A hybrid bat algorithm
This work addresses optimization challenges for researchers and practitioners in computational intelligence, but it is incremental as it builds upon existing methods.
The authors tackled the problem of improving the bat algorithm for optimization by hybridizing it with differential evolution strategies, resulting in a new swarm intelligence algorithm that shows promising results on standard benchmark functions and significantly improves the original bat algorithm.
Swarm intelligence is a very powerful technique to be used for optimization purposes. In this paper we present a new swarm intelligence algorithm, based on the bat algorithm. The Bat algorithm is hybridized with differential evolution strategies. Besides showing very promising results of the standard benchmark functions, this hybridization also significantly improves the original bat algorithm.