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. Performancemetrics 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.

This work is financed by the ERDF - European Regional Development Fund through the Operational Program for Competitiveness and Internationalization - COMPETE 2020 Program and by National Funds through the Portuguese funding agency, FCT - Fundação para a C

Sectorization problems,Co-evolutionary algorithms,Many-objective optimisation

• Ciências Físicas - Ciências Naturais
• Ciências Químicas - Ciências Naturais
• Engenharia Mecânica - Engenharia e Tecnologia
• Engenharia Química - Engenharia e Tecnologia