scholarly journals Modified Firefly Algorithm

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Surafel Luleseged Tilahun ◽  
Hong Choon Ong
Keyword(s):  
Objective Function ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Metaheuristic Algorithms ◽  
Current Position ◽  
Random Movement ◽  
Cpu Time ◽  
Modified Firefly Algorithm ◽  
Better Than

Firefly algorithm is one of the new metaheuristic algorithms for optimization problems. The algorithm is inspired by the flashing behavior of fireflies. In the algorithm, randomly generated solutions will be considered as fireflies, and brightness is assigned depending on their performance on the objective function. One of the rules used to construct the algorithm is, a firefly will be attracted to a brighter firefly, and if there is no brighter firefly, it will move randomly. In this paper we modify this random movement of the brighter firefly by generating random directions in order to determine the best direction in which the brightness increases. If such a direction is not generated, it will remain in its current position. Furthermore the assignment of attractiveness is modified in such a way that the effect of the objective function is magnified. From the simulation result it is shown that the modified firefly algorithm performs better than the standard one in finding the best solution with smaller CPU time.

scholarly journals Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the ‘Rush to Heuristics’

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5097
Author(s):  
Gianfranco Chicco ◽  
Andrea Mazza
Keyword(s):  
Global Optimization ◽  
Specific Problem ◽  
Optimization Problems ◽  
Energy Systems ◽  
Progressive Increase ◽  
Metaheuristic Algorithms ◽  
Metaheuristic Algorithm ◽  
Power And Energy Systems ◽  
Power And Energy ◽  
Better Than

In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods that are based on weak comparisons. This ‘rush to heuristics’ does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems and aims at providing a comprehensive view of the main issues that concern the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems.

scholarly journals The Median Solution of the Newsvendor Problem and Some Observations

2015 ◽  
Vol 6 (3) ◽  
pp. 55-60
Author(s):  
Pritibhushan Sinha
Keyword(s):  
Standard Deviation ◽  
Theoretical Analysis ◽  
Numerical Experiment ◽  
Objective Function ◽  
Optimization Problems ◽  
Newsvendor Problem ◽  
Expected Profit ◽  
Random Optimization ◽  
Better Than

Abstract We consider the median solution of the Newsvendor Problem. Some properties of such a solution are shown through a theoretical analysis and a numerical experiment. Sometimes, though not often, median solution may be better than solutions maximizing expected profit, or maximizing minimum possible, over distribution with the same average and standard deviation, expected profit, according to some criteria. We discuss the practical suitability of the objective function set and the solution derived, for the Newsvendor Problem, and other such random optimization problems.

Firefly Algorithm for Optimization Problem

2013 ◽  
Vol 421 ◽  
pp. 512-517 ◽  
Author(s):  
Nur Farahlina Johari ◽  
Azlan Mohd Zain ◽  
Mustaffa H. Noorfa ◽  
Amirmudin Udin
Keyword(s):  
Computer Science ◽  
Firefly Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Metaheuristic Algorithms ◽  
Science And Engineering ◽  
Multi Objective

This paper reviews the applications of Firefly Algorithm (FA) in various domain of optimization problem. Optimization is a process of determining the best solution to make something as functional and effective as possible by minimizing or maximizing the parameters involved in the problems. Several categories of optimization problem such as discrete, chaotic, multi-objective and many more are addressed by inspiring the behavior of fireflies as mentioned in the literatures. Literatures found that FA was mostly applied by researchers to solve the optimization problems in Computer Science and Engineering domain. Some of them are enhanced or hybridized with other techniques to discover better performance. In addition, literatures found that most of the cases that used FA technique have outperformed compare to other metaheuristic algorithms.

scholarly journals A New Modified Firefly Algorithm

2016 ◽  
Vol 4 (2) ◽  
pp. 18 ◽  
Author(s):  
Medha Gupta ◽  
Divya Gupta
Keyword(s):  
Firefly Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Collective Intelligence ◽  
Metaheuristic Algorithm ◽  
Numerical Studies ◽  
Modified Firefly Algorithm

<p class="Abstract"><span lang="EN-GB">Nature inspired meta-heuristic algorithms studies the emergent collective intelligence of groups of simple agents. </span><span lang="EN-AU">Firefly Algorithm is one of the new such swarm-based metaheuristic algorithm inspired by the flashing behavior of fireflies. The algorithm was first proposed in 2008 and since then has been successfully used for solving various optimization problems. In this work, we intend to propose a new modified version of Firefly algorithm (MoFA) and later its performance is compared with the standard firefly algorithm along with various other meta-heuristic algorithms. Numerical studies and results demonstrate that the proposed algorithm is superior to existing algorithms.</span></p>

scholarly journals Enhanced Firefly Algorithm Using Fuzzy Parameter Tuner

2018 ◽  
Vol 11 (1) ◽  
pp. 26 ◽  
Author(s):  
Mahdi Bidar ◽  
Samira Sadaoui ◽  
Malek Mouhoub ◽  
Mohsen Bidar
Keyword(s):  
Firefly Algorithm ◽  
Optimization Problems ◽  
Metaheuristic Algorithms ◽  
Exploration And Exploitation ◽  
Improved Firefly Algorithm ◽  
Engineering Problems ◽  
Exploitation And Exploration ◽  
Low Dimensional ◽  
Fuzzy Parameter

