This project was developed by Dimitre Ortt and supervised by the teacher Bianca Dantas from FACOM - UFMS, Campo Grande - MS, Brasil. It served as part of my Bachelor Degree conclusion work, together with several analysis of ther results, presented in August 2020.
Harmony search (HS) is a meta-heuristic optimization method, which is based on the concept of music improvisation. As any other optmization heuristic algorithm it depends on fine-tuning when choosing appropriate values of the parameters to allow for better change during the process of improvisation. The proposed algorithm has been evaluated for data clustering on several benchmarks. The clustering performance of proposed algorithm has been compared with K-Means and Genetic algorithm. Experimental results reveal that proposed algorithm provides better results than the above said techniques in terms of precision, recall, G-Measure, inter-cluster and intra-cluster distance. Please, read the articles mentioned below.
This project contais the implementation of two different types of Harmony Search algorithms, which are:
- The common Harmony Search (Kim et. al.) https://www.sciencedirect.com/science/article/pii/S1877705816318999 and
- Modified Harmony Search (Kumara et. al.) https://www.sciencedirect.com/science/article/pii/S2212017312005774