SUPPLEMENTARY DATA OF THE PAPER: Dynamic Multi Objective Particle Swarm Optimization Based on a New Environment Change Detection Strategy

This study introduces a new dynamic multi-objective optimization based particle swarm optimization algorithm (Dynamic-MOPSO). The main idea of this study is to solve such dynamic problem based on a new environment change detection strategy using the advantage of the particle swarm optimization. In this way, our approach has been developed not just to obtain the optimal solution, but also to have a capability to detect the environment changes. Thereby, Dynamic-MOPSO ensures the balance between the exploration and the exploitation in dynamic research space. Our approach is tested through the most popularized dynamic benchmark’s functions to evaluate its performance as a good method.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV