A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization

By Linqiang Pan, Cheng He, Ye Tian, Handing Wang, Xing-yi Zhang, Yaochu Jin. IEEE Transactions on Evolutionary Computation, 2019

302 citations recorded by the source corpus.

Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for solving expensive optimization problems where only a small number of real fitness evaluations are allowed. Most existing SAEAs are designed for solving low-dimensional single or multiobjective optimization problems, which are not well suited for many-objective optimization. This paper proposes a surrogate-assisted many-objective evolutionary algorithm that uses an artificial neural network to predict the dominance relationship between candidate solutions and reference solutions instead of approximating the objective values separately. The uncertainty information in prediction is taken into account together with the dominance relationship to select promising solutions to be evaluated using the real objective functions. Our simulation results demonstrate that the proposed algorithm outperforms the state-of-the-art evolutionary algorithms on a set of many-objective optimization test problems.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/46ddc3c7e6ad5d366f99c82238327e91ca37ed7c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Handing Wang
2. Yaochu Jin
3. Tinkle Chugh
4. Kaisa Miettinen
5. Dan Guo
6. Xing-yi Zhang
7. Ran Cheng
8. Ye Tian

In Computational Theory and Mathematics

1. Yaochu Jin
2. Handing Wang
3. Tinkle Chugh
4. Kaisa Miettinen
5. Dan Guo
6. Xing-yi Zhang
7. Ye Tian
8. Ran Cheng

In Management Science and Operations Research

1. Handing Wang
2. Yaochu Jin
3. Tinkle Chugh
4. Kaisa Miettinen
5. Ye Tian
6. Xing-yi Zhang
7. Ran Cheng

Citations in the corpus

• A Random Forest-Assisted Evolutionary Algorithm for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems (2020, background): “A large number of SAEAs for single-objective optimization [6], [25], [29], [30], multi-objective optimization [12], [22], [31], [32], and many-objective optimization [13], [33] have been proposed.”
• Data-Driven Evolutionary Optimization: An Overview and Case Studies (2019, background): “Some on-line optimization algorithms, e.g., K-RVEA [11, 13], CSEA [74], and SL-PSO [88] have been proposed to tackle these challenges.”