A comprehensive review of firefly algorithms

نویسندگان

  • Iztok Fister
  • Iztok Fister
  • Xin-She Yang
  • Janez Brest
چکیده

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. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations. Citations details: I. Fister, I. Fister Jr., X.-S. Yang, and J. Brest, A comprehensive review of firefly algorithms, Swarm and Evolutionary Computation, vol. 13, pp. 34-46, 2013.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A Quaternion Firefly Algorithm to Solve a Multi-row Facility Layout Problem (RESEARCH NOTE)

In this paper, a quaternion firefly algorithm is utilized to solve a multi-row facility layout design problem with the objective of minimizing the total cost of transportation. A quaternion firefly algorithm takes the motion of the firefly as a quaternion one. Consequently, the solution space is explored more accurately and the answers are of higher quality. That is, the answers are considerabl...

متن کامل

A Hybrid Algorithm using Firefly, Genetic, and Local Search Algorithms

In this paper, a hybrid multi-objective algorithm consisting of features of genetic and firefly algorithms is presented. The algorithm starts with a set of fireflies (particles) that are randomly distributed in the solution space; these particles converge to the optimal solution of the problem during the evolutionary stages. Then, a local search plan is presented and implemented for searching s...

متن کامل

OPTIMAL DESIGN OF TRUSS STRUCTURES BY IMPROVED MULTI-OBJECTIVE FIREFLY AND BAT ALGORITHMS

The main aim of the present paper is to propose efficient multi-objective optimization algorithms (MOOAs) to tackle truss structure optimization problems. The proposed meta-heuristic algorithms are based on the firefly algorithm (FA) and bat algorithm (BA), which have been recently developed for single-objective optimization. In order to produce a well distributed Pareto front, some improvement...

متن کامل

A HYBRID CHARGED SYSTEM SEARCH - FIREFLY ALGORITHM FOR OPTIMIZATION OF WATER DISTRIBUTION NETWORKS

Water distribution networks are one of the important and costly infrastructures of cities and many meta-heuristic algorithms in standard or hybrid forms were used for optimizing water distribution networks. These algorithms require a large amount of computational cost. Therefore, the converging speed of algorithms toward the optimization goal is as important as the goal itself. In this paper, a...

متن کامل

Hybridization of Cuckoo Search and Firefly Algorithms to Calculate the Interaction Parameters in Phase Equilibrium Modeling Problems

Liquid-liquid equilibrium (LLE) problems such as phase stability analysis, phase equilibrium calculations, chemical equilibrium calculations, binary interaction parameter identification of thermodynamic models and other problems of fluid characterization have been the core subject of many recent studies. This study introduces Cuckoo Search (CS), Firefly Algorithms (FA) and its variants as p...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Swarm and Evolutionary Computation

دوره 13  شماره 

صفحات  -

تاریخ انتشار 2013