Entropy diversity in multi-objective particle swarm optimization
type: Item
instance of: scholarly article
title: Entropy diversity in multi-objective particle swarm optimization
zbMATH Open document ID: 1345.90112
author: Eduardo J. Solteiro Pires
author: Paulo B. de Moura Oliveira
author: José A. Tenreiro Machado
published in: Entropy
publication date: 2016-05-10T00:00:00Z
review text: Summary: Multi-objective particle swarm optimization (MOPSO) is a search algorithm based on social behavior. Most of the existing multi-objective particle swarm optimization schemes are based on Pareto optimality and aim to obtain a representative non-dominated Pareto front for a given problem. Several approaches have been proposed to study the convergence and performance of the algorithm, particularly by accessing the final results. In the present paper, a different approach is proposed, by using Shannon entropy to analyze the MOPSO dynamics along the algorithm execution. The results indicate that Shannon entropy can be used as an indicator of diversity and convergence for MOPSO problems.
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 90C29
zbMATH DE Number: 6578433
zbMATH Keywords: multi-objective particle swarm optimization
zbMATH Keywords: Shannon entropy
zbMATH Keywords: diversity
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/e15125475
OpenAlex ID: W1995559168
cites work: Q4692508
cites work: Multi-objective optimization using evolutionary algorithms
cites work: Dynamical modelling of a genetic algorithm
cites work: Particle swarm optimization with fractional-order velocity
DOI: 10.3390/E15125475
description: scientific article; zbMATH DE number 6578433