Exploitation and exploration are two main search strategies of every metaheuristic algorithm. However, the ratio between exploitation and exploration has a significant impact on the performance of these algorithms when dealing with optimization problems. In this study, we introduce an entire fuzzy system to tune efficiently and dynamically the firefly algorithm parameters in order to keep the exploration and exploitation in balance in each of the searching steps. This will prevent the firefly algorithm from being stuck in local optimal, a challenge issue in metaheuristic algorithms. To evaluate the quality of the solution returned by the fuzzy-based firefly algorithm, we conduct extensive experiments on a set of high and low dimensional benchmark functions as well as two constrained engineering problems. In this regard, we compare the improved firefly algorithm with the standard one and other famous metaheuristic algorithms. The experimental results demonstrate the superiority of the fuzzy-based firefly algorithm to standard firefly and also its comparability to other metaheuristic algorithms.

scholarly journals Comparison between genetic algorithm and prey-predator algorithm

2014 ◽  
Vol 9 (4) ◽  
Author(s):  
Surafel Luleseged Tilahun ◽  
Hong Choon Ong
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Metaheuristic Algorithms ◽  
Benchmark Problems ◽  
Test Problems ◽  
Search Mechanism ◽  
Complex Optimization ◽  
Better Than

Metaheuristic algorithms are useful in solving complex optimization problems. Genetic algorithm (GA) is one of the well known and oldest metaheuristic algorithms. It was introduced in 1975 and has been used in many applications varying from engineering to management and many other fields as well. However, Prey-Predator algorithm (PPA) is one of recently introduced algorithm, in 2012, inspired by the interaction between preys and their predator. The motivation and the search mechanism for these two algorithms are different. In this paper the comparison of these two algorithms both from theoretical aspects and using simulation on selected benchmark problems is presented. According to the results, PPA performs better than GA in the selected test problems.

A Computationally Efficient Objective Function for the Evaluation of the Natural Frequency in Structural Modification and Optimization Problems

1997 ◽  
Vol 119 (4) ◽  
pp. 618-623
Author(s):  
Tien-Sheng Chang ◽  
E. B. Magrab
Keyword(s):  
Objective Function ◽  
Natural Frequency ◽  
Structural Modification ◽  
Optimization Problems ◽  
Computationally Efficient ◽  
Original Design ◽  
Additional Advantage ◽  
Cpu Time ◽  
Design Changes ◽  
Modification Technique

A highly computationally efficient objective function for measuring the change in the natural frequency when a structure is modified is introduced. When the optimization of an orthotropic plate is studied, a decrease in CPU time by at least a factor of 40 is obtained when the new objective function is compared to that derived from the minor structural modification technique. The greater the number of finite elements affected by the design changes the higher the factor of improvement will be. This new objective function has the additional advantage that it can be used for substantial modifications to the original design.

scholarly journals Otsu Based Optimal Multilevel Image Thresholding Using Firefly Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
N. Sri Madhava Raja ◽  
V. Rajinikanth ◽  
K. Latha
Keyword(s):  
Objective Function ◽  
Firefly Algorithm ◽  
Search Time ◽  
Structural Similarity ◽  
Threshold Level ◽  
Random Operator ◽  
Multilevel Thresholding ◽  
Search Technique ◽  
Signal Ratio ◽  
Cpu Time

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 Lévy 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.

Scalable Computing Architecture for Time-Dependent Transportation Optimization Problems Based on High-Performance Computing Techniques

2019 ◽  
Vol 2673 (4) ◽  
pp. 205-216 ◽  
Author(s):  
Pengfei (Taylor) Li ◽  
Peirong (Slade) Wang ◽  
Farzana Chowdhury ◽  
Li Zhang
Keyword(s):  
Objective Function ◽  
High Performance Computing ◽  
High Performance ◽  
Optimization Problems ◽  
Dynamic Network ◽  
Alternative Formulation ◽  
High Fidelity ◽  
Objective Functions ◽  
Performance Computing ◽  
Transportation Optimization

Traditional formulations for transportation optimization problems mostly build complicating attributes into constraints while keeping the succinctness of objective functions. A popular solution is the Lagrangian decomposition by relaxing complicating constraints and then solving iteratively. Although this approach is effective for many problems, it generates intractability in other problems. To address this issue, this paper presents an alternative formulation for transportation optimization problems in which the complicating attributes of target problems are partially or entirely built into the objective function instead of into the constraints. Many mathematical complicating constraints in transportation problems can be efficiently modeled in dynamic network loading (DNL) models based on the demand–supply equilibrium, such as the various road or vehicle capacity constraints or “IF–THEN” type constraints. After “pre-building” complicating constraints into the objective functions, the objective function can be approximated well with customized high-fidelity DNL models. Three types of computing benefits can be achieved in the alternative formulation: ( a) the original problem will be kept the same; ( b) computing complexity of the new formulation may be significantly reduced because of the disappearance of hard constraints; ( c) efficiency loss on the objective function side can be mitigated via multiple high-performance computing techniques. Under this new framework, high-fidelity and problem-specific DNL models will be critical to maintain the attributes of original problems. Therefore, the authors’ recent efforts in enhancing the DNL’s fidelity and computing efficiency are also described in the second part of this paper. Finally, a demonstration case study is conducted to validate the new approach.

Sign in / Sign up

Export Citation Format

Share Document