This repository contains the implementation of a new hybrid algorithm called MSM-LSHADE (Multi-Shared Mining Success-History based Adaptive Differential Evolution with Linear Population Size Reduction), which integrates Data Mining techniques and Parallel Processing.
MSM-LSHADE builds upon the DM-LSHADE algorithm [1], a hybrid variant of L-SHADE [2] with clustering-based data mining techniques. The DM-LSHADE introduced hybridization with K-Means [3] and X-Means [4] clustering algorithms, implemented using the Pyclustering Library.
In MSM-LSHADE, the hybrid algorithm is parallelized using MPI (Message Passing Interface). The new algorithm is executed cooperatively by multiple LSHADE slave processes that share mined patterns, enabling dynamic population adaptation and knowledge exchange. These patterns are mined and distributed by a master process, which coordinates the interaction and pattern integration across slaves.
[1] Santos, Raphael Gomes. DM-LSHADE GitHub Repository. (GitHub)
[2] Tanabe, Ryoji and Alex S. Fukunaga. “Improving the search performance of SHADE using linear population size reduction.” 2014 IEEE Congress on Evolutionary Computation (CEC) (2014): 1658–1665. (PDF) (Code)
[3] R. Duda and P. Hart. Pattern Classification and Scene Analysis. John Wiley & Sons, 1973.
[4] Pelleg, D. and Moore, A. W. “X-means: Extending K-means with Efficient Estimation of the Number of Clusters.” In Proceedings of the 17th International Conference on Machine Learning (ICML '00), 2000, pp. 727–734. (PDF)