Journal of Bionic Engineering ›› 2022, Vol. 19 ›› Issue (1): 240-256.doi: 10.1007/s42235-021-00114-8
• • 上一篇
Adaptive Barebones Salp Swarm Algorithm with Quasi‑oppositional Learning for Medical Diagnosis Systems: A Comprehensive Analysis
Jianfu Xia1,2, Hongliang Zhang3, Rizeng Li1, Zhiyan Wang4, Zhennao Cai3, Zhiyang Gu5, Huiling Chen3, Zhifang Pan6
- 1 Department of General Surgery, The Second Afliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou 325000, Zhejiang, People’s Republic of China 2 Soochow University, Suzhou, Jiangsu, People’s Republic of China 3 Department of Computer Science and Artifcial Intelligence, Wenzhou University, Wenzhou 325035, People’s Republic of China 4 School of Artifcial Intelligence, Jilin International Studies University, Changchun 130000, People’s Republic of China 5 Wenzhou Polytechnic, Wenzhou 325035, People’s Republic of China 6 The First Afliated Hospital of Wenzhou Medical University, Wenzhou 325000, People’s Republic of China
Adaptive Barebones Salp Swarm Algorithm with Quasi‑oppositional Learning for Medical Diagnosis Systems: A Comprehensive Analysis
Jianfu Xia1,2, Hongliang Zhang3, Rizeng Li1, Zhiyan Wang4, Zhennao Cai3, Zhiyang Gu5, Huiling Chen3, Zhifang Pan6
- 1 Department of General Surgery, The Second Afliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou 325000, Zhejiang, People’s Republic of China 2 Soochow University, Suzhou, Jiangsu, People’s Republic of China 3 Department of Computer Science and Artifcial Intelligence, Wenzhou University, Wenzhou 325035, People’s Republic of China 4 School of Artifcial Intelligence, Jilin International Studies University, Changchun 130000, People’s Republic of China 5 Wenzhou Polytechnic, Wenzhou 325035, People’s Republic of China 6 The First Afliated Hospital of Wenzhou Medical University, Wenzhou 325000, People’s Republic of China
摘要： The Salp Swarm Algorithm (SSA) may have trouble in dropping into stagnation as a kind of swarm intelligence method. This paper developed an adaptive barebones salp swarm algorithm with quasi-oppositional-based learning to compensate for the above weakness called QBSSA. In the proposed QBSSA, an adaptive barebones strategy can help to reach both accurate convergence speed and high solution quality; quasi-oppositional-based learning can make the population away from traping into local optimal and expand the search space. To estimate the performance of the presented method, a series of tests are performed. Firstly, CEC 2017 benchmark test suit is used to test the ability to solve the high dimensional and multimodal problems; then, based on QBSSA, an improved Kernel Extreme Learning Machine (KELM) model, named QBSSA–KELM, is built to handle medical disease diagnosis problems. All the test results and discussions state clearly that the QBSSA is superior to and very competitive to all the compared algorithms on both convergence speed and solutions accuracy.