A new hybrid firefly algorithm for complex and nonlinear problem

Afnizanfaizal Abdullah, Safaai Deris, Mohd Saberi Mohamad, Siti Zaiton Mohd Hashim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

76 Citations (Scopus)

Abstract

Global optimization methods play an important role to solve many real-world problems. However, the implementation of single methods is excessively preventive for high dimensionality and nonlinear problems, especially in term of the accuracy of finding best solutions and convergence speed performance. In recent years, hybrid optimization methods have shown potential achievements to overcome such challenges. In this paper, a new hybrid optimization method called Hybrid Evolutionary Firefly Algorithm (HEFA) is proposed. The method combines the standard Firefly Algorithm (FA) with the evolutionary operations of Differential Evolution (DE) method to improve the searching accuracy and information sharing among the fireflies. The HEFA method is used to estimate the parameters in a complex and nonlinear biological model to address its effectiveness in high dimensional and nonlinear problem. Experimental results showed that the accuracy of finding the best solution and convergence speed performance of the proposed method is significantly better compared to those achieved by the existing methods.

Original languageEnglish
Title of host publicationDistributed Computing and Artificial Intelligence - 9th International Conference
Pages673-680
Number of pages8
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event9th International Conference on Distributed Computing and Artificial Intelligence, DCAI 2012 - Salamanca, Spain
Duration: Mar 28 2012Mar 30 2012

Publication series

NameAdvances in Intelligent and Soft Computing
Volume151 AISC
ISSN (Print)1867-5662

Conference

Conference9th International Conference on Distributed Computing and Artificial Intelligence, DCAI 2012
Country/TerritorySpain
CitySalamanca
Period3/28/123/30/12

Keywords

  • biological model
  • Differential Evolution
  • Firefly Algorithm
  • hybrid optimization
  • parameter estimation

ASJC Scopus subject areas

  • General Computer Science

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