@inproceedings{4194d12649614685be189e22832118f2,
title = "Pareto optimal set approximation by models: A linear case",
abstract = "The optimum of a multiobjective optimization problem (MOP) usually consists of a set of tradeoff solutions, called Pareto optimal set, that balances different objectives. In the community of evolutionary computation, an internal or external population with a limited size is usually used to approximate the Pareto optimal set. Since the Pareto optimal set forms a manifold in both the decision and objective spaces under mild conditions, it is possible to use a model as well as a population of solutions to approximate the Pareto optimal set. Following this idea, the paper proposes to use a set of linear models to approximate the Pareto optimal set in the decision space. The basic idea is to partition the manifold into different segments and use a linear model to approximate each segment in a local area. To implement the algorithm, the models are incorporated in the multiobjective evolutionary algorithm based on decomposition (MOEA/D) framework. The proposed algorithm is applied to a test suite, and the comparison study demonstrates that models can help to improve the performance of algorithms that only use solutions to approximate the Pareto optimal set.",
keywords = "Evolutionary multiobjective optimization, MOEA/D, Regularity model",
author = "Aimin Zhou and Haoying Zhao and Hu Zhang and Guixu Zhang",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019 ; Conference date: 10-03-2019 Through 13-03-2019",
year = "2019",
doi = "10.1007/978-3-030-12598-1\_36",
language = "英语",
isbn = "9783030125974",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "451--462",
editor = "\{Coello Coello\}, \{Carlos A.\} and Patrick Reed and Kalyanmoy Deb and Erik Goodman and Kathrin Klamroth and Sanaz Mostaghim and Kaisa Miettinen",
booktitle = "Evolutionary Multi-Criterion Optimization - 10th International Conference, EMO 2019, Proceedings",
address = "德国",
}