Hybridizing MOEAs with mathematical-programming techniques

Saúl Zapotecas-Martínez, Adriana Lara, Carlos A. Coello Coello

Research output: Chapter in Book/Report/Conference proceedingChapter


© 2017 by Taylor & Francis Group, LLC. 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 languageAmerican English
Title of host publicationDecision Sciences: Theory and Practice
Number of pages161
ISBN (Electronic)9781482282566, 9781466564305
StatePublished - 30 Nov 2016


Cite this

Zapotecas-Martínez, S., Lara, A., & Coello Coello, C. A. (2016). Hybridizing MOEAs with mathematical-programming techniques. In Decision Sciences: Theory and Practice https://doi.org/10.1201/9781315183176