.png){kind=icon}
.png){kind=icon}
Return
Adaptive operator selection with test-and-apply structure for decomposition-based multi-objective optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.swevo.2021.101013.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we propose a test-and-apply based adaptive operator selection strategy (TAOS) towards significant improvement of decomposition-based multi-objective optimization. In this approach, the entire evolutionary process is structured into several continuous sections, each of which is designed to run a test-and-apply procedure, in order to pave the way for adaptive selection of the best possible operators. In the phase of test, the adopted operators are tested by running sequentially on individuals and then the operators credits are assigned by their successful update of counts to replace the parent solutions. In the phase of apply, the best operator is selected to run on the remaining time of this section. In comparison with the existing state of the arts, our introduced two phases with the test-and-apply strategy not only achieve a better fairness, but also a more appropriate balance between exploration and exploitation for the decomposition-based multi-objective evolutionary algorithms, where the decomposition approach is convenient for credit assignment in the test phase. To evaluate our proposed strategy, we have carried out extensive experiments and the results support that our proposed outperforms the existing state of the arts on three sets of multi-objective optimization problems.
Keywords:
Adaptive operator selection
Multi-objective optimization
Evolutionary algorithm
Credit assignment
Journal
IF:
8.5
Papers:
2.1K
Citations:
1.0W