Multi‐objective optimization using metaheuristics: non‐standard algorithms
El‐Ghazali Talbi, Matthieu Basseur, Antonio Jesús Nebro, Enrique Alba · International Transactions in Operational Research · 2011
Abstract 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 ofPareto‐optimal solutions in such a way that the correspondingPareto front fulfils the requirements of convergence to the truePareto front and uniform diversity. Most of the studies on metaheuristics for multi‐objective optimization are focused onEvolutionaryAlgorithms, 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.