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Publication Detailed Description
Innovations in Mechatronics Engineering II. Lecture Notes in Mechanical Engineering
Year (definitive publication)
2022
Language
English
Country
Switzerland
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Abstract
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.
Acknowledgements
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
Keywords
Sectorization problems,Co-evolutionary algorithms,Many-objective optimisation
Fields of Science and Technology Classification
- Physical Sciences - Natural Sciences
- Chemical Sciences - Natural Sciences
- Mechanical Engineering - Engineering and Technology
- Chemical Engineering - Engineering and Technology
Funding Records
| Funding Reference | Funding Entity |
|---|---|
| POCI-01-0145-FEDER-031671 | Comissão Europeia |
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