On Modifications Towards Improvement of the Exploitation Phase for SOMA Algorithm with Clustering-aided Migration and Adaptive Perturbation Vector Control

Abstract

This paper represents the next step in the development of the recently proposed single objective metaheuristic algorithm - Self-Organizing Migrating Algorithm with CLustering-aided migration and adaptive Perturbation vector control (SOMA-CLP). The CEC 2021 single objective bound-constrained optimization benchmark testbed was used for the performance evaluation of the modifications of the algorithm. The presented modifications were invoked by the results of CEC 2021 competition, where the SOMA-CLP ranked 7th out of 9 competing algorithms. This paper introduces three modifications of population organization process focusing on one particular phase of the SOMA-CLP algorithm aimed at exploitation. All results were compared and tested for statistical significance against the original variant using the Friedman rank test. The algorithm modification and analysis of the results presented here can be inspiring for other researchers working on the development and modifications of evolutionary computing techniques.