MOEA/D with Random Partial Update Strategy
| Autor: | Yuri Cossich Lavinas, Marcelo Ladeira, Felipe Campelo, Claus Aranha |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
education.field_of_study Mathematical optimization Computer Science - Artificial Intelligence Population Computer Science - Neural and Evolutionary Computing Context (language use) 02 engineering and technology 01 natural sciences Electronic mail 010104 statistics & probability Artificial Intelligence (cs.AI) 0202 electrical engineering electronic engineering information engineering Resource allocation 020201 artificial intelligence & image processing Resource management Fraction (mathematics) Neural and Evolutionary Computing (cs.NE) 0101 mathematics education |
| Zdroj: | CEC |
| Popis: | Recent studies on resource allocation suggest that some subproblems are more important than others in the context of the MOEA/D, and that focusing on the most relevant ones can consistently improve the performance of that algorithm. These studies share the common characteristic of updating only a fraction of the population at any given iteration of the algorithm. In this work, we investigate a new, more straightforward partial update strategy, in which a random subset of solutions is selected at every iteration. The performance of the MOEA/D-DE using this new resource allocation approach is compared experimentally against that of the standard MOEA/D-DE and the MOEA/D-DE with relative improvement-based resource allocation. The results indicate that using MOEA/D with this new partial update strategy results in improved HV and IGD values, and a much higher proportion of non-dominated solutions, particularly as the number of updated solutions at every iteration is reduced. |
| Databáze: | OpenAIRE |
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