Many-Objective Optimization by Space Partitioning and Adaptive ε-Ranking on MNK-Landscapes

Authors:
Hernán Aguirre;Kiyoshi Tanaka
Affiliations:
Fiber-Nanotech Young Researcher Empowerment Program, and Faculty of Engineering, Shinshu University, Nagano, Japan 380-8553;Faculty of Engineering, Shinshu University, Nagano, Japan 380-8553
Venue:
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Year:
2009

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Abstract

This work proposes a method to search effectively on many -objective problems by instantaneously partitioning the objective space into subspaces and performing one generation of the evolutionary search in each subspace. The proposed method uses a partition strategy to define a schedule of subspace sampling, so that different regions of objective space could be emphasized at different generations. In addition, it uses an adaptive ε -ranking procedure to re-rank solutions in each subspace, giving selective advantage to some of the solutions initially ranked highest in the whole objective space. Adaptation works to keep the actual number of highest ranked solutions in each subspace close to a desired number. The performance of the proposed method is verified on MNK-Landscapes. Experimental results show that convergence and diversity of the solutions found can improve remarkably on 4 ≤ M ≤ 10 objectives.