Journal
EVOLUTIONARY COMPUTATION
Volume 28, Issue 1, Pages 1-25Publisher
MIT PRESS
DOI: 10.1162/evco_a_00243
Keywords
Many-objective optimization; evolutionary multi-objective optimization; meta-objective; convergence; diversity
Funding
- National Natural Science Foundation of China [61803192, 61876075, 61773384, 61763026, 61673404, 61573361, 61503220]
- National Basic Research Program of China (973 Program) [2014CB046306-2]
- National Key RAMP
- D Program of China [2018YFB1003802-01]
- China Scholarship Council [201606420005]
Ask authors/readers for more resources
Pareto-based multi-objective evolutionary algorithms experience grand challenges in solving many-objective optimization problems due to their inability to maintain both convergence and diversity in a high-dimensional objective space. Exiting approaches usually modify the selection criteria to overcome this issue. Different from them, we propose a novel meta-objective (MeO) approach that transforms the many-objective optimization problems in which the new optimization problems become easier to solve by the Pareto-based algorithms. MeO converts a given many-objective optimization problem into a new one, which has the same Pareto optimal solutions and the number of objectives with the original one. Each meta-objective in the new problem consists of two components which measure the convergence and diversity performances of a solution, respectively. Since MeO only converts the problem formulation, it can be readily incorporated within any multi-objective evolutionary algorithms, including those non-Pareto-based ones. Particularly, it can boost the Pareto-based algorithms' ability to solve many-objective optimization problems. Due to separately evaluating the convergence and diversity performances of a solution, the traditional density-based selection criteria, for example, crowding distance, will no longer mistake a solution with poor convergence performance for a solution with low density value. By penalizing a solution in term of its convergence performance in the meta-objective space, the Pareto dominance becomes much more effective for a many-objective optimization problem. Comparative study validates the competitive performance of the proposed meta-objective approach in solving many-objective optimization problems.
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