Multi-objective optimization using metaheuristics: non-standard algorithms - Archive ouverte HAL Access content directly
Journal Articles International Transactions in Operational Research Year : 2012

Multi-objective optimization using metaheuristics: non-standard algorithms

(1) , (2) , (3) , (4)


In recent years, the application of metaheuristic techniques to solve multi‐objective optimization problems has become an active research area. Solving this kind of problems involves obtaining a set of Pareto‐optimal solutions in such a way that the corresponding Pareto front fulfils the requirements of convergence to the true Pareto front and uniform diversity. Most of the studies on metaheuristics for multi‐objective optimization are focused on Evolutionary Algorithms, and some of the state‐of‐the‐art techniques belong this class of algorithms. Our goal in this paper is to study open research lines related to metaheuristics but focusing on less explored areas to provide new perspectives to those researchers interested in multi‐objective optimization. In particular, we focus on non‐evolutionary metaheuristics, hybrid multi‐objective metaheuristics, parallel multi‐objective optimization and multi‐objective optimization under uncertainty. We analyze these issues and discuss open research lines.

Dates and versions

hal-00750705 , version 1 (12-11-2012)



El-Ghazali Talbi, Matthieu Basseur, Antonio Jesús Nebro, Enrique Alba. Multi-objective optimization using metaheuristics: non-standard algorithms. International Transactions in Operational Research, 2012, 19 (1), pp.283-306. ⟨10.1111/j.1475-3995.2011.00808.x⟩. ⟨hal-00750705⟩
162 View
0 Download



Gmail Facebook Twitter LinkedIn More