Success Rate-based Adaptive Differential Evolution L-SRTDE for CEC 2024 Competition

One of the most important problems of the Differ-ential Evolution algorithm is the adaptation of scaling factor, due to high sensitivity to this parameter. In this paper the L-SRTDE algorithm is proposed, where the scaling factor is set based on the ratio of improved solutions at each generation, i.e. success rate. The proposed algorithm is used to solve the benchmark problems from the CEC 2024 Bound Constrained Single Objective Numerical Optimization competition. Additional experiments are performed using the CEC 2017 and CEC 2022 benchmarks. The analysis of numerical results, considering both accuracy and speed of the algorithms shows that the proposed approach is capable of outperforming many alternative approaches, which use success history-based adaptation.