<p>This paper presents an efficient and effective variant of the competitive swarm optimizer (CSO) named multi-search pattern CSO (MSPCSO). Two tailored strategies are integrated into MSPCSO to enhance the robustness of CSO: the multi-search pattern mechanism aims to enrich the search diversity and further maintain the population diversity during optimization, and the success history-based parameter tuning strategy allows MSPCSO to adapt complex fitness landscapes in various optimization challenges. We conduct optimization experiments in CEC benchmarks and engineering challenges against nine cutting-edge optimizers to investigate the performance of MSPCSO. Rigorous statistical tests confirm the superiority and competitiveness of MSPCSO, while the sensitivity and ablation experiments are implemented to comprehensively analyze the performance of MSPCSO. Furthermore, we extend MSPCSO to ensemble learning methodology for the coffee leaf disease diagnosis, where several pre-trained deep learning models are trained in the coffee leaf disease dataset. The top three deep learning models are employed for ensemble learning according to the MSPCSO-optimized soft voting scheme. Experiments in public datasets confirm that the proposed MSPCSO-Ensemble achieves significant improvements in the accuracy of 1.021%, precision of 1.0149%, recall of 1.021%, and F1 score of 1.0217% against the second-best model, which demonstrates the competitiveness of MSPCSO-Ensemble in real-world scenarios.</p>