Journal
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
Volume 19, Issue 1-2, Pages 283-305Publisher
WILEY
DOI: 10.1111/j.1475-3995.2011.00808.x
Keywords
multi-objective optimization; metaheuristics; hybridization; parallelism; optimization under uncertainty
Ask authors/readers for more resources
In recent years, the application of metaheuristic techniques to solve multi-objective optimization problems (MOPs) has become an active research area. Solving these kinds of problems involves obtaining a set of Pareto-optimal solutions in such a way that the corresponding Pareto front fulfills the requirements of convergence to the true Pareto front and uniform diversity. Most studies on metaheuristics for multi-objective optimization are focused on Evolutionary Algorithms, and some of the state-of-the-art techniques belong to 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available