ABSTRACT
Hybrid algorithms aim to mix features from two or more evolutionary/
swarm algoprove both the exploration and exploitation
abilities of the algorithm. Generally, hybrid algorithms prrithms
to imesent the same quality of solution than the canonical ones,
in the worst case scenario. However, it is common that hybrid
algorithms present better outcomes than the canonical ones. In
this context, this paper proposes a cooperative hybrid algorithm
based on Harmony Search and Differential Evolution named HS-DE.
The algorithm has been tested in five benchmark functions well
known in the literature. Results have shown that HS-DE presents
better solutions than Genetic Algorithms, Particle Swarm Optimization,
Differential Evolution, and Harmony Search in all benchmark
functions.