Approaches for many-objective optimization: Analysis and comparison on MNK-landscapes

Abstract:

This work analyses the behavior and compares the performance of MOEA/D, IBEA using the binary additive ε and the hypervolume difference indicators, and AεSεH as representative algorithms of decomposition, indicators, and ε-dominance based approaches for many-objective optimization. We use small MNK-landscapes to trace the dynamics of the algorithms generating high-resolution approximations of the Pareto optimal set. Also, we use large MNK-landscapes to analyze their scalability to larger search spaces.

Año de publicación:

2016

Keywords:

Fuente:

scopus