Space Partitioning Evolutionary Many-Objective Optimization: Performance Analysis on MNK-Landscapes
Hernan E. Aguirre, Kiyoshi Tanaka · Transactions of the Japanese Society for Artificial Intelligence · 2010
This work proposes space partitioning, a new approach to evolutionary many-objective optimization.The proposed approach instantaneously partitions the objective space into subspaces and concurrently searches in each subspace.A partition strategy is used to define a schedule of subspace sampling, so that different subspaces can be emphasized at different generations.Space partitioning is implemented with adaptive -ranking, a procedure that re-ranks solutions in each subspace giving selective advantage to a subset of well distributed solutions chosen from the set of solutions initially assigned rank-1 in the high dimensional objective space.Adaptation works to keep the actual number of rank-1 solutions in each subspace close to a desired number.The effects on performance of space partitioning are verified on MNK-Landscapes.Also, a comparison with two substitute distance assignment methods recently proposed for many-objective optimization is included.