Supported Algorithms

Here are the list of optimization algorithms that AutoOED currently supports, including the most sample-efficient multi-objective Bayesian optimization algorithms and some classical multi-objective 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

Multi-Objective Optimization

AutoOED supports several classical multi-objective optimization algorithms including NSGA-II and MOEA/D, which are not as sample-efficient as multi-objective Bayesian optimization algorithms but run relatively faster. These algorithms also serve as an important module in multi-objective Bayesian optimization pipeline.

Algorithm Publication Name Publication Venue
MOEA/D [6]

Moea/d: A multiobjective evolutionary algorithm

based on decomposition

TVEC 2007
NSGA-II [7]

A fast and elitist multiobjective genetic algorithm:

Nsga-ii

TVEC 2002

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.
[6]Qingfu Zhang and Hui Li. Moea/d: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6):712–731, 2007.
[7]Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan. A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE transactions on evolutionary computation, 6(2):182–197, 2002.