A Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Global Optimization

In this p aper, a new and an effe ctive com bination of two metaheuristic algorithms, na mely Firefly Algorithm a nd the D ifferentia l evolution, has been propo sed. Thiis hybridization called as HFAD E, consis ts of tw o phases of Differential E volution (DE) and Firefly Algorithm (FA). Firefly alg orithm i s the nature- inspir ed algorithm whi ch has its roots in the lig ht intens ity attra ction process of firefly in the nature. Differential evolution is an Evolutionary Algor ithm that u ses the evolutionary operators like selection, recombination and mutation . FA and D E together are effective andd powerful algorithms but F A algorith m dependds on randoom directiions for search which led into retardation in finding thee best solution and DE needs more iteeration to find proper solution. As a result, this proposed methood has been designed to cover each a lgorithm deficiencies so as to make them more suitable for optimization in real wor ld domainn. To obta in the re quired ressults, the experiment on a set of bench mark functions was performed and fin dings sh owed that HFADE is a more preferable and effective method in solving the high-dimennsional functions.

Keywords:

Differential Evolution , Firefly Algorithm , Global Optimization , Hybrid Algorithm