@inproceedings{2e8d4fab8bb54a48a86a18ce6b6283bc,
title = "On the use of dynamic reference points in HypE",
abstract = "In evolutionary multiobjective optimization, hypervolume indicator is one of the most commonly-used performance metrics. To reduce its high computational costs in many objective optimization, Monte Carlo method is used in HypE (Hypervolume Estimation algorithm for multi-objective optimization) for approximating hypervolume values. However, the diversity preservation of HypE can be poor under inappropriate settings of the reference point. In this paper, the influence of the reference point on HypE is discussed and two variants of HypE algorithm with dynamic reference points are proposed to improve the performance of HypE. Our experimental results suggest that the new algorithms outperform HypE with fixed reference points on a set of multiobjective test instances with different shapes of Pareto fronts.",
keywords = "Evolutionary computation, Hypervolume, Multiobjective optimization, Reference point",
author = "Jingda Deng and Qingfu Zhang and Hui Li",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 11th International Conference on Simulated Evolution and Learning, SEAL 2017 ; Conference date: 10-11-2017 Through 13-11-2017",
year = "2017",
doi = "10.1007/978-3-319-68759-9\_11",
language = "英语",
isbn = "9783319687582",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "122--133",
editor = "Xiaodong Li and Mengjie Zhang and Yaochu Jin and Yuhui Shi and Ke Tang and Qingfu Zhang and Martin Middendorf and Tan, \{Kay Chen\} and Ying Tan",
booktitle = "Simulated Evolution and Learning - 11th International Conference, SEAL 2017, Proceedings",
}