Preliminary Results for Subpopulation Algorithm Based on Novelty (SAN) Compared with the State of the Art

Subpopulation algorithm based on novelty (SAN) has been investigated for some time and proved that it can be used for multi-objective optimization problems. It outperforms subpopulation algorithm based on general differential evolution (SAGDE) under the same framework, which highlights its special intrinsic mechanism. This intrinsic mechanism has something in common with some state-of-the-art multi-objective optimization algorithms. However, SAN has not yet proved its ability to be better than these algorithms and has not proven its ability to optimize problems with more than 5 objectives. In this paper, the advantage of SAN over other subpopulation algorithms, i.e., novelty search, is presented in detail. The similarities and differences between the intrinsic mechanisms of SAN, nondominated sorting genetic algorithm series (NSGAs) and multi-objective evolutionary algorithm based on decomposition (MOEA/D) are also analyzed. Finally, these three algorithms are evaluated on several well-known benchmark problems with more than two objectives. The result shows SAN surpassed NSGA-III (latest version in NSGAs) in 20 out of the 32 problems, surpassed MOEA/D in 26 problems in 10 runs, which preliminary proved it surpasses the State-of-the-Art.