MOEA/D with Uniformly Randomly Adaptive Weights
| Autor: | Hansenclever F. Bassani, Pedro H. M. Braga, Lucas R. C. de Farias, Aluizio F. R. Araújo |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Mathematical optimization education.field_of_study Computer science Population size Population Process (computing) Computer Science - Neural and Evolutionary Computing 02 engineering and technology Function (mathematics) Multi-objective optimization Set (abstract data type) 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Decomposition (computer science) 020201 artificial intelligence & image processing Neural and Evolutionary Computing (cs.NE) education |
| Zdroj: | GECCO |
| Popis: | When working with decomposition-based algorithms, an appropriate set of weights might improve quality of the final solution. A set of uniformly distributed weights usually leads to well-distributed solutions on a Pareto front. However, there are two main difficulties with this approach. Firstly, it may fail depending on the problem geometry. Secondly, the population size becomes not flexible as the number of objectives increases. In this paper, we propose the MOEA/D with Uniformly Randomly Adaptive Weights (MOEA/D-URAW) which uses the Uniformly Randomly method as an approach to subproblems generation, allowing a flexible population size even when working with many objective problems. During the evolutionary process, MOEA/D-URAW adds and removes subproblems as a function of the sparsity level of the population. Moreover, instead of requiring assumptions about the Pareto front shape, our method adapts its weights to the shape of the problem during the evolutionary process. Experimental results using WFG41-48 problem classes, with different Pareto front shapes, shows that the present method presents better or equal results in 77.5% of the problems evaluated from 2 to 6 objectives when compared with state-of-the-art methods in the literature. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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