A hybrid bat algorithm
Iztok Fister Jr., Du\v{s}an Fister, Xin-She Yang
TL;DR
This paper introduces a hybrid optimization algorithm combining the bat algorithm with differential evolution, demonstrating improved performance on benchmark functions and advancing swarm intelligence techniques.
Contribution
It presents a novel hybridization of the bat algorithm with differential evolution, enhancing optimization capabilities over existing swarm intelligence methods.
Findings
Significant performance improvements on benchmark functions
Effective hybridization of bat algorithm with differential evolution
Potential for broader application in optimization problems
Abstract
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.
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications · Neural Networks and Applications