Hybrid Genetic Firefly Algorithm for Global Optimization Problems



Global Optimization is an active area of research for the variety of optimization problems that are frequently arising in network design and operation, finance, supply chain management, scheduling, and many other areas. In the last few years, different types of evolutionary algorithms (EAs) have been proposed for solving and analyzing the properties of diverse types of optimization problems. EAs work with a set of random solutions called population and find a set of optimal solutions for the problems at hand in a single simulation run opponent to traditional optimization methods. Among the stochastic based algorithms, genetic algorithm (GA) is one of the most popular and frequently used stochastic based meta-heuristic inspired by natural evolution. The premature convergence, genetic drift and trapping in the local basin attraction are their major drawbacks. These issues can be overcome by hybridizing GA with some efficient local search optimizers and different search operators. In this paper, we have proposed hybrid GA by employing the Firefly Algorithm (FA) as search operator aiming at to improve the searching ability of the baseline GA. The performance of the suggested hybrid genetic firefly algorithm (HGFA) is hereby evaluated by using 24 benchmark functions which was designed for the special session of the 2005 IEEE Congress on Evolutionary Computation (CEC'05). The numerical results provided by HGFA are summarized in the numerical form such as best, mean and standard deviation by executing 25 times independently with different random seeds to solve each test problem. The suggested HGFA have tackled most of the used test problems with good convergence speed as compared to the stand alone Genetic Algorithm.

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