutionary programming. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:139-146, 2014.

A hybrid discrete firefly algorithm for multi-objective flexible job shop scheduling problem with limited resource constraints

Abstract: In this paper, a hybrid discrete firefly algorithm is presented to solve the multi-objective flexible job shop scheduling problem with limited resource constraints. The main constraint of this scheduling problem is that each operation of a job must follow a process sequence and each operation must be processed on an assigned machine. These constraints are used to balance between the resource limitation and machine flexibility. Three minimisation objectives—the maximum completion time, the workload of the critical machine and the total workload of all machines—are considered simultaneously. In this study, discrete firefly algorithm is adopted to solve the problem, in which the machine assignment and operation sequence are processed by constructing a suitable conversion of the continuous functions as attractiveness, distance and movement, into new discrete functions. Meanwhile, local search method with neighbourhood structures is hybridised to enhance the exploitation capability. Benchmark problems are used to evaluate and study the performance of the proposed algorithm. The computational result shows that the proposed algorithm produced better results than other authors’ algorithms.