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Meta-heuristic and Evolutionary Algorithms for Engineering Optimization
book

Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

by Omid Bozorg-Haddad, Mohammad Solgi, Hugo A. Loáiciga
October 2017
Intermediate to advanced content levelIntermediate to advanced
304 pages
8h 3m
English
Wiley
Content preview from Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

16 Firefly Algorithm

Summary

This chapter describes the firefly algorithm (FA), which is inspired by the flashing powers of fireflies. It is a meta‐heuristic optimization algorithm. This chapter presents in sequence a brief literature review of the FA and its applications, the characteristics of fireflies and their mapping to the FA, a detailed description of the FA, and a pseudocode of the FA.

16.1 Introduction

Yang (2008) introduced the firefly algorithm (FA) and applied it to solve several optimization test problems whose results compared favorably with the genetic algorithm (GA) and particle swarm optimization (PSO) (Yang, 2009). Yang (2010) merged the Levy flight (LF) approach searching with the FA and solved several optimization test problems by applying the proposed hybrid algorithm. The results indicated that the success rate of the Levy flight FA (LFA) was better than that of the standard FA. Yang (2011) applied chaos theory for auto‐tuning of the parameters of the algorithm. The results of the cited study compared favorably with those of the standard FA for the well‐known problem of the welded beam. Yan et al. (2012) developed an adaptive FA (AFA) to upgrade the FA’s capability in solving large dimensional. The latter authors showed that the AFA performed better with several test problems than the standard FA, differential evolution (DE), and PSO. Many studies have been devoted to improving the searching accuracy of the FA and have shown its better convergence ...

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Publisher Resources

ISBN: 9781119386995Purchase book