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

Published: 4 July 2022| Version 1 | DOI: 10.17632/37dpby2py9.1
Ahlem Aboud,
Raja Fdhila,
Adel Alimi


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.


Steps to reproduce

-This file details the supplementary data of the proposed Dynamic-MOPSO algorithm, all quantitative results are reported in the supplementary file. -This (.zip) file presents a set folders of 30 experimental runs, the title of each folder is as follow: FDA1, dMOP3, and DIMP2 including the obtained Pareto Optimal Front and Pareto Optimal Set and values of GD, HV and Spread quality indicators


Universite de Sousse Institut Superieur d'Informatique et des Technologies de Communication de Hammam Sousse


Artificial Intelligence, Particle Swarm Optimization, Multi-Objective Optimization, Adaptive Dynamics