Enhancing the Performance of JADE Using Two-phase Parameter Control Scheme and Its Application
The search efficiency of differential evolution (DE) algorithm is greatly impacted by its control parameters. Although many adaptation/self-adaptation techniques can automatically find suitable control parameters for the DE, most techniques are based on population information which may be misleading in solving complex optimization problems. Therefore, a self-adaptive DE (i.e., JADE) using two-phase parameter control scheme (TPC-JADE) is proposed to enhance the performance of DE in the current study. In the TPCJADE, an adaptation technique is utilized to generate the control parameters in the early population evolution, and a well-known empirical guideline is used to update the control parameters in the later evolution stages. The TPC-JADE is compared with four state-of-theart DE variants on two famous test suites (i.e., IEEE CEC2005 and IEEE CEC2015). Results indicate that the overall performance of the TPC-JADE is better than that of the other compared algorithms. In addition, the proposed algorithm is utilized to obtain optimal nutrient and inducer feeding for the Lee-Ramirez bioreactor. Experimental results show that the TPC-JADE can perform well on an actual dynamic optimization problem.
KeywordsDifferential evolution (DE) algorithm evolutionary computation dynamic optimization control parameter adaptation chemical processes
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This work was supported by National Natural Science Foundation of China (Nos. 61603244 and 41505001) and Fundamental Research Funds for the Central Universities (No. 222201717006).
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