SUPPLEMENTARY DATA OF THE PAPER: DPb-MOPSO: A Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization Algorithm

Published: 8 June 2022| Version 1 | DOI: 10.17632/zm4kjbn5zh.1
Contributors:
,
Nizar Rokbani,
Raja Fdhila,
Abdulrahman M. Qahtani,
Omar Almutiry,
Habib Dhahri,
Amir Hussain,
Adel Alimi

Description

This study proposes a Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm including two parallel optimization levels. At the first level, all solutions are managed in a single search space. When a dynamic change is successfully detected in the objective values, the Pareto ranking operator is used to enable a multiple sub-swarm’ subdivisions and processing which drives the second level of enhanced exploitation. A dynamic handling strategy based on random detectors is used to track the changes of the objective function due to time-varying parameters. A response strategy consisting in re-evaluate all unimproved solutions and replacing them with newly generated ones is also implemented. The DPb-MOPSO system is tested on a set of DMOPs with different types of time-varying Pareto Optimal Set (POS) and Pareto Optimal Front (POF). Inverted generational distance (IGD), mean inverted generational distance (MIGD), and hypervolume difference (HVD) metrics are used to assess the DPb-MOPSO performances.

Files

Steps to reproduce

-This file details the supplementary data of the proposed DPb-MOPSO algorithm, all parameters configuration are reported in the supplementary file. -This (.zip) file presents a set folders of 27 experimental runs, the title of each folder is as follow: A_DPbMOPSO_Tested_Problems___metrics_experiment___Exp___________________________ID -Each folder contains two sub_folders denoted by: - data: contains the output of the proposed DPb-MOPSO algorithm for each tested problem, all files are as follows: (FUN.ID_runs, VAR.ID_runs, IGD, HVD, MIGDfiles) - Reference Fronts: contains all true Front Pareto Files

Institutions

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

Categories

Artificial Intelligence, Computational Intelligence, Particle Swarm Optimization, Multi-Objective Optimization, Bio-Inspired Computing, Adaptive Dynamics, Swarm Intelligence Algorithm

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