2016

Chapitre de livre

In Recent advances in evolutionary multi-objective optimization (pp. 1-30). Cham: Springer International Publishing

Problems involving multiple conflicting objectives arise in most real world optimization problems. Evolutionary Algorithms (EAs) have gained a wide interest and success in solving problems of this nature for two main reasons: (1) EAs allow finding several members of the Pareto optimal set in a single run of the algorithm and (2) EAs are less susceptible to the shape of the Pareto front. Thus, Multi-objective EAs (MOEAs) have often been used to solve Multi-objective Problems (MOPs). This chapter aims to summarize the efforts of various researchers algorithmic processes for MOEAs in an attempt to provide a review of the use and the evolution of the field. Hence, some basic concepts and a summary of the main MOEAs are provided. We also propose a classification of the existing MOEAs in order to encourage researchers to continue shaping the field. Furthermore, we suggest a classification of the most popular performance indicators that have been used to evaluate the performance of MOEAs.

@incollection{elarbi2016multi,
  title={Multi-objective optimization: classical and evolutionary approaches},
  author={Elarbi, Maha and Bechikh, Slim and Ben Said, Lamjed and Datta, Rituparna},
  booktitle={Recent advances in evolutionary multi-objective optimization},
  pages={1--30},
  year={2016},
  publisher={Springer}
}