Entropy diversity in multi-objective particle swarm optimization

Pires, E. J. Solteiro; Machado, J. A. Tenreiro; Oliveira, P. B. Moura

http://hdl.handle.net/10400.22/3186

Utilize este identificador para referenciar este registo.

Nome:	Descrição:	Tamanho:	Formato:
ART_TenreiroMachado_2013.pdf		432.36 KB	Adobe PDF	Ver/Abrir

Contacte-nos

Autores

Pires, E. J. Solteiro

Machado, J. A. Tenreiro

Oliveira, P. B. Moura

Resumo(s)

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 analyzethe 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.

Palavras-chave

Multi-objective particle swarm optimization Shannon entropy Diversity

URI

http://hdl.handle.net/10400.22/3186

Editora

MDPI AG

DOI

10.3390/e15125475

Coleções

ISEP – GECAD – Artigos

Métricas Alternativas

Ver registo completo