An Application of Preference-Inspired Co-Evolutionary Algorithm to Sectorization

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2022

Autores

Öztürk, E (Autor) Outra; Rocha, P (Autor) Outra; Sousa, F (Autor) Outra; Lima, M (Autor) Outra; Rodrigues, AM (Autor) Outra; José Soeiro Ferreira (Autor) FEUP; Nunes, AC (Autor) Outra; Lopes, C (Autor) Outra; Oliveira, C (Autor) Outra

Ata de Conferência Internacional

Título: Lecture Notes in Mechanical Engineering

Páginas: 257-268

1st International Conference on Innovation in Engineering, ICIE 2021

28 June 2021 through 30 June 2021

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00W-RBQ

DOI: 10.1007/978-3-031-09385-2_23

Abstract (EN): Sectorization problems have significant challenges arising from the many objectives that must be optimised simultaneously. Several methods exist to deal with these many-objective optimisation problems, but each has its limitations. This paper analyses an application of Preference Inspired Co-Evolutionary Algorithms, with goal vectors (PICEA-g) to sectorization problems. The method is tested on instances of different size difficulty levels and various configurations for mutation rate and population number. The main purpose is to find the best configuration for PICEA-g to solve sectorization problems. Performance metrics are used to evaluate these configurations regarding the solutions¿ spread, convergence, and diversity in the solution space. Several test trials showed that big and medium-sized instances perform better with low mutation rates and large population sizes. The opposite is valid for the small size instances. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Idioma: Inglês

Tipo (Avaliação Docente): Científica