A Hybrid Firefly Algorithm for Continuous Optimization Problems
The search behavior of firefly algorithm (FA) is determined by the attractions among fireflies. In the standard FA and its most modifications, worse fireflies can move toward other better ones, while better fireflies seldom move to other positions. To enhance the search of better fireflies, this paper presents a hybrid firefly algorithm (HFA) Which employs a local search operator inspired by differential evolution (DE) Moreover, the control parameters are dynamically adjusted during the search process. Experiments are conducted on thirteen continuous optimization problems. Computational results show that HFA achieves better solutions than the standard FA and three other improved FA variants.
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