DDMOA: Descent Directions based Multiobjective Algorithm
Hybridization of local search based algorithms with evolutionary algorithms is still an under-explored research area in multiobjective optimization. In this paper, we propose a new multiobjective algorithm based on a local search method. The main idea is to generate new non-dominated solutions by ad...
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| Outros Autores: | , |
| Formato: | conferencePaper |
| Idioma: | eng |
| Publicado em: |
2012
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/1822/37046 |
| País: | Portugal |
| Oai: | oai:repositorium.sdum.uminho.pt:1822/37046 |
| Resumo: | Hybridization of local search based algorithms with evolutionary algorithms is still an under-explored research area in multiobjective optimization. In this paper, we propose a new multiobjective algorithm based on a local search method. The main idea is to generate new non-dominated solutions by adding to a parent solution a linear combination of descent directions of the objective functions. Additionally, a strategy based on subpopulations is implemented to avoid the direct computation of descent directions for the entire population. The evaluation of the proposed algorithm is performed on a set of benchmark test problems allowing a comparison with the most representative stateof- the-art multiobjective algorithms. The results show that the proposed approach is highly competitive in terms of the quality of non-dominated solutions, robustness and the computational efficiency. |
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