Supported Algorithms

Here are the list of optimization algorithms that AutoOED currently supports including the most sample-efficient multi-objective Bayesian optimization algorithms.

Multi-Objective Bayesian Optimization

AutoOED supports many popular and state-of-the-art algorithms for multi-objective Bayesian optimization, including: DGEMO, TSEMO, USeMO, MOEA/D-EGO, ParEGO.

Algorithm	Publication Name	Publication Venue
DGEMO [1]	Diversity-guided multi-objective bayesian optimization with batch evaluations	NeurIPS 2020
USeMO [2]	Uncertainty-aware search framework for multi-objective bayesian optimization	AAAI 2020
TSEMO [3]	Efficient multiobjective optimization employing gaussian processes, spectral sampling and a genetic algorithm	Journal of Global Optimization 2018
MOEA/D-EGO [4]	Expensive multiobjective optimization by moea/d with gaussian process model	TEVC 2009
ParEGO [5]	Parego: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems	TEVC 2006

References

[1]	Mina Konaković Luković, Yunsheng Tian, and Wojciech Matusik. Diversity-guided multi-objective bayesian optimization with batch evaluations. In Advances in Neural Information Processing Systems 33, NeurIPS 2020, December 6-12, 2020, virtual, 2020.

[2]	Syrine Belakaria and Aryan Deshwal. Uncertainty-aware search framework for multi-objective bayesian optimization. In AAAI Conference on Artificial Intelligence (AAAI), 2020.

[3]	Eric Bradford, Artur M Schweidtmann, and Alexei Lapkin. Efficient multiobjective optimization employing gaussian processes, spectral sampling and a genetic algorithm. Journal of global optimization, 71(2):407–438, 2018.

[4]	Qingfu Zhang, Wudong Liu, Edward Tsang, and Botond Virginas. Expensive multiobjective optimization by moea/d with gaussian process model. IEEE Transactions on Evolutionary Computation, 14(3):456–474, 2009.

[5]	Joshua Knowles. Parego: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Transactions on Evolutionary Computation, 10(1):50–66, 2006.