Explore the vast range of titles available.

There are many pre-trained deep learning-based face recognition models developed in the literature, such as FaceNet, ArcFace, VGG-Face, and DeepFace. However, performing transfer learning of these models for handling face sketch recognition is not applicable due to the challenge of limited sketch datasets (single sketch per subject). One promising solution to mitigate this issue is by using optimization algorithms, which will perform a fine-tuning and fitting of these models for the face sketch problem. Specifically, this research introduces an enhanced optimizer that will evolve these models by performing automatic weightage/fine-tuning of the generated feature vector guided by the recognition accuracy of the training data. The following are the key contributions to this work: (i) this paper introduces a novel Smart Switching Slime Mold Algorithm (S2SMA), which has been improved by embedding several search operations and control rules; (ii) the proposed S2SMA aims to fine-tune the pre-trained deep learning models in order to improve the accuracy of the face sketch recognition problem; and (iii) the proposed S2SMA makes simultaneous fine-tuning of multiple pre-trained deep learning models toward further improving the recognition accuracy of the face sketch problem. The performance of the S2SMA has been evaluated on two face sketch databases, which are XM2VTS and CUFSF, and on CEC’s 2010 large-scale benchmark. In addition, the outcomes were compared to several variations of the SMA and related optimization techniques. The numerical results demonstrated that the improved optimizer obtained a higher level of fitness value as well as better face sketch recognition accuracy. The statistical data demonstrate that S2SMA significantly outperforms other optimization techniques with a rapid convergence curve.

The Slime Mould Algorithm (SMA) has effectively solved various real-world problems such as image segmentation, solar photovoltaic cell parameter estimation, and economic emission dispatch. However, SMA and its variants still face limitations when dealing with low-dimensional optimization problems, including slow convergence and local optima traps. This study aims to develop an optimized algorithm, the Merged Slime Mould Algorithm (MSMA), to overcome these limitations and improve performance in low-dimensional optimization tasks. Additionally, MSMA introduces a novel approach by merging the Adaptive Opposition Slime Mould Algorithm (AOSMA) and the Smart Switching Slime Mould Algorithm (S2SMA), simplifying the hybridization process and enhancing optimization performance. MSMA eliminates the need for multiple initializations, avoids memory-switching requirements, and employs adaptive and smart switching rules to harness the strengths of both algorithms. The performance of MSMA is evaluated using the CEC 2005 benchmark and ten real-world applications. The Wilcoxon rank-sum test verifies the effectiveness of the proposed approach, with results compared to various SMA variations and related optimization methods. Numerical findings demonstrate superior fitness values achieved by the proposed strategy, while statistical results indicate MSMA's outperformance with a rapid convergence curve.