Controllability of Fractional-Order Particle Swarm Optimizer and Its Application in the Classification of Heart Disease
Source abstract
Abstract
This study proposes a method to improve fractional-order particle swarm optimizer to overcome the shortcomings of traditional swarm algorithms, such as low search accuracy in a high-dimensional space, falling into local minimums, and nonrobust results. In natural phenomena, our controllable fractional-order particle swarm optimizer can explore search spaces in detail to obtain high resolutions. Moreover, the proposed algorithm is memorable, i.e., position updates focus on the particle position of previous and last generations, rendering it conservative when updating the position, and obtained results are robust. For verifying the algorithm’s effectiveness, 11 test functions compare the average value, overall best value, and standard deviation of the controllable fractional-order particle swarm optimizer and controllable particle swarm optimizer; experimental results show that the stability of the former is better than the latter. Furthermore, the solution position found by the controllable fractional-order particle swarm optimizer is more reliable. Therefore, the improved method proposed herein is effective. Moreover, this research describes how a heart disease prediction application uses the optimizer we proposed to optimize XGBoost hyperparameters with custom target values. The final verification of the obtained prediction model is effective and reliable, which shows the controllability of our proposed fractional-order particle swarm optimizer.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Metaheuristic Optimization Algorithms Research · Advanced Control Systems Design · Fractional Differential Equations Solutions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807, Taiwan
Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, Penghu 880, Taiwan
Department of Intelligent Robotics, National Pingtung University, Pingtung 900, Taiwan
Department of Neurology, Kaohsiung Medical University Hospital, Kaohsiung 807, Taiwan
Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 807, Taiwan
Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung 807, Taiwan
Department of Mechanical Engineering, National Pingtung University of Science and Technology, Pingtung 912, Taiwan
Department of Mechanical and Computer-Aided Engineering, Feng-Chia University, Taichung 407, Taiwan
Funding
Ministry of Science and Technology, Taiwan
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