On a Cooperative Hybrid Algorithm Based on Harmony Search and Differential Evolution for Numerical Optimization

Abstract

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