Engineering Applications of Artificial Intelligence
An Intelligent Tuned Harmony Search algorithm for optimisation
Information Sciences: an International Journal
Network partitioning using harmony search and equivalencing for distributed computing
Journal of Parallel and Distributed Computing
Harmony-based feature selection to improve the nearest neighbor classification
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
Efficient stochastic algorithms for document clustering
Information Sciences: an International Journal
International Journal of Applied Metaheuristic Computing
An improved adaptive binary Harmony Search algorithm
Information Sciences: an International Journal
A hybrid quantum inspired harmony search algorithm for 0-1 optimization problems
Journal of Computational and Applied Mathematics
Survey A survey on applications of the harmony search algorithm
Engineering Applications of Artificial Intelligence
Malware detection by pruning of parallel ensembles using harmony search
Pattern Recognition Letters
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The theoretical analysis of evolutionary algorithms is believed to be very important for understanding their internal search mechanism and thus to develop more efficient algorithms. This paper presents a simple mathematical analysis of the explorative search behavior of a recently developed metaheuristic algorithm called harmony search (HS). HS is a derivative-free real parameter optimization algorithm, and it draws inspiration from the musical improvisation process of searching for a perfect state of harmony. This paper analyzes the evolution of the population-variance over successive generations in HS and thereby draws some important conclusions regarding the explorative power of HS. A simple but very useful modification to the classical HS has been proposed in light of the mathematical analysis undertaken here. A comparison with the most recently published variants of HS and four other state-of-the-art optimization algorithms over 15 unconstrained and five constrained benchmark functions reflects the efficiency of the modified HS in terms of final accuracy, convergence speed, and robustness.