Affiliations: Department of Electronics and Telecommunication
Engineering, Jadavpur University, Kolkata, India | Machine Intelligence Research Labs (MIR Labs),
Scientific Network for Innovation and Research Excellence, P.O. Box 2259 Auburn,
Washington 98071-2259, USA. E-mail: ajith.abraham@ieee.org
Abstract: Harmony Search (HS) is a recently developed stochastic algorithm
which imitates the music improvisation process. In this process, the musicians
improvise their instrument pitches searching for the perfect state of harmony.
Practical experiences, however, suggest that the algorithm suffers from the
problems of slow and/or premature convergence over multimodal and rough fitness
landscapes. This paper presents an attempt to improve the search performance of
HS by hybridizing it with Differential Evolution (DE) algorithm. The
performance of the resulting hybrid algorithm has been compared with classical
HS, the global best HS, and a very popular variant of DE over a test-suite of
six well known benchmark functions and one interesting practical optimization
problem. The comparison is based on the following performance indices - (i)
accuracy of final result, (ii) computational speed, and (iii) frequency of
hitting the optima.
Keywords: Global optimization, Meta-heuristics, Harmony Search, Differential Evolution, Explorative power, Population variance
