Integrating Competitive Framework into Differential Evolution: Comprehensive performance analysis and application in brain tumor detection

Abstract

This paper presents an efficient and effective optimizer based on the Success History Adaptive DE (SHADE) named Competitive Framework DE (CFDE). We integrate three tailored strategies into CFDE: (1) the competitive framework to identify and prioritize potential individuals, (2) the novel DE/loser-to-best/loser-to-winner mutation scheme to fully leverage the information from the population and competition to construct high-quality offspring individuals, and (3) the random memory initialization to diversify the search patterns of the individual. We conduct comprehensive numerical experiments on CEC2017, CEC2020, CEC2022, and eight engineering problems against eleven state-of-the-art optimizers to confirm the superiority and competitiveness of CFDE. Moreover, the sensitivity experiments on hyperparameters validate the robustness of CFDE, and the ablation experiments practically prove the independent contribution of integrated components. Furthermore, we propose a hybrid model named DenseNet-CFDE-ELM for brain tumor detection, where DenseNet-169 is employed for feature selection and CFDE-optimized Extreme Learning Machine (ELM) classifies the brain tumors in MRI scans. Experimental results on the brain tumor dataset downloaded from Kaggle confirm that the proposed DenseNet-CFDE-ELM achieves improvements in accuracy with 1.794%, precision with 1.696%, recall with 1.794%, and F1 score with 1.812% against the second-best ResNet-18 model. These results reveal the potential of CFDE in extensive real-world optimization scenarios. The source code of this research can be downloaded from https://github.com/RuiZhong961230/CFDE.