The search efficiency of differentialevolution （de） algorithm is greatly impacted by its control parameters. Although many adaptation/self-adaptation techniques can automatically find suitable control parameters fo...
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The search efficiency of differentialevolution （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 pop- ulation 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 TPC-JAde, 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-the-art 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.
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