Cluster-Based Population Initialization for differential evolution frameworks

Abstract This article proposes a procedure to perform an intelligent initialization for population-based algorithms. The proposed pre-processing procedure, namely Cluster-Based Population Initialization (CBPI) consists of three consecutive stages. At the first stage, the individuals belonging to a randomly sampled population undergo two subsequent local search algorithms, i.e. a simple local search that performs moves along the axes and Rosenbrock algorithm. At the second stage, the solutions processed by the two local searches undergo the K-means clustering algorithm and are grouped into sets on the basis of their euclidean distance. At the third stage the best individuals belonging to each cluster are saved into the initial population of a generic optimization algorithm. If the population has not been yet filled, the other individuals of the population are sampled within the clusters by using a fitness-based probabilistic criterion. This three stage procedure implicitly performs an initial screening of the problem features in order to roughly estimate the most interesting regions of the decision space. The proposed \{CBPI\} has been tested on multiple classical and modern Differential Evolution variants, on a wide array of test problems and dimensionality values as well as on a real-world problem. The proposed intelligent sampling appears to have a significant impact on the algorithmic functioning as it consistently enhances the performance of the algorithms with which it is integrated