A Fast Incremental Archive for Multi-objective Optimization.
Tobias Glasmachers · arXiv (Cornell University) · 2016
Maintaining an archive of all non-dominated points is a standard task in multi-objective optimization. A simple solution is to store all evaluated points and to obtain the non-dominated subset in a post-processing step. Alternatively the non-dominated set can be updated on the fly. Surprisingly, there exists little work on the latter setting. While keeping track of many non-dominated points efficiently is easy for two objectives, we propose an efficient algorithm based on a binary space partitioning (BSP) tree for the general case of three or more objectives. Our analysis and our empirical results demonstrate the superiority of the method over the brute-force baseline method.