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

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

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

Conference proceedings International

Title: Lecture Notes in Mechanical Engineering

Pages: 257-268

1st International Conference on Innovation in Engineering, ICIE 2021

28 June 2021 through 30 June 2021

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific