An enhanced Differential Evolution with Restart Mechanism for Numerical Optimization: RMDE
Keywords:
different evolution algorithm with a restart mechanism, metaheuristics, warehouse managementAbstract
This paper proposes an enhanced Differential Evolution algorithm with a Restart Mechanism (RMDE) for numerical optimization. The RMDE algorithm improves the search performance of an enhanced Differential Evolution algorithm with novel control parameter adaptation schemes (PaDE) by incorporating a restart mechanism. This mechanism, adapted from the Adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism (AMECoDEs), includes strategies to enhance population diversity and accelerate convergence. Additionally, RMDE refines the parameter adaptation techniques for the average crossover value and the average scaling factor. Experimental results on the CEC2017 test suite compared RMDE with several state-of-the-art differential evolution algorithms, including jSO, MPADE, JADE, SHADE, and PaDE, on problems with 10 and 30 dimensions. For the 10-variable problems, jSO, SHADE, and MPADE demonstrated superior search performance compared to RMDE, while JADE and PaDE showed comparable performance. However, as the problem size increased to 30 variables, RMDE significantly outperformed JADE, MPADE, and PaDE, with jSO and SHADE performing similarly. Furthermore, when considering the hybrid function type, the RMDE algorithm demonstrated superior search performance compared to all other algorithms. In a practical application to warehouse management problems, RMDE proved to be more effective than PaDE at finding solutions for medium and large-scale problems.
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