An investigation of clustering strategies in many-objective optimization: the I-Multi algorithm as a case study

Author:

Castro Olacir R.ORCID,Pozo Aurora,Lozano Jose A.,Santana Roberto

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Eusko Jaurlaritza

Secretaría de Estado de Investigación, Desarrollo e Innovación

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference61 articles.

1. Adra, S. F. (2007). Improving convergence, diversity and pertinency in multiobjective optimisation. Ph.D. Thesis, Department of Automatic Control and Systems Engineering, The University of Sheffield, UK.

2. Aggarwal, C. C., Hinneburg, A., & Keim, D. A. (2001). On the surprising behavior of distance metrics in high dimensional space. In International Conference on Database Theory (pp. 420–434). Springer.

3. Bader, J., Deb, K., & Zitzler, E. (2010). Faster hypervolume-based search using Monte Carlo sampling. In Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems. Lecture Notes in Economics and Mathematical Systems (Vol. 634, pp. 313–326). Berlin: Springer.

4. Benameur, L., Alami, J., & Imrani, A. E. (2009). A new hybrid particle swarm optimization algorithm for handling multiobjective problem using fuzzy clustering technique. In Proceedings of the 2009 International Conference on Computational Intelligence, Modelling and Simulation, CSSIM ’09 (pp. 48–53). Washington, DC: IEEE Computer Society.

5. Berkhin, P. (2006). A survey of clustering data mining techniques. In J. Kogan, C. Nicholas, & M. Teboulle (Eds.), Grouping multidimensional data: Recent advances in clustering (pp. 25–71). Berlin: Springer.

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