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 Improved Firefly Algorithm Based on Heuristic Information


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 Improved Firefly Algorithm Based on Heuristic Information
 CUI JiaruiLI QingYANG LiuyiWANG HengZHANG Boyu
(School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China)
 Keywords:firefly algorithm heuristic information global optimal Bayesian estimation numerical optimization
 Abstract:Firefly Algorithm (FA) is an optimization algorithm based on swarm intelligence which mimics the social behavior of fireflies based on the flashing and attraction characteristics of fireflies With the aim to address the disadvantages of the firefly algorithm of slow convergence speed and ease of falling into the local optimum in the later period of the evolution process, the firefly algorithm is improved herein Two kinds of heuristic information are proposed into the algorithm to guide the convergence of the algorithm The first one takes the current global best as the heuristic information referencing the “global optimal” idea in particle swarm optimization, therefore, an algorithm called FAGO (Firefly Algorithm based on Global Optimization) is formed The second one is called FABE (Firefly Algorithm based on Bayesian Estimation) using the optimal moving direction calculated by Bayesian estimation as heuristic information The improved algorithms in this study are applied to numerical simulations of several classical test functions and compared with traditional FA and some other′s research are carried out The simulation results show that the proposed algorithms can well accelerate the convergence speed and improve the convergence accuracy


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Last Update: 2019-03-26