An Attempt to Enhance NSGA-II With a Clustering Approach
Nadir Mahammed, Souad Bennabi, Abdelghani Bekka, Badia Klouche, Mahmoud Fahsi, Zouaoui Guellil · 2021 International Conference on Decision Aid Sciences and Application (DASA) · 2021
This article presents an attempt to enhance the genetic algorithm NSGA-II by hybridation. It uses the K-means clustering algorithm and silhouette coefficient. It operates in two stages. First, the right number of clusters is generated automatically using K-means clustering and verified by silhouette coefficient. Then, it executes NSGA-II for a defined number of iterations within the proposed algorithm. Results of the algorithm for some benchmark test functions are used to demonstrate the efficiency of the proposition.