Since most parameter control methods are based on prior knowledge, it is difficult for them to solve various problems.In this paper, an adaptive selection method used for operators and parameters is proposed and named...Since most parameter control methods are based on prior knowledge, it is difficult for them to solve various problems.In this paper, an adaptive selection method used for operators and parameters is proposed and named double adaptive selection(DAS) strategy. Firstly, some experiments about the operator search ability are given and the performance of operators with different donate vectors is analyzed. Then, DAS is presented by inducing the upper confidence bound strategy, which chooses suitable combination of operators and donates sets to optimize solutions without prior knowledge. Finally, the DAS is used under the framework of the multi-objective evolutionary algorithm based on decomposition, and the multi-objective evolutionary algorithm based on DAS(MOEA/D-DAS) is compared to state-of-the-art MOEAs. Simulation results validate that the MOEA/D-DAS could select the suitable combination of operators and donate sets to optimize problems and the proposed algorithm has better convergence and distribution.展开更多
基金supported by the National Natural Science Foundation of China(7177121671701209)
文摘Since most parameter control methods are based on prior knowledge, it is difficult for them to solve various problems.In this paper, an adaptive selection method used for operators and parameters is proposed and named double adaptive selection(DAS) strategy. Firstly, some experiments about the operator search ability are given and the performance of operators with different donate vectors is analyzed. Then, DAS is presented by inducing the upper confidence bound strategy, which chooses suitable combination of operators and donates sets to optimize solutions without prior knowledge. Finally, the DAS is used under the framework of the multi-objective evolutionary algorithm based on decomposition, and the multi-objective evolutionary algorithm based on DAS(MOEA/D-DAS) is compared to state-of-the-art MOEAs. Simulation results validate that the MOEA/D-DAS could select the suitable combination of operators and donate sets to optimize problems and the proposed algorithm has better convergence and distribution.
