Abstract
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.
Original language | English |
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Title of host publication | Decision Sciences |
Subtitle of host publication | Theory and Practice |
Publisher | CRC Press |
Pages | 185-231 |
Number of pages | 47 |
ISBN (Electronic) | 9781482282566 |
ISBN (Print) | 9781466564305 |
DOIs | |
State | Published - 30 Nov 2016 |