Resumen
In this chapter, we present hybridization techniques that allow us to combine evolutionary algorithms with mathematical-programming techniques for solving continuous multiobjective optimization problems. The main motivation for this hybridization is to improve the performance by coupling a global search engine (a multiobjective evolutionary algorithm [MOEA]) with a local search engine (a mathematical-programming technique). The chapter includes a short introduction to multiobjective optimization concepts, as well as some general background about mathematical-programming techniques used for multiobjective optimization and state-of-the-art MOEAs. Also, a general discussion of memetic algorithms (which combine global search engines with local search engines) is provided. Then, the chapter discusses a variety of hybrid approaches in detail, including combinations of MOEAs with both gradient and non-gradient methods.
Idioma original | Inglés |
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Título de la publicación alojada | Decision Sciences |
Subtítulo de la publicación alojada | Theory and Practice |
Editorial | CRC Press |
Páginas | 185-231 |
Número de páginas | 47 |
ISBN (versión digital) | 9781482282566 |
ISBN (versión impresa) | 9781466564305 |
DOI | |
Estado | Publicada - 30 nov. 2016 |